The fundamental observable in Global Navigation Satellite System Reflectometry (GNSS-R) is the so-called Delay-Doppler Map (DDM), which is derived from the cross-correlation of the reflected signal with either the direct signal or a locally generated replica of the transmitted signal as implemented in our algorithm. Efficient coherent integration is essential for enhancing the signal-to-noise ratio (SNR) and improving the quality of the retrieved data products. This work investigates the limitations of coherent integration in conventional GNSS-R (cGNSS-R) and proposes a novel blind compensation technique to extend integration time by mitigating phase differences caused by navigation bit changes and propagation effects. The maximum length of coherent integration depends on the application; however, achieving a higher SNR within the same integration time enhances the quality of DDMs. Simulation results obtained from high-dynamic synthetic data demonstrate the effectiveness of the proposed technique. Additionally, real data from the UK-DMC reflectometry mission is used to validate the approach.

ESA SMOS and NASA SMAP missions are measuring surface soil moisture (SM) globally from space using L-band microwave radiometers. Many studies have been conducted to validate the quality of these SM retrievals using data from different SM networks. However, neither the sampling is dense enough, nor the terrain is homogeneous enough within the pixel footprint. There is also experimental evidence that, under the same irrigation conditions, the vertical SM profiles are different because of the soil type, composition, and porosity. Therefore, the L-band brightness temperature measured by the radiometers is sensitive to the SM at different depths, which limits the accuracy of the validation. This study attempts to address this question by analyzing theoretically the contributions to the brightness temperatures of a SM layer at different depths. The study includes the effects of the frequency, polarization, incidence angle, and background SM value.

This study presents a Digital Twin (DT) that is being created to optimize the use of the available hydric resources, and mitigate the effects of the increasing water shortage in irrigated agriculture in fields in the Urgell channel region (Lleida). A DT is "a virtual representation of an object or system that spans its lifecycle, it is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." It will model the water fluxes using the knowledge of the amounts of water taken in, used, and returned to the environment, and other parameters that impact the water budget, such as atmospheric variables (temperature, water vapor deficit, relative humidity, solar radiance…), surface soil moisture, and evapotranspiration maps, etc. Satellite Earth Observation (EO) data, collocated with in-situ data from a network of 20 soil moisture probes and 2 meteo stations will be used to train the DT. Additionally, a rover-based ground penetrating radar will be used for cross-calibration.

Radars have been implemented using Software Defined Radios (SDR) for more than a decade. SDRs are convenient in terms of hardware development since the signal generation, conditioning, and post-processing can easily be implemented in hardware. However, signal synchronization remains a major issue, and it can seriously impact the final performance. In this study, the synchronization and the calibration of the transmitted signals in an SDR-based radar transmitting PRN modulated pulse is studied. Time synchronization and jitter are compensated using a "Filter Delay" block, and a peak detector in a reference channel. The amplitude and phase of the peak of the cross-correlation of a sample of the transmitted signal is used to calibrate the received echoes allowing to compensate on a pulse-by-pulse basis the amplitude and phase drifts.

Radio Frequency Interference (RFI) are disturbing signals in the radio frequency spectrum, which can arise from various external sources, intentional or not. These disturbances can degrade the performance of other signals present in the band. One of such bands of remarkable importance is the 23.8 GHz band, used to retrieve the water vapor concentration in the atmosphere. The Frequency Allocations in Remote Sensing (FARS) Technical Committee from the Geoscience and Remote Sensing Society (GRSS) tasked the development and testing of a low-power consumption pico-satellite payload to monitor the possible presence of RFI in the 24 GHz band due to 5G services. This paper presents the testing and characterization of the RFI detection payload.

This study addresses discerning causal relationships in complex systems, a key aspect of interpretable machine learning. It focuses on the unusual and intense early summer weather in South Asia during April and May 2022 that led to an increased number of forest fires. This work employs a Bayesian network (BN), constructed using the NOTEARS algorithm, to analyse the contribution of various land and atmospheric variables on the extent of burned areas. In a scenario analysis using peak values of 300-hPa meridional circulation index and 500-hPa Geopotential Height Anomalies, indicative of a strong atmospheric block, the likelihood of large burned areas (>3.06 log ha or >1150 ha) increases from 36.6% to 41.6%. This is due to a rise of conditional probabilities in the Vapor Pressure Deficit (VPD) (> 5.21 kPa) by 24.8%, and the Land Surface Temperature (LST) (>45.7°C) by 15.6%. In addition, sensitivity and spatial analyses indicate that extreme dry conditions, characterized by high LST and VPD due to the trapping effects of the omega block jet stream pattern, were the primary factors influencing the extent of burned areas during the 2022 South Asia heatwave.

Earth Observation (EO) using Signals of Opportunity (SoOp), the advent of high-performance and customizable Software Defined Radios (SDRs), and small satellites are revolutionizing today many Remote Sensing techniques. One of the most widely used SoOp are the Global Navigation Satellite System signals used for Radio Occultations (GNSS-RO) and Reflectometry (GNSS-R). In this last one, the reflected GNSS signals acquire properties of the surface where they are reflected, and when these signals are compared to the direct ones, one can infer surface roughness and dielectric constant information. Nowadays SDR-based GNSS-R instruments are becoming more cost-effective, power-efficient, and small enough to be adopted as CubeSats payloads. In GNSS-R receivers, all the information resides in the so-called Delay Doppler Map (DDM), which is the cross-correlation between the reflected signal and the direct one (interferometric GNSS-R or iGNSS-R), or a locally generated replica of the direct one (conventional GNSS-R or cGNSS-R) for different delays and Doppler frequency cuts. Producing these DDMs is computationally intensive due to the large number of Fast Fourier Transforms (FFTs) involved. To develop an efficient GNSS-R instrument, this work presents the development of such an instrument— the first of its kind in the United Arab Emirates (UAE)— and introduces its first operational version. Also, it explores the capture of raw GPS L1 signal, and its processing, having as auxiliary or reference information of the PRN codes of the satellites in view, and their central Doppler frequencies. The coherent and incoherent integration times will also be traded off to enhance the spatial resolution.

Numerous studies have demonstrated the effectiveness of CYGNSS data for flood detection and mapping. However, the vast majority of the studies only focuses on flood extent detection, ignoring the importance of flood water levels in post-disaster relief. Meanwhile, most of the existing studies on inland water levels altimetry using CYGNSS data are based on the CYGNSS raw IF data using either the time-delay method or the phase method for altimetry. Although high accuracy can be obtained, at present the CYGNSS raw IF data is not a standard data, so it cannot be applied to the study of emergency flooding events. Given the above research gaps, this study proposes an effective method to combine CYGNSS L1 standard data with DTM for rapid estimation of flood water levels. The effectiveness of the proposed methodology is validated using the case of the 2022 Pakistan mega-flood. Comparison with ICESat-2 altimetry data gives encouraging results.

The spatial spectra of three Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) datasets, produced by the Barcelona Expert Center (BEC), were assessed in this study along zonal and meridional directions. The datasets are the Level 3 (L3) SM gridded at 25 km, the Level 4 (L4) SM at 1 km and an experimental L4 SM at ~300 m. Since the L4 products are obtained by a downscaling algorithm that uses Normalized Difference Vegetation Index (NDVI), NDVI data from MODIS (1 km) and Sentinel-3 (~300 m) were also analyzed.Both L4 SM products provide useful spatial information of small-scale structures, with estimated effective spatial resolutions of ~2.5 km (for the L4 at 1 km) and ~500 m (for the L4 at ~300 m). The NDVI data used for the downscaling have a significant impact not only on the spatial patterns of the resulting SM product, but also on its spectrum.

Passive Microwave remote sensing is a very energy-efficient and time-proven technology for remote sensing. Microwave Radiometry and Global Navigation Satellite System (GNSS)-Reflectometry are two widely-used examples. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers have sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily rely on the identification and location of the interference sources. Due to the vast amount of degrees of freedom found in these RFIs, classification using classical signal processing techniques becomes problematic, especially if combined RFIs are found within the same bandwidth. In this work, continuous recordings of real RFI events from a real-time system to detect and locate RFI sources are used as training data for an automated classification system, the description and performance of which are presented and analyzed.

With CubeSats having evolved beyond their initial educational purpose and now being employed across a diverse range of applications, more and more private enterprises are deploying large constellations for a plethora of applications. This increased popularity and enhanced performance of the nanosatellite form factor has led to heightened demands for reliability and cost efficiency. Presently, commissioning subsystem providers has become challenging, with a prevailing trend towards seeking fully integrated platforms in the market. Paradoxically, CubeSats are now less accessible to universities and research institutions than they were a few years ago.To address these challenges, the PocketQube concept emerged. Making up the picosatellite class, and measuring 50x50x50 mm3, they provide a cost-effective alternative, particularly for educational purposes. Students working with PocketQubes encounter design challenges akin to those faced with larger satellites. This paper comes in continuation of the IEEE-GRSS "Open PocketQube Kit" educational initiative, developed by the NanoSat-Lab at the Universitat Politecnica de Catalunya and is focused along the integration and testing of the subsystems, as well as performing a full system validation and an environmental testing.The initiative has resulted in the development of three distinct PocketQube models:PoCat-1 equipped with a VGA camera, andPoCat-2 andPoCat-3 designed for monitoring radio-frequency interference at L-band (1-2 GHz) and K-band (24-25 GHz) respectively. The latter two have undergone a series of drone campaigns in the first half of 2024, sponsored by the Frequency Allocations in Remote Sensing (FARS) Technical Committee of the IEEE Geoscience and Remote Sensing Society (GRSS).

This study presents a methodology for disaggregating the ESA Climate Change Initiative (CCI) Soil Moisture (SM) maps from 0.25° to a 60 m grid, using a feedforward neural network. This technique is applied over an area of 66,700 km2, encompassing parts of Oklahoma and Kansas (US), throughout 2021. The disaggregation approach leverages synergies between different variables, including various Sentinel-2 bands and indices, land surface temperature from MODIS, accumulated precipitation from ERA5-Land, terrain elevation and slope from the STRM, and soil composition. The methodology employs a two-step process: (i) the model is first trained using all the variables at low resolution (0.25°), and (ii) it is then employed to estimate the SM at high resolution using the input variables at 60 m. Results indicate that the model trained at low resolution achieves a reasonably high accuracy over the testing data (RMSE3•m-3and R2>0.9). The preliminary analysis of the 60 m resolution SM maps and their comparison with two in-situ stations show a strong correlation (R>0.8), and uRMSE close to 0.04 m3•m-3, with a bias ranging from 0.022 m3•m-3to 0.06 m3•m-3.

Storage of interception water in the canopy (Sc) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (Mg) and Sc. The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. Sc-values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal Mgcycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that Scmaxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify Scand evaporation fluxes independently from EC measurements and field experiments.

Global Navigation Satellite System Reflectometry (GNSS-R) is a remote sensing technology that uses GNSS signals reflected over Earth’s surface, offering advantages such as high spatio-temporal resolution and cost-effectiveness. GPS navigation signals convey navigation information through the navigation bits, in which phase jumps occur randomly at 20 ms interval. However, navigation information is meaningless in GNSS-R and limits the coherent integration time. This study presents a technique to mitigate the impact of the navigation bits on the generation of DDMs by using the raw GNSS-R data from the Fengyun-3E (FY-3E) satellite. Sample results show that, after correcting for the navigation bits, the signal-to-noise ratio (SNR [dB]) of DDMs increased by ~0.4 dB over sea to up to 3 dB over sea ice, showing that this improvement is very significant for coherent targets. The proposed navigation bit correcting method in this study significantly improves the usability of these DDMs, and opens the door to other applications that require larger integration times.

ESA’s BIOMASS is the seventh Earth Explorer mission. It will be devoted to the global monitoring of: 1) above ground forest biomass and biomass change maps with an accuracy90%, a 50-200 m spatial resolution, and 2-12 months temporal resolution; and 3) global forest height maps with an accuracy of 20-30%, 100 m spatial resolution, and 12 months temporal resolution.BIOMASS is based on a P-band SAR (438 MHz center frequency, 6 MHz bandwidth) orbiting in a dawn-dusk, Sun-synchronous orbit at 674 km, that will systematically acquire fully- (quad-) polarized image data in an interferometric mode over all major forested areas on the globe and a tomographic phase (7 images) to retrieve forest vertical structure information. At P-band ionospheric effects are very important and need to be corrected for. This paper describes the current status of the Ionospheric module for the Biomass End-to-end Performance Simulator (BEEPS-IOM).

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA Payload) [1] is a development of the UPC NanoSat Lab, aimed at Earth Observation (EO). It is one of the winners of the Second IEEE GRSS Student Grand Challenge, and will fly on-board AlainSat-1, a 3U CubeSat developed by the National Space Science and Technology Center (NSSTC) of the United Arab Emirates.RITA hosts three experiments. An L-band microwave radiometer (MWR) will gather data of soil moisture and sea ice thickness and concentration, aided with a Radio-frequency Interference (RFI) detection algorithm. A LoRa transceiver will perform on-demand execution of the EO experiments [2]. Finally, a Near-Infrared (NIR) Hyperspectral Camera will gather data for vegetation monitoring, agriculture applications, hydrology and coastal and inland waters mapping, among others [3].This work is focused on the calibration and validation of the Hyperspectral imager, at optical, electronic and spectral levels, as well as in the verification of its performance to measure Normalized Difference Vegetation Index (NDVI).

Earth observation satellite missions are becoming increasingly important and have been also boosted by the appearance of small satellites, affordable to small companies and universities. Technology miniaturization and the reduction in power consumption have been a key point in the reduction of the dimensions of these spacecrafts, being able to integrate powerful payloads in a very limited space. In missions where the observations are made at lower frequency bands, the antenna dimensions limit the achievable angular resolution. For this reason, significant investments are being made to develop antenna deployment systems for small satellites, and the deployable antennas themselves.This work presents the design and prototyping of a deployable Fresnel Zone Plate antenna for CubeSats. The first model is a technical demonstrator, but it is also meant for a GNSS-Radio Occultations payload on a 6U CubeSat. Due to its characteristics and modularity it could be easily redesigned and modified to be used in communications, etc. at other frequency bands.

Detection of marine litter is an urgent task given the large amount of plastics that end up in all water systems. This manuscript provides the description and results obtained from a ground-based Global Navigation Satellite System - Reflectometry (GNSS-R) system built in a controlled indoor environment intended to test the capabilities of GNSS-R techniques for marine litter detection, when different wave conditions and plastic litter were used. Results show that it may be possible to detect the damping of waves produced by the accumulations of some types of plastic.

The Remote sensing and Interference detector with radiome-Try and vegetation Analysis (RITA) payload was selected in 2019 the 2nd GRSS Student Grand Challenge to fly onboard AlainSat-1, a 3U CubeSat developed at the National Space Science and Technology Center (NSSTC) in United Arab Emirates. RITA payload includes a passive Microwave Radiometer (MWR), a LoRa transceiver, and an hyper-spectral camera. The objective of the payload is to retrieve Earth Observation (EO) parameters such as: soil moisture, sea-ice thickness and concentration, among others. A Radio Frequency Interference (RFI) detection algorithm is also included to the payload to create interference maps in the received signals.

Due to the recent licensing of the 26 GHz 5G communications bands, the passive microeaves Remote Sensing community is concerned that spurious out-of-band emissions could interfere with the measurements acquired in the 23.8 GHz water vapor resonance band. For this reason, the Frequency Allocations in Remote Sensing (FARS) Technical Committee from the IEEE Geoscience and Remote Sensing Society (GRSS) has procured the development of a RFI monitoring payload to detect possible interferences in the 24 to 25 GHz band. This payload is compatible with the PocketQubes being developed in the framework of the "IEEE GRSS Open PocketQube Kit" educational initiative, and it will be used to measure and determine the occurrence of possible interferences close to the 23.8 GHz water vapor band. This work presents the design, implementation and testing of the 24 to 25 GHz RFI monitoring payload designed for a 1P PocketQube.

In the work a random forest model has been implemented as an interpretable machine learning tool in the effort to estimate the burned areas caused by fire outbreaks in India, Pakistan, and Myanmar in April and May 2022. The proposed model combines environmental and atmospheric (including upper tropospheric) factors suggested to drive patterns of burned areas, and determines the weight of each factor on the propagation of fires. Results demonstrate that the model mimics the actual burned area by considering a combination of vegetation, atmosphere, and human-related variables and improves accuracy by approximately 7% after adding jet stream features. This approach could lead to implement a semi-operational forecast system that may be tested in multiple demonstration sites.

AlainSat-1 is an educational and scientific nanosatellite project that was initiated in late 2019 by the IEEE Geoscience and Remote Sensing Society (GRSS) along with National Space Science and Technology Center (NSSTC) of UAE University in the frame of the 2nd Student Grand Challenge [1]. The project involves close collaboration between four international universities to design, build, test and launch a remote sensing CubeSat.The spacecraft is a 3U CubeSat that has a mass of around 4 Kgs. The spacecraft has an active 3-axis control system capable of attitude determination and control to less than one degree. Two communications systems will be used on-board: a UHF System and an S-Band System. The project has passed the Critical Design Review (CDR) stage and is currently in the assembly and integration phase. The satellite is currently planned for launch to a sun-synchronous orbit on-board a Falcon 9 rocket in the second quarter of 2024.

Lithosphere-Atmosphere-Ionosphere coupling models have been widely studied and applied to earthquakes and their potential precursors. Recent evidence has been found that electromagnetic phenomena related to earthquakes might produce ionospheric anomalies even before their occurrence. This study analyzes their correlation focusing on the ionospheric scintillation associated to seismic activities in 2022 in the area around the Coral Sea. Among all the ionospheric indicators, this study analyzes the S4scintillation index from COSMIC-2 GNSS Radio Occultation (GNSS-RO), to estimate the ionospheric perturbation and identify anomalies. Two cases of earthquakes in 2022 with magnitudes larger than 6 are presented showing the positive correlation between the ionospheric scintillation and earthquake precursors.

Hyperspectral sensors are increasing in popularity for Earth Observation applications due to their ability to gather data over multiple spectral bands. However, the processing of such amount of information is difficult to handle for the current computing capabilities of small satellites. Several Band Selection methodologies have been developed in the last years; although, some of them demand very low computational resources, they use, at least, Level 1 data products. Therefore, the Level 0 data needs to be processed and the spectral bands coregistered. Artificial Intelligence has shown its potential to reduce the computational burden while achieving high accuracies in EO applications. In this study, a Neural Network-based methodology is proposed to select a spectral band set directly using non coregistered data captured by hyperspectral sensors.

The ionospheric scintillation can be explained as the fluctuations in the phase and intensity of electromagnetic rays after crossing the ionosphere. Rino’s theory was proposed in 1979 to quantify this scintillation, and subsequent models appeared to predict its characteristics. One of them is the WideBand ionospheric scintillation Model (WBMOD) from 1984. This study aims to provide a neural network that emulates the behavior of WBMOD model, by learning from phase and intensity scintillation data gathered from several ESA projects. By using the Rino’s power-law phase-screen ionospheric scintillation theory, the values of the height-integrated irregularities strength (CkL) and the slope of its PSD (q) can be obtained from the physically measured S4and σϕ. So, using this data, two neural networks are presented which obtains results that fit well with the WBMOD expected values.

A modification of the Barcelona Expert Center (BEC) algorithm to downscale the Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) to 300 m spatial resolution is presented. It maintains the same functional relationship as the currently implemented version but employs the following inputs: SMOS brightness temperature (TB) and SM (25 km), European Center for Medium Weather Forecast (ECMWF) skin temperature (9 km), and Sentinel 3 Normalized Difference Vegetation Index (NDVI, 300 m).The performance of the downscaled SMOS SM at 300 m is analyzed by means of a temporal validation with in-situ observations from the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) and the Continuous Soil Moisture and Temperature Ground-based Observation Network (RSMN) during the year 2021. No significant differences in correlation, unbiased root mean square difference (ubRMSD) and bias are obtained over both networks compared to the 25 km and 1 km SM products, suggesting the BEC downscaling algorithm could work at hundreds of meters and result in a similar SM accuracy.

Over 14 million tons of plastic end up yearly in the ocean, originating from polluted waterways such as rivers, sewers, or storm waters, or from industrial and nautical activities with improper garbage disposal methods. Due to its long decomposition time, and the presence of currents and gyres, most of plastic debris freely floating in the ocean end up forming large extensions of marine litter known as garbage patches. An example is the Great Pacific garbage patch located in the central North Pacific Ocean covering an area larger than 1.6 million squared kilometers. Some works have recently reported the potential of GNSS-Reflectometry to detect marine plastic litter from space. This study presents the results of a controlled field experiment conducted under the auspices of ESA at the "Atlantic Basin" at the Deltares research institute (Delft, The Netherlands).

In the last decades, the number of wireless devices has notably increased, and improper RF filtering has resulted in an increase in Radio Frequency Interference (RFI). At L-band this is due to a plethora of reasons: from VHF/UHF harmonics, higher frequency band (e.g. WiFi boosters) emissions, or even intentional RFI created by Personal Privacy Devices (PPD), all these have resulted in a degradation of both Active and Passive Microwave Remote Sensing [1], [2]. Almost simultaneously, there has been an explosion of smallsats, notably following the CubeSat and PocketQube [3] form factors, both being excellent hands-on project topics for introducing students into the aerospace domain [4].In these circumstances, the IEEE Geoscience and Remote Sensing (GRSS) Open PocketQube Kit [5] initiative was started. The goal is to develop a set of flight ready Picosatellites with Earth Observation payloads complete with assembly, integration and testing tutorials and video tutorials. Each of these PocketQubes, while identical in all other regards, will house different Remote Sensing oriented payloads. One of them, and the focus of this work, is a L-band (1-2 GHz) Radio Frequency Interference Monitor, notably for the L1, L2 and L5-bands of GNSS [6] systems, and the 1400-1427 Mhz band reserved for passive observations.

A Random Forest (RF) regression-tree method to derive high-resolution (60 m) surface soil moisture maps is proposed in this study. The developed methodology integrates multi-source synergies by incorporating information from the visible, near-infrared until short-wave infrared spectrum (Sentinel-2), reanalysis data (ERA5-Land) and terrain information (SRTM), using exclusively open access data. The analysis focuses on the central part of the Iberian Peninsula and covers a four-year period (2018-2021). The resulting high-resolution soil moisture maps exhibit greater spatial heterogeneity compared to the ESA Climate Change Initiative (CCI) soil moisture, which was used as a reference in the training of the RF model. These maps have been evaluated using in situ soil moisture measurements from the REMEDHUS network, and show good agreement in terms of Pearson's correlation (0.83), and uRMSE (0.028 m3•m-3), demonstrating the method’s significant potential for deriving high-resolution soil moisture information.

From 1998 to 2022, Earthquakes have caused more than 850 thousand deaths and US$ 700 billion in economic losses. At this time, there is no reliable precursor to predict them. Many studies have attempted to link Land Surface Temperature (LST) anomalies to earthquake occurrence. This study uses long-term datasets from FengYun-2F/VISSR (Visible and Infrared Spin Scan Radiometer) sensors [1] between 2014 and 2022 to analyze the extent of LST anomalies and their potential link to earthquakes. More than 14,000 land and coastal earthquakes of magnitude higher than M4 have been studied between [+40°, +140°] longitude. Three methods frequently used in the literature, the Standard Deviation (STD), the Interquartile (IQT), and the Z-Score techniques have been implemented to detect LST anomalies on temporal series. Other parameters, such as the confusion matrix and the Receiver Operating Curves (ROC), have also been computed to assess and improve their performance and select the optimum correlation threshold. A positive LST anomaly is typically found before the earthquakes, followed by an LST decrease after the event.

This study presents a new signal processing method to mitigate RFI, based on the Karhunen-Loève Transform. The main property of this method is that it is adaptive and RFI-agnostic – i.e., it is able to potentially mitigate any type of RFI waveform. It is based on the estimation of the covariance matrix of the process, and the derivation of a basis that spans the RFI subspace. The methodology to find this basis is explained in detail. The theoretical performance obtained using KLT is explored by simulations, along with the evaluation of the influence of the system parameters. In addition, a couple of examples with real data are provided.

Significant Wave Height (SWH) is a key parameter to characterize waves, which is typically used in sea state monitoring such as wave forecast to ensure ocean navigation safety. Satellite radar altimeter is probably the primary tool to obtain SWH information. However, it cannot be used for large-scale sea state monitoring unless many of theses satellites are deployed. In this article, we aim to study the potential of Global Navigation Satellite System (GNSS)-Reflectometry (GNSS-R) in SWH measurement based on spaceborne Delay-Doppler Maps (DDMs) data. First, 3 observables (i.e., Delay-Doppler Map Average (DDMA), leading edge slope (LES) of normalized integrated delay waveform (NIDW) (LES-NIDW), and trailing edge slope (TES) of NIDW (TES-NIDW) derived from the DDMs are introduced for SWH estimation. Then, an empirical SWH retrieval model is proposed based on three observables. Subsequently, ERA5 SWH is used as reference data to verify the performance of the proposed model. The experimental results show that the Root Mean Square Error (RMSE) and Correlation Coefficient (CC) estimated by SWH of the three observables are better than 0.54 m and 0.88 m, respectively. Among them, the estimation performance based on DDMA observable is the best, with RMSE and CC of 0.49 m and 0.89 m. This study shows the potential of spaceborne GNSS-R in SWH retrieval.

GNSS-R (Global Navigation Satellite Systems Reflectometry) can be understood as a multi-static radar with as many transmitters as navigation satellites and in view and can be tracked. GNSS-Reflectometers can process the reflected signals as a scatterometer, as an altimeter, or as an unfocused synthetic aperture radar. GNSS-R has demonstrated its potential to infer numerous geophysical variables over land (soil moisture, vegetation height, detecting freeze-thaw states... ), over the ocean (wind speed and direction, significant wave height, sea surface altimetry... ), over sea ice (extent, depth, type... ). Even a marine plastics litter product has been recently released by NASA, and some have suggested that sea surface salinity could also be inferred. In scatterometric applications the most widely used GNSS-R observable is the peak of the Delay Doppler Map (DDM), and many efforts have been directed towards an accurate instrument calibration. However, many geophysical parameter retrievals have neglected some variations of the DDM linked to the observation geometry, as well as the sensitivity to other geophysical variables. In this study we present analyze some of these effects and present a sensitivity analysis for the ocean case, notably the impact of the wind direction (WD), the 10 m height wind speed (mathrm{U}-{10}), which is routinely obtained today from GNSS-R observables, the sea surface temperature (SST) and sea surface salinity (SSS), and the presence of oil slicks. This quantitative study illustrates the challenges presented to retrieve some of these variables, the required corrections and their accuracy.

Following the discovery of the ionosphere by Marconi in 1901, different disciplines have been influenced from the ionospheric effects on radio-waves, such as communications or Earth Observation missions. The ionosphere acts as an electrical layer that is continuously changing due mainly to solar activity. Therefore, it is not a trivial work to predict how radio signals would be affected. This study presents the implementation of a Matlab ray-tracer to predict radio-wave propagation through the ionosphere. This program is inspired on a Fortran code developed in the 70's, but it is extended to include the state-of-the-art models, such as the IRI (International Reference Ionosphere), the IGRF (International Geomagnetic Reference Field), and the NRLMSISE-00 (Naval Research Lab, atmospheric model). A statistical model of bubbles and depletions is also included for increased accuracy. The simulator provides several graphs and a text document, both summarizing the ray trajectories and main propagations effects. This tool is being developed as part of an ESA project devoted to the study of ionospheric effects in low frequency radars, namely radar sounders and Synthetic Aperture Radars, and GNSS systems.

In 2019, the IEEE 2nd GRSS Student Grand Challenge selected the Remote sensing and Interference detector with radiomeTry and vegetation Analysis (RITA) payload to fly on board of AlainSat-1, a 3U CubeSat from the United Arab Emirates' National Space Science and Technology Center (NSSTC). The RITA payload is equipped with a Commercial Off- The-Shelf (COTS) Software Defined Radio (SDR) for the RF signal conditioning and a System on a Chip (SoC) processor in charge of controlling the execution and processing of each experiment that conforms the payload. RITA includes an L-band microwave radiometer (MWR), a Radio Frequency Interference (RFI) detector, a LoRa transceiver, and a hyper-spectral camera. A multi-level acquisition strategy joining several sensors for the aforementioned experiments' allows this payload retrieve parameters such as: soil moisture derived from the MWR, Normalized Difference Vegetation Index (NVDI) obtained from the hyper-spectral camera, improved acquisition of vegetation-related measurements through the LoRa transceiver, or even the compilation of worldwide interference maps. This manuscript will focus on the design, implementation, verification, and testing of the MWR on board the RITA payload, a successor of previous CubeSat missions such as FSSCat, which carried the FMPL-2 payload, or 3Cat-4 carrying FMPL-1 payload.

Today hyperspectral imagers are capable to capture hundreds of spectral bands simultaneously, while they are getting smaller, fitting in CubeSats. The huge amount of information provided by these sensors is used for numerous studies, and it can be used to infer multiple variables. However, the huge data volume generated is difficult to be managed by large satellites, and it is almost impossible in the smaller ones. This work presents a low resource consuming unsupervised band selection algorithm that uses only the information of the captured image, and no further auxiliary information. It can be used to reduce the amount of data to be downloaded, while controlling the performance degradation. © 2022 IEEE.

Passive microwave remote sensing encompasses techniques well suited for use in systems with limited power budget, due to the low consumption linked to reception-only operation. Some examples of these techniques include Microwave Radiometry (MWR), Global Navigation Satellite Systems Reflectometry (GNSS-R), and Radio-Frequency Interference (RFI) detection algorithms. Small satellite missions including these instruments, such as FSSCat, have proven the value of passive remote sensing techniques with high revisit times due to the low orbits, and the possibility to horizontally scale the mission with more satellites. The Flexible Microwave Payload (FMPL) used in the FSSCat mission has continued development within the RITA Payload project, and it is now being used in Drone-based platforms to provide in-situ data using space-proven acquisition algorithms. In this work, the development of a drone-based FMPL will be presented.

Recent studies have shown possible signatures or precursors of seismic activity in the ionosphere. Our group is focusing on ionospheric scintillation associated with seismic activity. By the time this study was conducted, the sudden volcanic eruption in the Spanish La Palma island, starting on September 19th, opened a possibility to study the impact of this exceptional, well time-defined seismic event on the iono-sphere and the radio-wave propagation through it. A complete study measuring scintillation on GNSS signals and its correlation to seismic activity is presented in this extended abstract. In particular, scintillation data from two ground stations in the Canary Islands, NASA CYGNSS GNSS-Reflectometry and Spire GNSS Radio-Occultation measurements have been used. A linear correlation analysis has been conducted between S4, and the earthquakes generated energy in 6 h intervals. Small, and slightly positive regression coefficients have been found with almost all methods.

Annually, earthquakes cause human and material losses. For instance, between 1998 and 2018, 846 thousand deaths and about US$ 661 billion of economic losses were recorded due to earthquakes. Currently, there is no clear precursor to forecast earthquakes. However, numerous investigations have attempted to find precursor proxies based on Land Surface Temperature (LST) anomalies. In this study, a big database collected from GOES/ABI instrument during the full year 2020 has been used to calculate the LST anomalies in the earthquakes zones. A total of 1350 earthquakes of Mw ≥ 4 were studied in 2020. Two methods commonly used in the literature, the interquartile method, and the standard deviation of the time series, have been applied to detect LST anomalies. The confusion matrix, some figures of merit, and the receiver operating characteristic curve have been used to evaluate and enhance the performance of the methods and choose the optimum decision threshold. A positive anomaly is usually found before the earthquakes, followed by an LST decrease after the event.

Vegetation canopy height (CH) is one of the important remote-sensing parameters related to forests’ structure, and it can be related to the biomass and the carbon stock. Global navigation satellite system-reflectometry (GNSS-R) has proved capable to retrieve vegetation information at a moderate resolution from space (20–65 km) using L1 C/A signals. In this study, data retrieved by the airborne microwave interferometric reflectometer (MIR) GNSS-R instrument at L1 and L5 are compared to the Global Forest CH product, with a spatial resolution of 30 m. This work analyzes the waveforms (WFs) measured at both bands, and the correlation of the waveform width and the reflectivity values to the CH product. A neural network algorithm is used for the retrieval, showing that the combination of the reflectivity and the waveform width allows to estimate the CH information at a very high resolution, with a root-mean-square error (RMSE) of 4.25 and 4.07 m at L1 and L5, respectively, which is an error about 14% of the actual CH.

The ability to enhance the spatial resolution of measurements collected by a conical-scanning microwave radiometer (MWR) is discussed in terms of noise amplification and improvement of the spatial resolution. Simulated (and actual) brightness temperature profiles are analyzed at variance of different intrinsic spatial resolutions and adjacent beams overlapping modeling a simplified 1-D measurement configuration (MC). The actual measurements refer to Special Sensor Microwave Imager (SSM/I) data collected using the 19.35 and the 37.00 GHz channels that match the simulated configurations. The reconstruction of the brightness profile at enhanced spatial resolution is performed using an iterative gradient method which allows a fine tuning of the level of regularization. Objective metrics are introduced to quantify the enhancement of the spatial resolution and noise amplification. Numerical experiments, performed using the simplified 1-D MC, show that the regularized deconvolution results in negligible advantages when dealing with low-overlapping/fine-spatial-resolution configurations. Regularization is a mandatory step when addressing the high-overlapping/low-spatial-resolution case and the spatial resolution can be enhanced up to 2.34 with a noise amplification equal to 1.56. A more stringent requirement on the noise amplification (up to 0.6) results in an improvement of the spatial resolution up to 1.64.

For the first time, rain effects on the polarimetric observations of the global navigation satellite system reflectometry (GNSS-R) are investigated. The physical feasibility of tracking the modifications in the surface roughness by rain splash and the surface salinity by the accumulation of freshwater is theoretically discussed. An empirical analysis is carried out using measurements of a coastal GNSS-R station with two side-looking antennas in right- and left-handed circular polarizations (RHCP and LHCP). Discernible drops in RHCP and LHCP powers are observed during rain over a calm sea. The power drop becomes larger at higher elevation angles. The average LHCP power drops by$\approx ~5$dB at an elevation angle of 45°. The amplitude of the correlation sum shows a dampening, responding to rain rate systematically. The LHCP observations show higher sensitivity to rainfall compared to RHCP observations. The retrieved standard deviation of surface heights shows a steady increase with the rain rate. The derived surface salinity shows a decrease at rains higher than 10 mm/h. This study confirms the potential under environmental conditions of the GNSS-R ground-based station, e.g., with salinity mostly lower than 30 psu, over a calm sea, being a starting point for future investigations.

Radio frequency interference (RFI) is an increasing problem in microwave radiometry, particularly for Earth observation, because the antennas are pointing toward the Earth. RFI has been observed at the L-band in European Space Agency’s (ESA’s) Soil Moisture and Ocean Salinity (SMOS) Earth Explorer mission, National Aeronautics and Space Administration’s (NASA’s) Soil Moisture Active and Passive (SMAP) and Aquarius missions, and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and WindSat missions at 10.7 and 18.7 GHz (Misra and de Matthaeis, 2014). Therefore, dedicated onboard systems are, nowadays, a must to detect and remove contaminated measurements, improving radiometric accuracy and increasing the spatial coverage. In this work, a novel detection technique, specially tailored for synthetic aperture interferometric radiometers (SAIRs), is proposed and its performance analyzed. It is based on the change of the shape of the cross correlation function at lags different from zero under the presence of RFI. The performance of the proposed technique is compared to other common RFI detection algorithms, and its limitations and advantages are discussed. Postcorrelation detection performance is found to outperform other commonly used algorithms, such as kurtosis. In addition, it presents some convenient properties for its practical application in correlation and real aperture radiometers.

Radio Frequency Interference (RFI) is an increasing problem particularly for Earth Observation using Microwave Radiometry. RFI has been observed, for example, at L-band by the ESA’s SMOS (Soil Moisture and Ocean Salinity) Earth Explorer and by NASA’s SMAP (Soil Moisture Active Passive) and Aquarius missions, as well as at C-band by AMSR-E and AMRS-2; and at 10.7 GHz and 18.7 GHz by AMSR-E, AMRS-2, WindSat and GMI [1], [2]. Therefore, systems dedicated to interference detection and removal of contaminated measurements are nowadays a must in order to improve the radiometric accuracy and reduce the loss of spatial coverage caused by interference. In this work, the feasibility of using the Empirical Mode Decomposition (EMD) technique for RFI mitigation is explored. The EMD, also known as Hilbert-Huang Transform (HHT), is an algorithm that decomposes the signal into Intrinsic Mode Functions (IMF). The achieved performance is analyzed, and the opportunities and caveats that this type of methods present are described. EMD is found to be a practical RFI mitigation method, albeit presenting some limitations, and a considerable complexity. Nevertheless, in some conditions, EMD exhibits a better performance than other commonly used methods (such as frequency binning). In particular, it has been found that EMD performs well for RFI affecting the < 25% lower part of the Intermediate Frequency (IF) bandwidth.

The calibration strategy for synthetic aperture interferometric radiometers based on noise injection has been successfully demonstrated in the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) instrument onboard the Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA). With the emerging demands of increasing both array size and frequency bands, the internal calibration hardware becomes more difficult to implement. Although several external calibration methods have been proposed, the so-called phase ambiguity problem was not carefully considered. In this article, a novel calibration strategy for both phase and amplitude calibrations based on redundant space calibration is proposed, which utilizes the unique structure of equispaced circular array and rotational sampling strategy. The original irregular phase ambiguity degenerates to the fixed$\pi $-ambiguity, where only two possible solutions to phases are introduced. Both phase and amplitude of visibility can be simultaneously calibrated by the proposed strategy, and no cooperative calibration target is needed.

In the theoretical part of this work, a calibration strategy for both phase and amplitude calibration of rotating equispaced circular array based on redundant space calibration was introduced. It avoids the challenging implementation of conventional noise-injection networks, especially for a large-scale array, by using the strong self-consistency accomplished by array rotation. This study presents comprehensive simulation results of the proposed calibration strategy, where calibration residuals and image reconstruction errors are evaluated. The phase calibration shows outstanding performance, while the amplitude calibration is found to be biased due to the logarithms used to linearize the system of calibration equations, and the resulting image reconstruction bias is scene-dependent. A bias correction method is proposed accordingly, which requires ana prioriregional brightness temperature within the observation scene. The performance of both phase and amplitude calibration take advantage of increasing the number of elements, which promises the system scalability for high spatial resolution applications.

Soil Moisture and Ocean Salinity mapping by Earth observation satellites has contributed significantly toward a better understanding of the Earth system, such as its hydrosphere or climate. Nevertheless, an increased spatial resolution below 10 km with a radiometric resolution in the range of 2 K–3 K of radiometric data could yield a more complete picture of global hydrological processes and climate change. Operational radiometers, such as SMOS, have already approached prohibitive sizes for spacecraft due to the required large antenna apertures. Therefore, radiometer concepts based on a large number of satellites flying in close proximity (swarms) have been proposed as a possible solution. This article investigates the orbit mechanics of placing a satellite swarm-based motion induced synthetic aperture radiometer (MISAR) in low Earth orbit for Earth observation applications. The aperture synthesis antenna array is formed by a large number of individual antennas on autonomously controlled nanosatellites (deputies) and a correlator antenna in the Y-configuration carried by a chief satellite. The proposed design methodology is based on the optimization of satellite positions within a plane and the subsequent translation of coordinates into initial conditions for general circular orbits (GCOs). This enables a more computationally efficient orbit optimization and ensures the time invariance of the antenna array response. Based on this methodology, simulations have been performed with swarms consisting of up to 96 satellites. Simulations show that the spatial resolution of an aperture synthesis radiometer can be increased to less than 10 km for applications where the requirements on radiometric sensitivity are more relaxed ($\Delta T\sim 3$K).

An enhanced spatial resolution brightness temperature product is proposed for future conical scan microwave radiometers. The technique is developed for Copernicus Imaging Microwave Radiometer (CIMR) measurements that are simulated using the CIMR antenna pattern at the L-band and the measurement geometry proposed in the Phase A study led by Airbus. An inverse antenna pattern reconstruction method is proposed. Reconstructions are obtained using two CIMR configurations, namely, using measurements collected at L-band by the forward (FWD) scans only, and combining forward and backward (FWD+BWD) scans. Two spatial grids are adopted, namely,$3 \mathrm {\,\,km} \times 3 \mathrm {\,\,km}$and$36 \mathrm {\,\,km} \times 36 \mathrm {\,\,km}$. Simulation results, referred to synthetic and realistic reference brightness fields, demonstrate the soundness of the proposed scheme that provides brightness temperature fields reconstructed at a spatial resolution up to ~1.9 times finer than the measured field when using the FWD+BWD combination.

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the Single Channel Algorithm (SCA) is first applied to SMOS brightness temperatures at vertical polarization (TBV) to estimateSMat coarse-resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of single scattering albedo (ω) and soil roughness (hs) parameters. These effective parameters are used together with fine-scale multi-angular Sentinel-1 backscatter in a single-pass active-passive downscaling approach to estimateTBVat fine-scale (1 km) for each SMOS incidence angle. TheseTBVare finally inverted to obtain the corresponding high-resolutionSMmaps. Results over the Iberian Peninsula for year 2018 show an increasing trend of ω and a decreasing trend ofhswith SMOS incidence angle, with almost no variability of ω across land cover types. The active-passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fineTBVmaps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolutionSMmaps have maximum differences in mean and standard deviation of 0.016 and 0.015 m3/m3, respectively, and compare well within situmeasurements. Our results indicate that model-based microwave approaches to estimateSMcan be adequately adapted to account for the incidence angle diversity of planned missions such as CIMR, ROSE-L and Sentinel-1 next generation.

This paper address the design of a phase tracking block for the DVB-S2X user terminals in a satellite precoding system. The spectral characteristics of the phase noise introduced by the oscillator, the channel, and the thermal noise at the receiver are taken into account. Using the expected phase noise mask, the optimal parameters for a second-order PLL intended to track channel variations from the pilots are calculated. To validate the results a Simulink model was implemented considering the characteristics of the hardware prototype. The performance of the design was evaluated in terms of the accuracy and stability for the frame structure of superframe Format 2, as described in Annex E of DVB-S2X.

ESA's BIOMASS Earth Explorer mission aims at providing global above ground forest biomass. Its payload is a UHF synthetic aperture radar. At UHF ionospheric effects, notably the group delay and phase advance, the Faraday rotation, and the intensity and phase scintillations (or rapid fluctuations) are significant and have to be properly modelled to be compensated, to understand the limitations of the proposed technique, and to select the optimum observation conditions. This paper summarizes the structure and main characteristics of the ionospheric module of the BIOMASS end-to-end performance simulator (BEEPS-IOM).

FSSCat, the “Federated Satellite Systems/3Cat-5” mission was the winner of the 2017 ESA S^3 (Sentinel Small Satellite) Challenge and overall winner of the Copernicus Masters competition. FSSCat consists of two 6 unit cubesats carrying on board UPC's Flexible Microwave Payload - 2 (FMPL-2), an L-band microwave radiometer and GNSS-Reflectometer implemented in a software defined radio, and Cosine's HyperScout-2 visible and near infrared + thermal infrared hyperspectral imager, enhanced with PhiSat-1, a on board Artificial intelligence experiment for cloud detection. Both spacecrafts include optical and UHF inter-satellite links technology demonstrators, provided by Golbriak Space and UPC, respectively. This paper describes the mission, and the main scientific results of the FMPL-2 obtained during the first three months of the mission, notably the sea ice concentration and thickness, and the downscaled soil moisture products over the Northern hemisphere.

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (δ), representative of biomass/structure dynamics. The dynamic range of δ is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that δ ranging from 0 to 3.35.10-4is needed for modelling physically reasonable Mg values. The global average of δ shows consistent spatial patterns across vegetation distributions, and δ seasonality is coherent with the phenology of the studied vegetation types. These findings enable the separation of information on vegetation water and biomass/structure inherent within VOD.

Microwave radiometers can be considerably affected by Radio Frequency Interference (RFI). These man-made interference conceal the underlying natural signal, preventing the retrieval of geophysical variables. Adopting on-board detection and mitigation techniques is a requirement to reduce the impact of RFI. Several families of RFI detection algorithms have been developed over the last years (e.g. [1]–[4]). In this work, a new detection technique is proposed and its performance analyzed. It is based in the distortion of the shape of the cross-correlation function at lags different from zero under the presence of RFI. Its performance is compared to other common RFI mitigation algorithms. Proposed methods' performance is found to surpass other common algorithms such as signal Kurtosis, while presenting some convenient properties for its practical application in correlation and synthetic aperture radiometers.

Consistent sea ice monitoring requires accurate estimates of sea ice concentration. Current retrieval algorithms are based on low-resolution microwave radiometry data with limited penetration depth and are unable to resolve surface characteristics of sea ice in sufficient detail which is necessary to discriminate intact sea ice from closed water. Important information about surface roughness conditions are contained in the distribution of radar backscattering images which can be - in principle - used to detect melt ponds and different sea ice types. In this work, a two-step probabilistic approach based on Expectation-Maximization and Bayesian inference considers the spatial and statistical characteristics of medium-resolution daily-available Sentinel-1 SAR images. The presented method segments sea ice into a number of separable classes and enables to discriminate surface water from the remaining sea ice types.

Knowledge about sea ice concentration and extent in polar regions is of great interest both for economic interests, and as a proxy of the climate change. Retrieved maps are based on data from microwave radiometers, which are currently provided by large satellite missions. Nowadays, CubeSats have proven to be a cost-effective alternative. Due to their low cost, they can be launched in large constellations to obtain high spatial coverage and daily revisit. This study presents a neural network approach to generate sea ice concentration and sea ice extension maps using the L-band microwave radiometer, and the GNSS-Reflectometer data from the FMPL-2 instrument onboard3Cat-5/A, one of the two CubeSats of the FSSCat mission. The results obtained during the first 2 months of the mission are presented.

Several factors may induce perturbations on the ionospheric plasma, changing its average electron density and creating small-scale irregularities, changing its shape and altitude. Solar irradiance and space weather are some of the main factors affecting the ionosphere. They produce a seasonal and daily dependence, modulated by the solar cycle, with more ionospheric activity during periods of higher solar activity. Recent studies shows that another source of perturbations for the ionosphere may be related to internal Earth parameters as seismic activity, in particular, earthquakes. In the period before an earthquake, rocks in the lithosphere are subjected to pressures and movements that may create variations of electromagnetic fields and low frequency waves interacting with the ionosphere. In this work, the ionospheric scintillation intensity index or S4 is estimated from GNSS-R data collected by NASA CYGNSS, and it is correlated with earthquakes events in 2020. Furthermore, it is compared with plasma fluctuation indices measured by ESA Swarm satellites. Two earthquakes in 2020 with magnitudes larger than 7 in the central America region are shown in this work.

The Flexible Microwave Payload -2 is the microwave remote sensing payload of the FSSCat mission. The instrument is the outcome of years of work of the Universitat Politécnica de Catalunya Passive Remote Sensing Laboratory, and the NanoSat-Lab. The FSSCat mission was launched on board the Vega VV16, on September the 3rd, 2020. The instrument was executed for the very first time on September the 16th, 2020, and it was commissioned less than two weeks later. This extended abstract presents the geo-located FMPL-2 measurements, which consist of a Global Navigation Satellite System - Reflectometer and an L-band radiometer. The performance of the instrument and preliminary level 1C results are presented, showing that CubeSats can to perform passive microwave remote sensing scientific missions.

This work presents the first scientific results over land from the Flexible Microwave Payload −2 (FMPL-2), onboard the FSSCat mission. FMPL-2 is composed of an L-band microwave radiometer and a Global Navigation Satellite System - Reflectometer (GNSS-R). Two separate ANNs models are trained using the first three months of collected data of both observations, with the objective to retrieve global soil moisture maps. The first network addresses the coarsely-resolved FMPL-2 antenna footprint in a downscaling approach. Predicted values resulted in good agreement with those obtain from the SMAP mission, with an error smaller than 9.6%, and a bias smaller than 0.001 m3/m3. The second network is implemented to estimate soil moisture exclusively on GNSS-R data. In this second case, the combination of multiple GNSS-R measurements in a single track allows to retrieve soil moisture data with an error standard deviation with respect to SMAP lower than 0.056 m3/m3, with a bias smaller than 0.0007 m3/m3.

The Microwave Interferometric Reflectometer (MIR) is an airborne GNSS-R instrument developed by Universitat Politècnica de Catalunya. In 2018, it was flown twice over the agricultural Yanco area, New South Wales, Australia, once after a very dry period, and a further time the day after a strong rain event. This rain event resulted in many crop fields being entirely flooded, producing a saturation in the GNSS-R reflectivity value. In this work, the received data set is processed to identify the optimum integration time with the goal to minimize pixel blurring. This issue is assessed for airborne conditions, and then extra-polated to the spaceborne case. The presented results show that the blurring of the GNSS waveform is produced even from an airborne sensor with short integration times. Following the determination of an optimal integration time for the platform in use, the surface roughness term in the reflectivity equation can be isolated due to the signal saturation during very wet surface conditions. The final results from the two channels (L1 C/A and L5) are subsequently presented. In this case, it is shown that most reflectivity variations in GNSS-R measurements are linked to surface roughness and Speckle noise fluctuations rather than soil moisture changes.

Vegetation Optical Depth (VOD) is highly sensitive to Above-ground Biomass (AGB), particularly at L-band, as this band is more sensitive to the canopy layer and has greater capacity to penetrate the vegetation. However, higher frequencies, such as C- and X-bands are more related to smaller vegetation structures. Then a combination of these three bands is expected to be relevant to accurate AGB retrievals. This study presents a comparison of three VOD products at three bands and evaluates the performance of a multi-frequency VOD combination by applying a Principal Component Regression (PCR) to Forest biomass. Results show that L-band VOD captures the 84% of the biomass and the PCR is built, mainly, for this band meaning that higher frequencies do not contribute substantially to the AGB retrieval in this study, still they do not affect the retrievals negatively.

The novel Correlated Triple Collocation (CTC) analysis allows to assess three different data sources of similar spatial resolutions, but with two of them being correlated. In this study, the CTC was applied to estimate the unbiased random errors of the global soil moisture (SM) data provided by two L-band satellite missions —the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP)— and one numerical model—the ERA5-Land. The three existing SMOS SM products distributed by different research institutions were also analyzed. Preliminary results revealed that errors of SMOS and SMAP SM are correlated, with correlations of ∼0.5-0.6. Thus, only ERA5-Land can be considered as independent. The lowest error was obtained for SMAP (0.025 m3m−3), followed by ERA5-Land (0.036 m3m−3). Among the SMOS SM, SMOS-IC had the lowest error (0.046 m3m−3), SMOS-BEC showed an intermediate value (0.048 m3m−3), and SMOS-CATDS had the highest error (0.055 m3m−3).

Incidence angle diversity of space-borne L-band radiometers needs to be taken into account for a consistent estimation of surface soil moisture (SM). In this study, the Land Parameter Retrieval Model (LPRM) is applied to SMOS brightness temperatures to calibrate the effective scattering albedo (w) and the soil roughness (h1) parameter against ERA5-land SM. The analysis is carried out for SMOS data at three different incidence angles ($32.5\pm 5^{\circ},\ 42.5\pm 5^{\circ}$and$52.5\pm 5^{\circ}$) focusing in 2016 on the three main land cover types of the Iberian Peninsula according to the Climate Change Initiative (agricultural, forest and grassland). The parameterization shows an increasing trend of w and h1with rise of incidence angle. The SM retrieval have been evaluated with in situ SM measurements of the REMEDHUS network on rainfed crop fields. Both compare well at the three incidence angles, obtaining high correlations (0.81-0.85), an ubRMSE around 0.04 m3m−3and low bias (0-0.015 m3m−3).

Radio Frequency Interference (RFI) is a major concern for both real and synthetic aperture radiometers. After the lessons learnt from SMOS, ESA is preparing the next generation of L-band interferometric radiometers with RFI mitigation integrated into the cross-correlators. This work presents a preliminary design and results of a pre-correlation RFI mitigation algorithm tailored for interferometric radiometers. The results show that the correlation error introduced by the RFI is reduced on average to the half, with peaks of 20 dB of mitigation.

Distributed antenna arrays, fractionated payloads and cooperative platforms can provide unprecedented performance in the next generation of spaceborne communications and remote sensing systems. Remote phase synchronization of physically separated oscillators is the first step towards a coherent operation of distributed systems. This work shows the preliminary results of a TDD remote phase synchronization algorithm with a master-follower architecture. Herein, we describe the implementation and validation of the proposed algorithm. The implementation has been conducted in a Cubesat-ready software defined radio and validated at the end-to-end satellite communications testbed available at the University of Luxembourg.

A modification to the extended CLEAN deconvolution method for passive microwave interferometric radiometers is presented to account for the interferometer beam patterns that vary depending on the source position within the interferometer field of view. This method is developed for the newly proposed multisatellite rotating microwave interferometric radiometer concept for geostationary atmospheric sounding, which is found to have a spatially varying beam pattern. The proposed method is implemented by the piecewise application of Fast Fourier Transform (FFT)-based convolution, i.e., the image is divided into discrete segments, where the beam pattern is assumed to be constant within each segment. This approach allows the proposed method to exploit the speed advantage of the FFT. Applying this method to the proposed multisatellite interferometer shows a twofold improvement in the radiometric accuracy of the restored brightness temperature map, as compared with the constant beam pattern CLEAN.

In this study, an enhanced spatial resolution product is proposed for the forthcoming L-band microwave radiometer operated on board of the FSSCat mission. The product is obtained reconstructing the brightness temperature on a finer spatial resolution grid using an antenna pattern deconvolution method. Simulated FSSCat noisy measurements are used to showcase the soundness of the proposed approach that results in remarkable reconstruction performance.

The ionosphere impacts radio-wave propagation, notably up to a few GHz. The main applications impacted by the ionosphere are GNSS positioning and timing, Earth Observations (especially low frequency SAR missions e.g. BIOMASS, and GNSS-R), and Space Weather. While most effects can be compensated by using dual-frequency receivers and circular polarization antennas, ionospheric scintillation (rapid intensity and phase fluctuations) cannot. Climatological models for the mean stable part of the electron density in the ionospheric layers (e.g. IRI or NeQuick) or for the magnetic field (e.g. WMM) have significantly improved in the past years. However, models of the inhomogeneous part, responsible for scintillation, can be improved, since they are based on relatively old data (e.g. WBMOD), or climatological inputs are limited to properly characterize all latitudes and solar conditions (e.g. GISM or WAM). This study first assesses the goodness of GISM, the model adopted by the ITU-R, by comparing GISM predictions and measured scintillation data. Then, the impact of measured intensity and phase scintillation on TDS-1 GNSS-R data is illustrated.

FSSCAT mission was the 2017 ESA Sentinel Small Satellite (S^3) Challenge winner, and the Copernicus masters competition overall winner, and it will be launched in late March 2020. FSSCAT data will provide soil moisture data, sea ice cover and coarse resolution sea ice thickness. FSSCat will be launched for Kourou Space port on June 18th, 2020, 10:51 PM local time. MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) campaign is a one-year long field experiment to study the climate system and the impact of the climate change in the Arctic sea. GNSS-R data from the second leg has been received recently. At the conference the first results of the FMPL-2 payload (combined GNSS-R and microwave radiometer instrument) on board FSSCat will be presented. Preliminary results on the PYCARO-2 GNSS-R instrument in MOSAIC, and model results fed with in-situ auxiliary data are presented in this paper.

In February 2019 a Project Authorization Request was approved by the Institute of Electrical and Electronics Engineers (IEEE) Standards Association with the title “Standard for Global Navigation Satellite System Reflectometry (GNSS-R) Data and Metadata Content”. A Working Group has been assembled to draft this standard with the purpose of unifying and documenting GNSS-R measurements, calibration procedures, and product level definitions. The Working Group (http://www.grss-ieee.org/community/technical-committees/standards-or-earth-observations/) includes members, collaborators, and contributors from academia, international space agencies, and private industry. In a recent face-to-face meeting held during the ARSI+KEO 2019 Conference, the need was recognized to develop a standard with a wide range of operations, providing procedure guidelines independently of constraints imposed by current limitations on geophysical parameters retrieval algorithms. As such, this effort aims to establish the fundamentals of a potential virtual network of satellites providing inter-comparable data to the scientific community.

The celebration of the International Polar Year in 2007 remarked the necessity to better monitor the polar regions. Moreover, the H2020 “Operational Network of Individual Observation Nodes” (ONION) project analyzed and proposed an overall EU strategy and technical guidelines to develop and implement innovative Earth Observation concepts in the time frame 2021-2027. The top priority that was identified was the Maritime Weather Forecast, and in particular the Arctic sea ice monitoring use-case. Currently, those needs have been addressed with remote sensing satellites or in-situ instruments. However, in-situ instruments often sink or crash with ice blocks. Additionally, these instruments are isolated, making challenging its deployment and the data retrieval, using LEO satellite communications (e.g. Iridium, OrbComm). This also poses a problem in terms of modularity and flexibility, since sensors rely on proprietary communication systems. This work proposes a modular and cost-effective solution to this problem applying the paradigm of the Internet of Things to this context. Particularly, Long Range (LoRa) is one of the most frequently used and efficient technologies that also ensures long ranges. As part of the presented work, a satellite constellation is proposed, analyzed to cover the Arctic region. The maximum number of sensors simultaneously using the LoRa technology is also evaluated.

Attitude determination and control systems (ADCS) are critical in many satellite missions, especially when high pointing accuracy is needed, such as in optical imagers. Current actuators used to control the platform's attitude, such as reaction wheels or magnetorquers, have some limitations, inducing some jitter, which is crucial in small satellites. This jitter induces the image blurring. This work analyzes the impact of actuator's jitter, and then a deblurring technique to minimize and correct the distortion produced is studied. Two points are addressed to restore the blurred images. First, the data coming from the inertial sensors embarked on the satellite to infer the movement of the camera is used. Secondly, a deblurring technique for non-constant blurring over an image is applied.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission was launched 2 Nov 2009 and, to date, is still in good health, providing valuable L-band observations of the Earth surface [1]. A number of products are obtained from these, including thin sea ice [2], frost/thaw soils [3], high winds [4], ocean surface wind [5] and Sun brightness temperature [6], besides the main mission measurements of soil moisture and sea surface salinity [7] [8]. This paper deals with the description and early results of some technology activities conducted by ESA applying the lessons learnt by SMOS and in preparation of an advanced L-band radiometer mission.

Remote Sensing from distributed platforms has become attractive for the community in the last years. Phase, frequency, and time synchronization are a crucial requirement for many such applications as multi-static remote sensing and also for distributed beamforming for communications. The literature on the field is extensive, and in some cases, the requirements an complexity of the proposed synchronization solution may surpass the ones set by the application itself. Moreover, the synchronization solution becomes even more challenging when the nodes are flying or hovering on aerial or space platforms. In this work, we discuss the synchronization considerations for the implementation of distributed remote sensing applications. The general framework considered is based on a distributed collection of autonomous nodes that synchronize their clocks with a common reference using inter-satellite links. For this purpose, we implement a synchronization link between two nodes operating in a full-duplex fashion. The experimental testbed uses commercially available SDR platforms to emulate two satellites, two targets, and the communication channel. The proposal is evaluated considering phase and frequency errors for different system parameters.

As compared to the using L1C/A signals, L5/E5a Global Navigation Satellite System - Reflectometry (GNSS-R), gives improved resolution over the Earth's surface due to the sharper auto-correlation function. Furthermore, the larger transmitted power (+3dB with respect to L1 C/A), and correlation gain (+40dB) allows the reception of weaker reflected signals. If high directivity antennas are used, very short incoherent integration times are needed to have enough signal-to-noise (SNR) ratios, allowing the reception of multiple specular reflection points such as crest of consecutive waves without the blurring induced by long incoherent integration times. This study presents for the first time experimental evidence of the wind and swell waves signatures in the GNSS-R waveforms, and compares them with models.

Global Navigation Satellite Systems Reflected (GNSS-R) signals exhibit an incoherent and a coherent components [1], [2]. Current models assume that one or the other are dominant, and the calibration, and geophysical parameter retrieval (eg. wind speed, soil moisture ...) are developed accordingly. Even the presence itself of the coherent component of a GNSS reflected signal has been a matter of discussion in the last years. In this work, the method used in [3] to separate the leakage of the direct signal from the reflected one is applied to a set of GNSS signals reflected collected over the ocean by the MIR [4], [5], an airborne dual-band (L1/E1 and L5/E5a), multi-constellation (GPS and Galileo) GNSS-R instrument with two 19-elements array with 4 beam-steered each. The results presented demonstrate the feasibility of the proposed technique to untangle the coherent and incoherent components in GNSS reflected signals. This technique allows the processing of these components separately, which will increase the calibration accuracy (as today both are mixed together), and allows high resolution applications since the spatial resolution of the coherent component is determined by the size of the first Fresnel zone [6] (300-500 meters from a LEO satellite), and not by the size of the glistening zone (~25 km from a LEO satellite).

This study addresses the spatial resolution enhancement of synthetic microwave radiometer observations as obtained by the forthcoming Copernicus Imaging Microwave Radiometer (CIMR). An antenna pattern deconvolution scheme is used to exploit the actual L-band CIMR antenna pattern together with a regularization scheme to reconstruct the brightness field at enhanced spatial resolution. Simulation results, referred to synthetic and realistic reference brightness fields, demonstrate the soundness of the proposed scheme that provides brightness fields reconstructed at a spatial resolution up to 1.5 times finer than the measured field.

Weather forecast relies to a large extent on data acquired by satellite. LEO polar satellites provide global coverage, but poor spatial resolution. GEO satellites provide a better coverage and revisit time, at expenses of poorer spatial resolution. LEO constellations of small satellites offer the promise of global coverage with good spatial resolution and revisit time. However, hosted payloads on aircrafts offer the potential of very high resolution and very low revisit time for regional applications. In this work, the idea of equipping commercial airliners with low-cost Global Navigation Satellite System - Reflec-to meter (GNSS-R) receivers is explored, with emphasis on real-world data and the implications the expected scientific yield would offer.

Global Navigation Satellite System (GNSS) signals are used in Earth Observation for Radio Occultation and Reflectometry. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers and navigation have suddenly sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily relies on the nature and location of the interfering sources. In some cases, null-steering or shielding can be used to mitigate RFI effects. In this work, a system to detect and locate RFI sources is presented, including signal classification and recording for countermeasure-related decision-making.

In the last years, the need to access Earth Observation high spatial resolution data with very low latency, ideally in near-real-time, has increased. Distributed Satellite Systems have emerged as an effective and efficient architecture to deal with these tight requirements. One of these systems is the Federated Satellite Systems which explore the benefits of sharing unused and available resources between satellites, such as memory storage or downlink opportunities. This interaction is known as satellite federations. Additional downlink contacts may enable low-latency communications and increase the downlink capacity. Therefore, the deployment of federations would allow satisfying current Earth Observation community demands. This work contributes to establish this promising paradigm by presenting a proof-of-concept of a federated system, called FSS Experiment payload. This payload is boarded into three stratospheric balloons, and it provides them communications means to create federations. The design of the payload, and the flight campaign results are presented in this work. These results demonstrate the feasibility of deploying federations, as well as the benefits of deploying them in future EO missions.

Unaffected by the atmospheric conditions and solar illumination, L-band emission and scattering are sensitive to vegetation water content and can be used to estimate crop yield. However, for rice which has an inundated period during its growing cycle, the current methods do not work due to the water under the crops. In this paper, we propose to use Global Navigation Satellite System Reflectometry (GNSS-R) signals to find how the water in rice field influence vegetation optical depth (VOD) which had been recently used to estimate the crop yield. Soil moisture (SM) and VOD in Thailand rice fields are compared to signal to noise ratio (SNR) from CYGNSS. Good correlation among them has been found. Results indicate that GNSS-R signals can be used to flag the presence of water and develop an adapted VOD algorithm that can be used to improve the estimation of rice yields.

Global Navigation Satellite Systems Reflectometry can be understood as a multistatic radar using satellite navigation signals as signals of opportunity. The scattered signals over sea ice, flooded areas, even under dense vegetation, and in some cases, over land show a significant coherent component. Under coherent scattering conditions, it is usually stated that the coherent signal component comes from an area equal to the first Fresnel zone. This letter analyzes in more detail the spatial resolution in this forward scattering configuration, showing that, when coherent scattering is nonnegligible, the spatial resolution is mostly determined by the geometry and not by typical surface roughness values. As the scattering area around the specular reflection point increases and encompasses the first Fresnel zone, the received power increases and then it fluctuates as higher order Fresnel zones are included (rapid phase changes due to the spherical waves). These contributions may explain in part the large scattering encountered over inhomogeneous land regions, as these different contributions add or subtract, depending on the phase of the electric field, and are weighted by different scattering coefficients (i.e., changes in the dielectric constant and/or surface roughness, such in water ponds or some agricultural fields). Finally, over homogeneous targets, when all Fresnel zones are included, the received power tends asymptotically to the value obtained using the free-space propagation with a total path length equal to the sum of the path lengths, weighted by the reflection coefficient. This value can also be interpreted as coming from an effective region that is actually ~0.6 times the first Fresnel zone.

The relative phase and amplitude calibration between individual receiving elements are one of the key challenges in interferometric microwave radiometry. For monolithic interferometers, for example the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) on board the Soil Moisture and Ocean Salinity (SMOS) mission, the calibration of these two parameters is accomplished by correlated noise injection, where a noise signal from a central noise source is distributed to multiple elements in phase, and any measured phase and amplitude differences can be measured and compensated. As new multisatellite interferometers are being proposed, a new strategy capable of calibrating these errors between the free-flying antennas as well as any antenna position errors is required. In this article, a new calibration scheme is proposed which uses a set of microwave beacons in place of the central noise source, placed at known locations within the interferometer's field of view. The visibility function produced by these point-sources can be precalculated, and any errors between the calculated and the measured visibility samples can be attributed to the errors to be determined. Since the phase of the measured visibility is a function of phase and antenna position errors, this technique is capable of calibrating these two parameters simultaneously. Calibration equations for far- and near-field beacons are presented. Using these expressions, five interferometric calibration routines are proposed and examined for geostationary formation flight microwave radiometers. Although this technique is ideal for multisatellite interferometers with variable antenna positions, it is also applicable to monolithic interferometers that can undergo substantial array deformation.

During these years, novel Distributed Satellite Systems (DSS) have addressed the new Earth Observation (EO) requirements (e.g. near-real time access to data, or multi-point observations). One of the DSS proposals is the concept of Federated Satellite System (FSS) which has explored the benefits of sharing available and unused resources between satellite to maximize the system utility. The possibility to use additional downlink opportunities thanks to federations is a resource that could improve current EO missions, and thus achieving the required performance. For that reason, the FSS Experiment payload has been implemented as a FSS proof-of-concept between 6-Unit CubeSats in the FSSCAT mission. This article presents the design, and the test results of this payload demonstrating its feasibility for future EO missions.

Global Navigation Satellite System Reflectometry and L-band microwave radiometry have been used for soil moisture, biomass, and cryosphere studies. Combining both technologies in a low-power and cost-effective solution could largely improve current Earth observations.3Cat-4 mission is a 1-Unit CubeSat technology demonstrator of the Flexible Microwave Payload - 1, a reduced size payload that combines these two technologies. This work presents the objectives of the3Cat-4 mission and the details of the spacecraft architecture and performance. Each spacecraft subsystem is detailed at hardware and software levels. In addition, the presented results indicate that the current spacecraft design will survive the flight conditions (i.e. thermal and structural ones).

Mapping carbon stocks in the tropics is essential for climate change mitigation. Passive microwave remote sensing allows estimating carbon from deep canopy layers through the Vegetation Optical Depth (VOD) parameter. Although their spatial resolution is coarser than that of optical vegetation indices or airborne Lidar data, microwaves present a higher penetration capacity at low frequencies (L-band) and avoid cloud masking. This work compares the relationships of airborne carbon maps in Central and South America with both (i) SMAP L-band VOD at 9 km gridding and (ii) MODIS Enhanced Vegetation Index (EVI). Models to estimate carbon stocks are built from these two satellite-derived variables. Results show that L-band VOD has a greater capacity to model carbon variability than EVI. The resulting VOD-derived carbon estimates are further presented at a detailed (9 km) spatial scale.

Radio Frequency Interference (RFI) is an increasing problem in Microwave Radiometry, particularly for Earth Observation. It has been observed at L-band in ESA's SMOS (Soil Moisture and Ocean Salinity) Earth Explorer mission, as well as in NASA's SMAP (Soil Moisture Active and Passive) and Aquarius missions, as well as in AMSR-E and WindSat missions at 10.7 GHz and 18.7 GHz [1]. Therefore, dedicated on-board systems are a must to detect and remove contaminated measurements, improve the radiometric accuracy, and guarantee a larger spatial coverage. In this work, the influence of quantization and sampling in the performance of several RFI detection and mitigation techniques is studied for different types of microwave radiometers. It will be shown that coarse quantization schemes strongly impairs RFI mitigation performance, a fundamental aspect to be taken into account when defining the architecture of future instruments.

Soil moisture is a key indicator for the management of a resource as precious and scarce as water. Sectors such as agriculture, environmental monitoring, and territory management, need to know the soil hydrological stress to take the appropriate actions. This work describes the second campaign conducted in the framework of the HUMIT project to test the readiness of data fusion techniques to retrieve high resolution soil moisture from hyperspectral and GNSS-R data. The results of the HUMIT project support the feasibility of a combined 6U CubeSat optical and GNSS-R mission for soil moisture monitoring. This second flight was carried out on October 22nd2018 near Balaguer (41.79° N, 0.80° E), over an agricultural area of approximately 4 km2.

In this study, the ability of GNSS-R technique to observe and track hurricane is investigated from the CYGNSS mission, as the specular reflection points tracks pass through the eye of Hurricane Irma. The clear variations of the CYGNSS observables, including the Normalized Bistatic Radar Cross Section (NBRCS), the Leading Edge Slope (LES), the Signal-to-Noise ratio (SNR), and the derived wind speed, are examined. CYGNSS-derived wind speeds are compared to other satellite missions, including ASCAT, WindSat, and SMAP. CYGNSS-derived wind speeds exhibit a similar behaviour than the collocated ASCAT, WindSat, and SMAP over the hurricane eye, but are underestimated under high wind conditions.

In January 2019 a Project Authorization Request was submitted to the IEEE standards association with the title "Standard for Calibration of Microwave Radiometers in the 300 MHz to 1 THz Frequency Range for Geoscience Applications". An open committee is being assembled to draft this standard with the purpose of unifying and documenting calibration procedures for a wide range of microwave radiometers. The committee includes members, collaborators, and contributors from academia, international government and private industry. We include ground-based, air-borne, and space-borne systems. The standard will also define standardized terminology, and address procedures required to obtain traceability to fundamental units or constants. The scope of the standard encompasses various radiometer geometries, Dicke switching, total power, and differential, as well as different polarization configurations including fully polarized (full Stoke's) radiometers. The standard will also separately address free-space and single-mode (e.g. transmission-line) radiometer calibration techniques.

Global Navigation Satellite System Reflectometry (GNSS-R) uses the GNSS reflected signals to study parameters of the Earth's surface such as ocean surface height, wind speed, soil moisture, sea surface target detection. In this paper fourteen DDMs (Delay Doppler Maps) of TechDemoSat-1 (TDS-1) containing anomalous artefacts are presented and analyzed. Anomalous artefacts are not caused by the reflection from Earth surface targets, occultation, nor the leakages of direct signals, but likely - according to their delays- from reflection of targets above the Earth's surface (either airborne or spaceborne).

Spaceborne constellations composed by several heterogeneous platforms are an actual solution to undertake Earth Observation missions. However, designing such missions present problems due to the heterogeneity and the multiple design levels that must be considered. In this paper, a high-level methodology to address the design of a spaceborne Earth Observation constellation is outlined. In addition to the framework, this document presents an application of this methodology to a specific use-case, the Agriculture Hydric Stress for a worldwide coverage, and describes its results.

Phase, frequency and time synchronization is a crucial requirement for many applications as such as multi-static remote sensing and distributed beamforming for communications. The literature on the field is very wide, and in some cases, the requirements of the proposed synchronization solution may surpass the ones set by the application itself. Moreover, the synchronization solution becomes even more challenging when the nodes are flying or hovering on aerial or space platforms. In this work, we compare and classify the synchronization technologies available in the literature according to a common proposed framework, and we discuss the considerations of an implementation for distributed remote sensing applications. The general framework considered is based on a distributed collection of autonomous nodes that try to synchronize their clocks with a common reference. Moreover, they can be classified in non-overlapping, adjacent and overlapping frequency band scenarios.

The FSSCat mission is a two nano-satellite Earth Observation mission based on 6-unit CubeSats. This paper is focused on the description of one of the remote sensing payloads: the Flexible Microwave Payload 2 (FMPL-2), on board the3Cat-5/A. The payload design is based on a Software Defined Radio, aiming to demonstrate the capabilities of nano-satellites to provide valuable scientific data. Two different instruments are integrated into a single payload: a multi-constellation (GPS and Galileo) Global Navigation Satellite System Reflectometer (GNSS-R), and a Total Power Radiometer (TPR) with Radio Frequency Interference (RFI) detection and mitigation capabilities. This paper presents the payload design, and the tests conducted during the qualification campaign.

The Flexible Microwave Payload 2 (FMPL-2) is a Remote Sensing payload carried on board3Cat-5/A, one of the two 6-unit CubeSats of the FSSCat mission. FMPL-2 is a payload based on a Software Defined Radio (SDR), integrating two instruments in a single platform: a multi-constellation (GPS and Galileo) Global Navigation Satellite System Reflectometer (GNSS-R), and a Total Power Radiometer (TPR) with Radio Frequency Interference (RFI) detection and mitigation capabilities. This paper focuses on the description of the software developed for the platform, with a detailed description on the optimizations used for the two instruments.

Arctic and Antarctic sea ice covers are in a sharp contrast in terms of characteristics, distributions, and processes with a drastic decrease in the Arctic versus the opposite increase in the Antarctic in a changing climate. In quantifying polar sea ice differences to address the contrasted sea ice behaviors, two key parameters are sea ice thickness and melt pond fraction, which remain challenging to measure extensively in time and in space with a sustainable approach. Here, we present a new paradigm for such measurements using bistatic radar reflectometry, thanks to developments of low-cost receivers to acquire reflected signals from numerous existing transmitter systems operated at multiple frequencies to be replenished and sustained indefinitely into the future. For sea ice thickness measurement to determine ice volume, reflected signals likely come from the bottom ice-water interface avoiding large errors inherent in current altimetry techniques due to uncertainty in free-board height and snow cover. Regarding melt pond faction on sea ice to estimate albedo and insolation, the bistatic reflection can be dominated by melt pond water with permittivity that is one order of magnitude larger compared to that of snow or ice. These are examined by a combination of numerical Kirchhoff (KA) simulator and Numerical Maxwell Model of 3D simulations (NMM3D) to preserve phase and amplitude information and thereby account for both coherent and incoherent effects. Physical insights from the rigorous theory for bistatic radar reflectometry will be valuable to develop future satellite missions to resolve cryospheric science issues concerning the polar sea ice differences.

Global Soil Moisture and Ocean Salinity (SMOS) Level 3 (L3) soil moisture (SM) products are being routinely distributed by the Barcelona Expert Centre (BEC). The quality and accuracy of these SM products have been demonstrated not only by direct validation, but also by its adoption in a wide range of applications. Recently, changes in SMOS Level 2 (L2) SM have led to the reprocessing of the BEC SMOS L3 SM. As in previous versions, a filtering and a weighted binning based on the uncertainty of the SM retrievals by means of the Data Quality Index (DQX) was applied for the L3 production. However, the DQX was modified in the latest L2 release (v650), which could possibly have an influence in the performance of the derived products.This study assesses the impact of the current DQX-based BEC L3 SM quality filtering and binning approach and the possibility of using an alternative strategy based on the chi-squared (χ2) parameter, which is defined as the cost function of the retrieval. The study is performed over continental USA using in situ SM from the U.S. Climate Reference Network (USCRN) as a benchmark. In both approaches, similar results were obtained in terms of correlation and unbiased root mean square difference (ubRMSD). Nevertheless, the χ2-based L3 SM is in general slightly wetter and has a lower dry bias than the DQX-based L3 SM. Further assessments will be performed to stablish the optimal filtering/binning of BEC SMOS L3 SM products.

Thanks to the successful operations of the UK TDS-1 and NASA CYGNSS GNSS-R missions, a wealth of Delay-Doppler Maps (DDM) are being measured from the ocean, but also from land reflections. Using the land reflected DDM, several studies are being conducted to retrieve the land geophysical parameters, such as soil moisture, vegetation depth, and biomass. Although they have shown the dependence of the land geophysical parameters on the DDM, it is also shown that many other parameters impact the DDM. This work presents the impacts of some parameters on the DDM. For the systematical and efficient study, an E2E simulator is used. The simulator generates the synthesized DDM reflected over land varying the input parameters, which are the specular point position on the Earth, the elevation angle at the specular points, soil moisture, etc. From the simulation results, the relation between the input parameters and the DDM is individually analyzed, providing the clue to the retrieval algorithm of the geophysical parameters.

The number of applications based on Global Navigation Satellite Systems (GNSS) has been increasing in the last years. With its proliferation, Radio-Frequency Interference (RFI) has become one of the most concerning topics on GNSS-based devices for navigation, positioning and timing, but also for Earth Observation purposes such as GNSS-Reflectometry and Radio Occultations due to the corruption of the received signal and the corresponding geophysical measurements. The Front-End GNSS Interference eXcisor (FENIX) mitigates the problem of RFI by providing a plug and play solution to excise interferences out of virtually any system. The new version of FENIX is also capable of operating with large signal bandwidths (~50 MHz) and dual-frequency applications. This paper shows the performance of the system for commercial GNSS receivers and GNSS-reflectometry applications. A discussion of the impact of the new architecture on its future capabilities is presented as well.

Mosquitoes are the most important vectors of some human diseases in tropical countries. In particular, Aedes ægypti is the main vector for Chikungunya, Dengue, and Zika viruses in Brazil (Scavuzzo, 2018). The infection causes flu-like symptoms, and sometimes evolves into a life-threatening condition called severe dengue or hemorrhagic dengue. It is a widespread infection that occurs in all regions of the planet's tropical climate, and now because of the climate change, it may expand to some other regions. In recent years, transmission has increased predominantly in urban areas and has become a major public health problem.Recently, there has been an increasing trend on GeoHealth studies by use of remotely sensed data for mapping health risk and monitoring vector-borne diseases. Thus, the aim of this work is to generate risk maps integrating those environmental and climate indicators with adaptive capacity factors to define regions with high risk of vector-borne diseases in Brazil. Consequently, we present a multi-indicator study, similar to the one already developed in Vietnam by Nguyen and Liou (Nguyen and Liou, 2018) for the Aedes Albopictus, but the present study is in Brazil for the Aedes ægypti and adding the information of the Soil Moisture (SM) from the Barcelona Expert Center (BEC). Then, the study combines climatological data (e.g. soil moisture, temperature and precipitation), and demographic and socioeconomic data over the Brazilian territory for the 2013-2018 period. Dengue episodes distribution data for this period have been obtained from the Notifiable Diseases Information System (SINAN), developed by Ministry of Health.

As the TDS-1 and CYGNSS spaceborne GNSS-R missions progress, a large number of Delay-Doppler Maps (DDM) is being collected, from the land scattered as well as from the ocean. Based on those data, the feasibility study on the retrieval of land geophysical parameters such as soil moisture and vegetation is being studied. This work presents the E2E simulator that generates the synthesized DDM reflected over land. For the efficient development, the existing simulator for ocean scattering has been upgraded to accommodate the specific functionalities for land scattering: topography, surface roughness, vegetation effects, in addition to surface soil moisture, obviously. The developed end-to-end (E2E) simulator is used to conceive the retrieval algorithms and assess their performance. In this work, the upgraded items are described and illustrated with some simulation results.

The characterization of the ionosphere and its impact on the propagation of radio-wave signals has received an increasing interest for satellite communications, Global Navigation Satellite Systems (GNSS) applications (i.e. navigation and timing), and Earth Observation (EO) missions, especially low frequency SAR missions and GNSS-Reflectometry (GNSS-R). Although the main climatological models for the mean stable part of the electron density in the ionospheric layers (e.g. IRI or NeQuick) or for the magnetic field (e.g. WMM) have been significantly improved in the past years, the inhomogeneous part, responsible for scintillation effects, can still be significantly improved, since models are based on relatively old data (e.g. WBMOD), or the climatological inputs are limited to properly characterize all latitudes and solar conditions (e.g. GISM or WAM). ESA's CLIM IONO study aims to use the experimental observations of the ionosphere collected in the past years to assess the performance of climatological ionosphere models, with the focus on scintillation models, in order to evaluate their ability to properly support future needs, to identify weak areas if any, to propose recommendations for improvements and to implement these improvements whenever possible in existing models. This work focuses on the capabilities of GNSS-R techniques to characterize ionospheric scintillation, notably at low and high latitudes.

FSSCAT is an innovative mission concept consisting of two federated 6U Cubesats in support of the Copernicus Land and Marine Environment services. The first CubeSat carries the Flexible Microwave Payload-2 (FMPL-2), a dual microwave payload (a GNSS-Reflectometer and an L-band radiometer with interference detection/mitigation), and the second one a hyper-spectral optical payload. The combined use of these payloads will allow to measure soil moisture, ice extent, and ice thickness, and to detect melting ponds over ice. FSSCAT also includes radio and optical inter-satellite links to test some of the techniques and technologies for the upcoming satellite federations. FSSCAT will be the precursor of a constellation of federated small satellites for Earth observation achieving high temporal resolution and moderate spatial resolution in a cost-effective manner.

The potential of GNSS-R techniques to estimate land surface parameters such as Soil Moisture (SM) is experimentally studied using 2014-2017 global data from the UK TechDemoSat-1 (TDS-1) mission. The approach is based on the analysis of the sensitivity to Soil Moisture of different observables extracted from the Delay Doppler Maps (DDM) computed by the SGR-ReSI instrument using the L1 (1575.42 MHz) left-hand circularly-polarized (LHCP) reflected signals emitted by navigation satellites. After quality-filtering the data (spacecraft not `in eclipse' and the direct signal not present in the DDM), topography filtering the data, correction for background noise, antenna pattern gain, different distance propagation factors, and vegetation attenuation, the sensitivity of different GNSS-R observables to SM and its dependence with incidence angle is analyzed.

Global change is increasing the risk of forest decline worldwide, impacting carbon and water cycles. Hence, there is an urgent need for predicting forest decline occurrence. To that purpose, this study links forest decline events in Catalonia, detected by the DEBOSCAT forest monitoring program, with information from the Soil Moisture and Ocean Salinity (SMOS) satellite. Firstly, this study reviews the role of the SMOS soil moisture in a previous forest decline episode occurred in 2012, where the authors concluded that dry soils increased the probability of observing decline in broadleaved forests. Secondly, the present study detects that forest decline in 2012 and 2016 was linked to very dry soil conditions (generally with SM3·m-3). A similar analysis is proposed using SMOS Vegetation Optical Depth (VOD) data, which is a proxy of vegetation hydric status. Results and preliminary models will be presented at IGARSS 2018.

Vegetation Optical Depth (VOD) at L-band is highly sensitive to the water content and above-ground biomass of vegetation. Hence, it has great potential for monitoring crop phenology and for providing crop yield forecasts. Recently, the Multi-Temporal Dual Channel Algorithm (MT -DCA) has been proposed to retrieve L-band VOD from Soil Moisture Active Passive (SMAP) measurements. In previous research, SMAP VOD has been compared to crop phenology and has been used to derive crop yield estimates. Here, we review and expand these initial research studies. In particular, we quantify the capability of VOD to detect different crop stages, and test different VOD metrics (i.e., maximum, range and integrals of VOD) to provide crop yield estimates in the United States Corn Belt. Results show that VOD captures 50% to 70% of crop changes during growing and maturing phases, and that it explains between 44% (in heterogeneous crop regions) and 74% (in homogenous croplands) of final crop yields.

The3Cat-3/MOTS mission is a joint initiative between the Institut Cartogràfic i Geològic de Catalunya (ICGC) and the Universitat Politècnica de Catalunya-BarcelonaTech (UPC) to foster new and innovative technologies on Earth Observation (EO) based on the data fusion of Global Navigation Satellite Systems Reflectometry (GNSS-R) and optical payloads on a 6U CubeSat platform. The final objective of the mission is to provide soil moisture mapping through the GNSS-R payload and improve its resolution by data fusion with the multi-spectral optical data acquired. This technique has been successfully applied to SMOS, but never performed from a 6U CubeSat carrying both payloads. An airborne optical and GNSS-R field experiment has been carried out to test/develop the algorithm and to analyze the results both separately (GNSS-R + optical data) and as a soil moisture product, once performed the data fusion.

This work is part of “Operational Network of Individual Observation Nodes” (ONION). ONION is an H2020 project devoted to identify solutions and to satisfy emerging needs of the European Earth Observation (EO) market. In this regard, this work presents a review and analysis of the potential payloads and next generation of sensors in order to cover the measurements gaps of the top ten use cases of the European EO infrastructure. For each use case the potential instruments to support stakeholder requirements are identified, pointing out the aspects in which they represent an innovation with respect to existing technological solutions. This identification of instruments is based on existing, operational, under development, and demonstration missions. The capability of the instrument technologies is evaluated according to the trends in the design for small EO satellites (payload mass <; 500 kg). This work concludes with a set the potential instruments to be possibly developed as part of the future European space infrastructure, proposing an integrated set of instruments and measurements to optimize the performances, simplifying the implementation, and reducing the costs.

The3Cat-4 mission aims to demonstrate the capabilities of nano-satellites plus the versatility of a Software Defined Radio for passive Earth Observation. Three different microwave payloads are integrated into a single unit CubeSat platform: a multi-constellation (GPS and Galileo) and a dual-band (L1 and L2) Global Navigation Satellite System - Reflectometer receiver, a total power radiometer including a novel Radio Frequency Interference (RFI) detection and mitigation technique, and an Automatic Identification System receiver for vessels tracking. Being able to validate these technologies in a CubeSat enables their fast adoption as hosted payloads or in more performing dedicated platforms in the future. This paper shows a novel approach for embedding multiple passive microwave payloads in a single platform.

Global Navigation Satellite Systems - Reflectometry (GNSS-R) has shown his potential for remote sensing. Since new wider band signals are being broadcasted, and new systems are becoming available, a performance comparison in fair conditions between signals and system is required. For this purpose, the Universitat Politecnica de Catalunya has been developing the MIR instrument, an airborne GNSS reflec-to meter that mimics PARIS-IOD, and that also aims to better understand some GNSS-R techniques, the RFI analysis and mitigation, and the use of beamforming techniques to improve the GNSS-R capabilities. This work presents the advances in the instrument, the end-to-end tests, and the field campaign being planned in Australia for Spring, 2018.

In order to provide high resolution soil moisture maps, the Institut Cartografic i Geológic de Catalunya (ICGC) in colab-oration with Universitat Politecnica de Catalunya are planing to deploy the3Cat-3 picosatellite, which aims to estimate soil moisture from space using Global Navigation Satellite Systems - Reflectometry (GNSS-R) and to downscale it using data fusion with a multi-spectral camera. As a proof of concept, a field campaign is being carried out in Lleida, Catalunya. MALYGNSS is a GNSS reflectometer flying as an opportunity sensor that will validate the main reflectometer of the project: the CORTO instrument.

The maximum coherent integration time of GNSS reflected (GNSS-R) signals over sea depends on the sea state, as well as on the geometry formed by the receiver, the GNSS satellite and the reflection point. In this paper, real data obtained with a dedicated airborne instrument named MALYGNSS (Multi-band Airborne L-Band reflectometrY with GNSS) is used to acquire GPS L1 C/A and L2C, and Galileo E1OS signals. The direct signals are used as a reference to detect any phase shift due to data bits changes and the presence of a residual Doppler frequency. These phase transitions are removed in the reflected signals, and coherent correlations of different lengths are performed in order to study sea correlation time.

After more than 8 years in orbit the Soil Moisture and Ocean Salinity (SMOS) satellite is still in good health and several algorithms for improving its spatial resolution have been proposed and validated in a variety of catchments. However, none of them has yet been applied at the global scale. In this article we present: i) a review of the latest SMOS-BEC downscaling algorithm, which allows for its global application using an adaptive moving window and ii) a thorough validation of the resulting maps over two in-situ networks: REMEDHUS in Spain and OzNet in Australia. The proposed algorithm combines SMOS brightness temperatures (at ~40 km spatial resolution), and MODIS-derived Land Surface Temperature and Normalized Differenced Vegetation Index (at 1 km), into 1km soil moisture maps. This paper also presents a variant of the algorithm, which allows for cloud-free retrievals. A statistical comparison has been carried out when the MODIS Land Surface Temperature is replaced in the algorithm by the one provided by the ERA5 reanalysis. Fine-scale estimates show good agreement in terms of correlation and root-mean-squared error with in-situ soil moisture.

Radio-Frequency Interference (RFI) is one of the most concerning topics in Global Navigation Satellite Systems (GNSS) and GNSS - Reflectometry (GNSS-R) because any undesired signal present at the antenna can corrupt the received signal and the eventual geophysical measurements. The purpose of this paper is two-fold. First, an RFI analysis is performed to the raw data obtained from an airborne dual-band GNSS reflectometer. Finally, the Front-End GNSS Interference eXcisor (FENIX) mitigation algorithm is applied to the raw data, and then the Signal-to-Noise Ratio (SNR) improvement at the Delay-Doppler Map (DDM) is obtained. From the analysis of one of the datasets, two RFI signals were detected and characterized. Moreover, FENIX output signal was revealed to be much cleaner than before. The mean SNR improvement with coherent DDMs of 1 ms is 0.72 dB, whereas the SNR improvement increases to 1.49 dB when considering 50 incoherent averages. This improvement represents a reduction in the geophysical parameter estimation uncertainty.

Soil moisture constitutes one of the main factors for the study where there is a water deficit in the soil, mainly for semiarid regions. The semiarid region of Brazil, which extends from the northern of the Piaui State to the north of Minas Gerais, is a region vulnerable to drought. the accuracy of soil moisture estimation is important for different studies. Thus, the aim of this work is to present the soil moisture derived from different methods: 1) Soil Moisture and Ocean Salinity (SMOS) satellite products generated at the Barcelona Expert Center (BEC) [2] and 2) System of Monitoring and Alert of Anomaly for Agriculture (SIMAGRI) model, at the semiarid region of Brazil. This region is selected because is a semiarid region recently affected by droughts and in situ measurements are available. Then it is very suitable for validation. In this paper an inter-comparison work with data recorded by that network, HR SM data from BEC and SIMAGRI model is presented. This study has been carried out from November, 2015 to June, 2016. We are going to present the statistical study, using correlation coefficient (R), Bias, and Root Mean Square Error (RMSE) as metrics. The results of this study will help to analyze environmental and economic impacts when droughts are detected in this area, which economy is mainly based on agriculture and to act for mitigating the negative consequences. Finally, it seems that the combination of satellite product and SIMAGRI model can be used as a valuable tool for monitoring and alert in case of dry and/or flooding episodes in different agricultural regions.

During these years, novel Distributed Satellite Systems (DSS) have disrupted traditional space segment paradigms. In particular, Federated Satellite Systems and fractionated spacecraft have explored the benefits of interacting satellite networks and are envisioned to improve Earth observation performance while maximizing mission utility. Inter-Satellite Communications capabilities and spacecraft coordination mechanisms are cornerstone aspects that need be designed and evaluated to achieve many of the envisioned DSS characteristics (e.g. in-orbit data services, autonomous mission planning). Given the system complexity, heterogeneity and large-scale, the design of these critical features has to be grounded on simulation-based frameworks. Consequently, this paper presents the design of a highly modular and reconfigurable software that enables the emulation of DSS. The paper motivates the design and discusses its adaptability to future user needs.

The impact of Radio Frequency Interference (RFI) is a very serious problem for spaceborne microwave radiometry. Many Soil Moisture Ocean Salinity (SMOS) images show serious contamination by the RFI. SMOS is even more impacted by the RFI than real aperture case because the grating lobes are usually higher in Synthetic Aperture Interferometric Radiometer (SAIR) compared to real aperture one. RFI effects should properly be mitigated or filtered out to retrieve the geophysical parameters from SMOS measurements. This work presents a feasibility study of RFI mitigation/filtering for SAIRs. Instead of dealing with brightness temperature image directly, RFI filtering of the subspace of covariance matrix is introduced.

A generic software tool to evaluate the impact of ionospheric disturbances is presented, including low and high latitude physically-based models, and low and high frequency fluctuations. This tool has been developed specifically to assess the performance of navigation receivers, but it is ready to simulate other frequencies and even receivers in dynamic conditions, which allows it to be used in other applications such as communications, GNSS-R, radar altimetry or SAR.

Vegetation Optical Depth (VOD) is related to Vegetation Water Content (VWC). This provides new and highly valuable information for ecological and agricultural studies. In this work, VOD from the Soil Moisture Active-Passive (SMAP) satellite has been retrieved with the new Multi-Temporal Dual-Channel Algorithm (MT-DCA). Then, it has been applied to the study of crop yield trends and crop composition. The increase on VOD (ΔVOD) during crop development has been compared to yield data in two selected regions located in the United States. The first region presents a heterogeneous crop composition and weak ΔVOD-yield relationship (r2=0.21). The second region presents a highly homogenous cover and a strong exponential relationship (r2=0.65) between ΔVOD and yield. A saturation of yield is observed at a certain ΔVOD value. This pattern is probably due to an increasing plant density, which limits the crop yield due to plant physiological stress.

The3Cat-3/MOTS mission is a joint initiative between the Institut Cartogràfic i Geològic de Catalunya (ICGC) and the Universitat Politècnica de Catalunya-BarcelonaTech (UPC) to foster new and innovative technologies on Earth Observation (EO) based on data fusion of Global Navigation Satellite Systems Reflectometry (GNSS-R) and optical payloads on a 6U CubeSat platform [1]. Since 2012, there has been a growing trend to use small satellites [2]. Small satellites possess intrinsic advantages over larger platforms in terms of cost, flexibility and scalability and have also made possible constellations of a great number of satellites at an affordable cost. The latest technological advances and the miniaturization of most devices are also key factors to explain the boom of small satellites.3Cat-3/MOTS mission analysis will be addressed to transform EO data into territorial climate variables (information) at the best possible spatial-temporal resolution.

This work reviews and analyses potentially relevant technologies to ensure that the gaps and stakeholder needs for sea ice products are covered. Each variable related to sea ice that presenting gaps using the European EO infrastructure on the horizon (2020-2030) is revised according the stakeholder requirements, together with the feasibility of deploying available technologies on small satellites. A survey of instruments oriented towards sea ice observations in the past and current is presented. Based on this survey technological capabilities in terms of the spatial resolution, swath, mass, power, and data rate are obtained. In order to identify the technologies, numerical scores are assigned based on instrument capabilities. This works concludes with a set of the technologies compatible with small platforms, with the objective to complement the sea ice data products of Copernicus Services.

Global Navigation Satellite Systems - Reflectometry (GNSS-R) has opened promising results for remote sensing applications. The broadcast of new signals with wider bandwidth provides an opportunity to increase the performance of GNSS reflectometry applications. The Microwave Interferometric Reflectometer (MIR) is an instrument being developed to compare different GNSS-R techniques, namely the conventional and interferometric ones, and assess the capabilities of these new signals and bands of the GPS and Galileo systems. This work describes the tests to validate the beamforming system.

The demonstration of the feasibility of navigation signals signals for remote sensing has lead to a significant increase of new GNSS-R instruments in the recent years. Despite of the type instrument, the observables are based on the cross-correlations of the received GNSS signals. Most of these instruments combine the signals received by multiple chains, in such a way that they need a proper calibration. This work proposes a calibration procedure for GNSS-R instruments by using a calibration signal with similar statistics to the GNSS signals. The method is tested and validated with the Microwave Interferometric Reflectometer (MIR) instrument, which contains 152 chains. The test results will be presented at the conference.

The ESA's Soil Moisture and Ocean Salinity (SMOS, 2009-2017) is the first mission using L-band radiometry to monitor the Earth's global surface soil moisture (SM). After more than 7 years in orbit, many studies have contributed to improving the quality and applicability of SMOS-derived SM maps. In this research, a novel downscaling algorithm is proposed for retrieving high resolution (1 km) SM. This model is an extension of the “universal triangle” technique, and also introduces the concept of adaptive moving window. Its inputs are the low resolution SMOS BEC L3 SM and the brightness temperatures at vertical and horizontal polarizations (SMOS L1C), and the high resolution NDVI and LST from optically-based sensors. The proposed method allows obtaining high resolution SM maps worldwide, with no limitation in extension.

Radio-Frequency Interference (RFI) is a growing problem for applications based on the Global Navigation Satellite Systems (GNSS), including Earth observation using GNSS - Reflectometry (GNSS-R). Nowadays, many efforts are being concentrated to develop RFI signal detectors with high sensitivity, which can help to control the proliferation of RFI generators or jammers. This work aims at designing and testing an instrument capable of detecting and localizing RFI signals at GNSS bands using a microwave radiometer architecture, and a combination of statistical and time-frequency digital detection techniques. The instrument has been tested using different kinds of RFI signals, and its performance has been evaluated as a function of the RFI power. A sensitivity of -135 dBm for continuous wave and chirp signals is reported.

Ships and ice monitoring is of key importance in numerous applications, such as maritime traffic control, prevention of illegal activities, climate change studies, and maritime security. In this work, the feasibility of near real-time ocean target detection from a constellation of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) images is demonstrated by addressing the two following points: 1) simulation of the revisit time allowed by constellations of GNSS-R instruments; 2) derivation and implementation of an ocean target detector aimed at identifying inhomogeneous features inside the delay-Doppler map. The revisit time is computed by simulating a realistic mission as a function of the constellation size and the number of receiving channels of the GNSS-R instrument. The proposed ocean target detection algorithm consists of four steps — pre-processing, pre-screening, selection, and geolocation — and is validated using actual U. K. TechDemoSat-1 data.

A sea ice detection algorithm developed using the U.K. TechDemoSat-1 (U.K. TDS-1) global navigation satellite systems (GNSSs)-reflectometry data over the Arctic and Antarctic regions is presented. It is based on measuring the similarity of the received GNSS reflected waveform or delay Doppler map (DDM) to the coherent reflection model waveform. Over the open ocean, the scattered signal has a diffusive, incoherent nature; it is described by the rough surface scattering model based on the geometric optics and the Gaussian statistics for the ocean surface slopes. Over sea ice and, in particular, newly formed sea ice, the scattered signal acquires a coherence, which is characteristic for a surface with large flat areas. In order to measure the similarity of the received waveform or DDM, to the coherent reflection model, three different estimators are presented: the normalized DDM average, the trailing edge slope (TES), and the matched filter approach. Here, a probabilistic study is presented based on a Bayesian approach using two different and independent ground-truth data sets. This approach allows one to thoroughly assess the performance of the estimators. The best results are achieved for both the TES and the matched filter approach with a probability of detection of 98.5%, a probability of false alarm of ~ 3.6%, and a probability of error of 2.5%. However, the matched filter approach is preferred due to its simplicity. Data from AMSR2 processed using the Arctic Radiation and Turbulence Interaction STudy Sea Ice algorithm and from an Special Sensor Microwave Imager/Sounder radiometer processed by Ocean and Sea Ice SAF have been used as ground truth. A pixel has been classified as a sea ice pixel if the sea ice concentration (SIC) in it was larger than 15%. The measurement of the SIC is also assessed in this paper, but the nature of the U.K. TDS-1 data (lack of calibrated data) does not allow to make any specific conclusions about the SIC.

This work shows a technique to estimate the vegetation opacity and infer the vegetation water content using a single GPS receiver located under the vegetation layer being analyzed. It is based on the measurement of the received signal power when the GPS satellite is at the zenith, and when the GPS satellite is with an elevation of 30 degrees. Assuming a horizontally stratified vegetation layer, the attenuation on the GPS signal due to vegetation doubles at 30° elevation angle compared to the zenith. This attenuation is directly related to the vegetation opacity. Then, the vegetation opacity is related to the vegetation water content by a linear parameter. Initial results relating the measured GPS signal power to canopy parameters obtained from hemispheric photos are shown.

This work demonstrates a methodology to detect sea ice presence over the Arctic and Antarctic regions using Global Navigation Satellite Systems (GNSS)-Reflectometry (GNSS-R) data obtained with the UK TDS-1 satellite. The algorithm is based on estimating the degree of coherence of the received GNSS reflected waveform or Delay-Doppler Map (DDM). While at open ocean conditions, the scattered signal follows the diffuse scattering model, over flat sea ice it follows the coherent scattering model. In order to measure the degree of coherence of the received waveform or DDM, a correlation with the clean Woodward Ambiguity Function (WAF) is performed. The more similar the received signal is to the WAF, the more coherent is the scattering, and consequently, the more likely a flat sea ice surface is involved. In order to assess the performance of the proposed estimator a probabilistic study based on a Bayesian approach is performed, using the OSISAF Sea Ice Concentration (SIC) maps as ground truth. A probability of detection of 97%, a probability of false alarm of 2%, and a probability of error of 2.5% are the best results obtained for the Arctic region.

The GNSS rEflectometry, Radio Occultation and Scatterometry onboard the International Space Station (GEROS-ISS) is an innovative experiment for climate research, proposed in 2011 within a call of the European Space Agency (ESA) for installation at the ISS. This international proposal was the only one selected for further studies by ESA out of ~25 submitted ones. In this work, an updated assessment of the instrument performance for the near-nadir altimetry (GNSS-R) mode and scatterometry is assessed, including the effect of the inter-modulation signals, and the expected degradation due to presence of radio-frequency interference coming from DME/TACAN systems. These results correspond to the study lead by Airbus Defence and Space-Spain, one of the two parallel Phase A studies conducted by ESA.

Global Navigation Satellite Systems-Reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multi-static radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from a ground-based and airborne experiments, but studies using space-borne data are still preliminary. This work presents a sensitivity study of Using TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e. vegetation covers). Despite the scattering in the data, which can be attributed to the temporal and spatial (footprint size) collocation mismatch with the SMOS and MODIS NDVI data, and errors in the land use data preliminary results show a good correlation with soil moisture.

Global Navigation Satellite Systems Reflectometry (GNSSR) is a sort of multi-static radar using navigation signals as signals of opportunity. Due to the large number of transmitting satellites multiple scattering points over the Earth's surface can be simultaneously tracked, which provides wide-swath and improved spatio-temporal sampling over current space-borne radar altimetry missions. The lack of experimental datasets from space covering signals from multiple constellations (GPS, GLONASS, Galileo and Beidou) at dual-band (L1 and L2), and dual-polarization (Right and Left Hand Circular Polarization: RHCP and LHCP), over the ocean, land, and cryosphere remains a bottleneck to further develop these techniques.3Cat-2 is a 6 unit (3 × 2 elementary blocks of 10 × 10 × 10 cm3) cubesat mission designed and implemented at the Universitat Politècnica de Catalunya-BarcelonaTech. It will be launched in late July 2016. During one of the two stratospheric balloon flights of the3Cat-2 payload, the P(Y) and C/A ReflectOmeter (PYCARO), it was found that the received signal power was nearly constant with the balloon height. This revealed the presence of a strong coherent component. This work presents the derivation of a unified equation to account for both incoherent and coherent scattering contributions.

Global Navigation Satellite Systems Reflectometry (GNSSR) is now being recognized as a powerfull tool for remote sensing. With the addition of new frequency bands and codes, it is possible to increase the resolution and accuracy of the measurements. GPS L5 and Galileo E5 bands are becoming popular because of the open codes, with wider bandwidths than the L1 band ones. However, these bands are shared with two radio navigation systems (DME and TACAN) that transmit with high powers and might interfere them. The use of mitigation techniques may affect the power of the received signal, which will affect the GNSS-R observables. This work presents several the mitigation techniques, and evaluates the performance and impact of the selected ones for ESA's GEROS experiment onboard the International Space Station.

This work presents an improvement of the P2EPS end-to-end simulator to assess the performances of Global Navigation Satellite System Reflectometry (GNSS-R) space missions over land. Beyond the single specular reflection model, topography effects are included. In order to avoid the computational burden of facet approach based on the bi-static radar model, the Phong reflection model is employed. By calculating the delay, Doppler frequency, and relative intensity of scattered points, the topography effects can be accounted for as a sort of impulse response in the delay, and Doppler domains to be convolved with the Woodward Ambiguity function.

The GNSS rEflectometry, Radio Occultation and Scatterometry onboard the International Space Station (GEROS-ISS) experiment consists of the installation of a GNSS reflectometry (GNSS-R) instrument on the International Space Station (ISS). Due to the complex structure of ISS, the GEROS-ISS instrument suffers from multipath effects. The collected signals are not only from received directly, but also after multiple reflections in the ISS structure. This work analyzes the multipath effect of the GEROS-ISS instrument based on the ISS structure. Using the ray tracing simulation, all rays reaching the instrument after one or two reflections are computed, as well as the corresponding additional delays. This information, together with the direction of arrival to the antenna arrays (up and down-looking), and the co- and cross-polar antenna patterns is used to assess the impact of multipath on the waveforms are demonstrated.

This work presents the first Delay Doppler Maps (DDMs) obtained with the Microwave Interferometric Reflectometer (MIR) airborne instrument. MIR is a GNSS Reflectometer (GNSS-R) which uses the conventional (cGNSS-R) and interferometric (iGNSS-R) techniques for ocean and land observation. It computes real-time sample-to-sample complex DDMs with averaging and alignment, of up to 4 simultaneous GPS/Galileo satellites at the L1/E1, and L5/E5a bands. Satellites are tracked with high directivity antennas with beam steering. The MIR instrument is designed to validate and compare both GNSS-R techniques with the new available signals, with special focus in the GEROS-ISS payload, and to contribute to the development of geophysical retrieval algorithms.

The number of applications based on Global Navigation Satellite Systems (GNSS) has been increasing in last years. In the passive remote sensing field, GNSS-Reflectometry (GNSS-R) systems have been revealed as cost-effective solutions to retrieve a number of geophysical parameters. However, GNSS-based devices are prone to suffer from Radio-Frequency Interference (RFI) effects due to the extreme low power of involved signals. This paper shows the preliminary results of the Front-End GNSS Interference eXcisor (FENIX), a system designed to mitigate the RFI problem in GNSS-based systems. FENIX is composed by two stages: the RF stage and the SP stage. The former conditions the signal from the antenna, throw the SP stage, and to the receiver; and the latter excises the RFI from the GNSS signal combining thresholding with the Multiresolution Fourier Transform (MFT). Preliminary results show that FENIX can mitigate up to 30 dB a chirp RFI signal generated from commercial jammers.

GEROS-ISS (GEROS hereafter) stands for GNSS REflectometry, Radio Occultation and Scatterometry onboard the International Space Station. It is a scientific experiment, proposed to the European Space Agency (ESA) in 2011 for installation aboard the ISS. The main focus of GEROS is the dedicated use of signals from the currently available Global Navigation Satellite Systems (GNSS) for remote sensing of the System Earth with focus to Climate Change characterisation. The GEROS mission idea and the current status are briefly reviewed.

The empirical results of a global navigation satellite systems reflectometry (GNSS-R) experiment onboard the Balloon EXperiments for University Students (BEXUS) 17 stratospheric balloon performed north of Sweden over boreal forests show that the power of the reflected signals is nearly independent of the platform height for a high coherent integration time Tc= 20 ms. This experimental evidence shows a strong coherent component in the forward scattered signal, as compared with the incoherent component, that allows to be tracked. The bistatic coherent reflectivity is also evaluated as a function of the elevation angle, showing a decrease of ~6 dB when the elevation angle increases from 35° to 70°. The received power presents a clearly multimodal behavior, which also suggests that the coherent scattering component may be taking place in different forest elements, i.e., soil, canopy, and through multiple reflections canopy-soil and soil-trunk. This experiment has provided the first GNSS-R data set over boreal forests. The evaluation of these results can be useful for the feasibility study of this technique to perform biomass monitoring that is a key factor to analyze the carbon cycle.

In radar altimetry, the electromagnetic (EM) bias is originated by the smaller reflectivity of wave crests than troughs; thus, the average sea surface height is underestimated. Bias uncertainty is currently the largest factor in altimetry error budgets. The EM bias in a bistatic forward-scattering configuration at L-band, such as in Global Navigation Satellite Systems Reflectometry (GNSS-R) altimetry, remains one of the major sources of uncertainty in the altimetry error budget. In this paper, the EM bias is computed using numerical simulations. To do so, a time-dependent synthetic non-Gaussian sea surface is created using the Pierson-Moskowitz and Elfouhaily sea surface height spectra and spreading function. The sea surface is then discretized in facets and “illuminated” using a right-hand circular polarization GNSS signal, previously recorded by an up-looking antenna connected to a data logger. The waves scattered from each facet are then computed using the physical optics method under the Kirchhoff approximation, and the radar cross section of each facet is computed. The scattered electric fields are “collected” by a down-looking left-hand circular polarization antenna, and the EM bias is computed based on its fundamental definition. The numerical model is validated against Millet's model (a combined model of the weakly non-linear and modulation transfer models) with real data at C- and Ku-bands. Then, the numerical model is applied at L-band, for bistatic configurations, including different azimuth angles and different wind speeds. It is found that the EM bias is almost insensitive to the sea surface spectra selected and increases with increasing wind speed and incidence/scattering angle (up to ~-20 cm at $\theta_{i,s}=45^{\circ}$ and $U_{10}=12\ \mbox{m/s}$), and it also exhibits a non-negligible azimuthal dependence, which must be accounted for in the error budgets of upcoming GNSS-R altimetry missions.

Soil moisture and ocean salinity (SMOS) brightness temperature (TB) images and calibrated visibilities are related by the so-called G-matrix. Due to the incomplete sampling at some spatial frequencies, sharp transitions in the TB scenes generate a Gibbs-like contamination ringing and spread sidelobes. In the current SMOS image reconstruction strategy, a Blackman window is applied to the Fourier components of the TBs to diminish the amplitude of artifacts such as ripples, as well as other Gibbs-like effects. In this paper, a novel image reconstruction algorithm focused on the reduction of Gibbs-like contamination in TB images is proposed. It is based on sampling the TB images at the nodal points, that is, at those points at which the oscillating interference causes the minimum distortion to the geophysical signal. Results show a significant reduction of ripples and sidelobes in strongly radio-frequency interference contaminated images. This technique has been thoroughly validated using snapshots over the ocean, by comparing TBs reconstructed in the standard way or using the nodal sampling (NS) with modeled TBs. Tests have revealed that the standard deviation of the difference between the measurement and the model is reduced around 1 K over clean and stable zones when using NS technique with respect to the SMOS image reconstruction baseline. The reduction is approximately 0.7 K when considering the global ocean. This represents a crucial improvement in TB quality, which will translate in an enhancement of the retrieved geophysical parameters, particularly the sea surface salinity.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission has been providing L-band brightness temperature (BT) using its instrument, the Microwave Imaging Radiometer using Aperture Synthesis. In the measurements, the negative effect of radio frequency interference (RFI) is clearly present, deteriorating the quality of geophysical parameter retrieval. Detection and geolocation of RFI sources are essential to remove or at least mitigate the RFI impacts and ultimately improve the performance of parameter retrieval. This paper discusses a new approach to SMOS RFI source detection, based on the MUltiple SIgnal Classification (MUSIC) algorithm. Recently, the feasibility of MUSIC direction-of-arrival estimation has been shown for the RFI source detection of the synthetic aperture interferometric radiometer. This paper refines the MUSIC RFI source detection algorithm and tailors it to the SMOS scenario. To consolidate the RFI source detection procedure, several required steps are devised, including the rank estimation of the covariance matrix, local peak detection and thresholds, and multiple-snapshot processing. The developed method is tested using a number of SMOS visibility samples. In the test results, the MUSIC method shows an improvement on the accuracy and precision of the RFI source geolocation, compared with a simple detection method based on the local peaks of BT images. The MUSIC results especially outperform the SMOS BT image on the spatial resolution.

The Light Airborne Reflectometer for GNSS-R Observations (LARGO) is an airborne instrument designed for measuring the coherent reflectivity from different soils. In this work, an improved version of LARGO has been used in a field campaign together with other conventional remote sensing instruments. All the corrections made to the raw coherent reflectivity including the antenna pattern compensation, and a topographic correction are presented. The correlation between corrected coherent reflectivity and in situ soil moisture was not high enough due to the dry conditions of the field campaign.

This work presents two field campaigns performed between December 2014 and June 2015 in order to better understand the behavior of Global Navigation Satellite Systems (GNSS) reflected signals over land. The instruments used in both field campaigns are briefly introduced. One of them is focused on circular polarization, whereas the other is focused on linear polarization (horizontal and vertical). In one field experiment there is evidence of multipath due to the antenna performance. A method to mitigate it is proposed in this work. Finally, some preliminary soil moisture estimations using vertical polarization are shown.

The GNSS rEflectometry, Radio Occultation and Scatterometry onboard International Space Station (GEROS-ISS) is an innovative experiment for climate research, proposed in 2011 within a call of the European Space Agency (ESA) for installation at the ISS. This international proposal was the only one selected for further studies by ESA out of ~25 submitted ones. In this work, the assessment of the instrument performance for the near-nadir altimetry (GNSS-R) mode is assessed, including the effects of multi-path in the ISS structure, the electromagnetic-bias, and the orbital height decay. In the absence of ionospheric scintillations, the altimetry rms error is20 dB at equatorial regions, mainly after sunset, which will seriously degrade the altimetry and the scatterometry performances of the instrument.

Scientific evaluation of the3Cat-2 payload (PYCARO reflectometer) has been performed from the BEXUS 19 stratospheric balloon flight with an apogee of ~ 27,000 m over boreal forests and lakes. The payload was configured in closed-loop mode during this flight. Results show the first-ever multi-constellation Global Navigation Satellite Systems Reflectometry (GNSS-R) measurements at dual-band and dual-polarization.

Fires are a concerning topic in Mediterranean areas. They are increasing in number and extension, probably due to the anomalous dry and hot conditions experienced in this region in the last decade. In this study, more than 2,000 fires that took place in the Iberian Peninsula (2010-2014) were analyzed. The new all-weather version of SMOS-derived soil moisture product at fine scale resolution, as well as ERA-Interim Skin Temperature datasets, were used. Soil moisture and temperature anomalies based in these datasets were computed and included in the database. These information allowed analyzing prior-to-fire conditions. Results reported that more than 70% of fires started under dry and hot conditions, and this percentage rose till 94% in the anomalous conditions prior to the biggest fires. A relation between soil moisture, temperature and burned area is found which could set the basis for a fire risk index based on SMOS data and temperature information.

For a given geometry, the coherence time of a GNSS-R waveform can be initially related to the significant wave height. However, when the coherence time is analyzed, several issues shall be considered, such as the geometry (i.e altitude, elevation angle). This work presents a review of the coherence time and its dependence on the geometry. In addition, the impact that the SWH has on both the coherence time and elevation angles is analyzed. Real data from different scenarios (spaceborne, airborne, and ground based) are used in this study.

The effectiveness of incoherent averaging depends on the level of correlation between consecutive waveforms, which can vary with the geometry and with the sea state conditions. In addition it is also dependent on the lag position, since the level of correlation is not the same for all the lags. An initial estimation of the lag correlation can be done by means the ratio (R) between the actual number of incoherent averages and the effective number of incoherent averages. This work is structured in two parts: In the first part the concept of using of the ratio R as a new observable to estimate the significant wave height is detailed. In the second part, data from the TIGRIS experiment is used in order to relate the R with the significant wave height, comparing its performance with the performance obtained with the coherence time.

The MIR (Microwave Interferometric Reflectometer) is a new dual frequency Global Navigation Satellite Systems Reflectometry (GNSS-R) instrument that implements conventional and interferometric reflectometry, both with GPS and Galileo signals. This paper presents the instrument development progress, mainly in the control part, where a methodology to determine how much the platform attitude will affect the beam steering and a satellite selection algorithm have been developed.

The Dome-C region, in the East Antarctic Plateau, has been used for calibration/validation of satellite microwave radiometers since the 1970's. However, its use as an independent external target has been recently questioned due to some spatial inhomogeneities found in L-band airborne and satellite observations. This work evidences the influence of the Antarctic ice thickness spatial variations on the measured SMOS and Aquarius brightness temperatures (TB). The possible effects of subglacial water and bedrock on the acquired radiometric signals have also been analyzed. A 3-months no-daylight period during the Austral winter has been selected. Four transects over East Antarctica have been defined to study the spatial variations. A good agreement between SMOS and Aquarius TBchanges and ice thickness variations over the whole Antarctica has been observed, obtaining linear correlations of 0.6-0.7 and slopes of 8.6-9.5 K/km. The subglacial lakes may affect the vertical physical temperature profile and/or the dielectric properties of the ice layers above. As expected, the subglacial bedrock is not contributing to the measured TB, since the maximum estimated L-band penetration depth is ~1-1.5 km.

In the SMOS measurements, the negative effect of Radio-Frequency Interference (RFI) is clearly present, deteriorating the Brightness Temperature (BT) images. RFI sources have to be monitored and accurately geolocated in order to improve the quality of geophysical parameter retrieval from the SMOS BT image, and eventually develop algorithm to cancel them. This paper present a case study of SMOS RFI source detection and geolocation based on MUSIC (MUltiple SIgnal Classification) algorithm. Thanks to the high angular/spatial resolution of MUSIC algorithm, the RFI sources densely populated in East Asia can be separately geolocated, which is difficult using SMOS standard brightness temperature images.

This work shows a state-of-the-art GNSS reflectometry end-to-end performance simulator, selected for the ESA's GNSS rEflectometry, Radio Occultation and Scatterometry onboard International Space Station (GEROS-ISS). For the efficient development process, the PAU/PARIS End-to-end Performance Simulator (P2EPS) are used as main building blocks for the GEROS-Simulator. To accommodate the GEROS specific situation of platform and instrument, P2EPS is modified. Additionally, several considerations in spaceborne GNSS-R techniques are newly updated in GEROS-SIM, improving the performance in terms of accuracy and completeness. GEROS-SIM will be delivered as a web application, i.e., executable via web browser connecting GEROS-SIM webpage hosted by UPC-BarcelonaTech.

Radio-Frequency Interference (RFI) is a growing problem specially for those systems that work with low power signals such as passive remote sensing instruments. Consequently, RFI mitigation techniques are currently under development. This works aims at evaluating back-end mitigation algorithms in terms of their probability of detection and mitigation performance. Results show that Wavelet Denoising (WD), and Multiresolution Fourier Transform (MFT) are the best techniques in most scenarios, specially for GNSS-based instruments.

New remote sensing techniques based on the analysis of the Earth's surface-reflected signal from the Global Navigation Satellite Systems (GNSS-Reflectometry, or GNSS-R in short) are emerging. Soil moisture and vegetation status are some of the potential parameters that could be also retrieved from these sources. However, the complex interactions between the soil-vegetation interface can lead to spurious effects on the reflected signal. In order to study these effects, an airborne campaign was developed in an experimental area in Spain in August, 2014. A new GNSS-R-based instrument was flown together with thermal and optical cameras mounted on a paramotor. Ground measurements of soil moisture were taken during the flight. Maps at very high spatial resolution of reflectivity, Land Surface Temperature (LST) and the Digital Surface Model (DSM) were jointly analyzed, together with the ground observations. The results showed an important influence of the topography (i.e., the local incidence angle) on the GNSS-R reflectivity, and promising patterns relating reflectivity with soil moisture and LST were found. However, owing the dry soil and weather conditions during the experiment, further tests are needed over different environment and climatic conditions.

In Global Navigation Satellite System Reflectometry (GNSS-R), power waveforms or Delay-Doppler Maps (DDM) are incoherently averaged to reduce the standard deviation of the fluctuations caused by the speckle and thermal noise. This letter proposes a simple and innovative processing concept based on the computation of the variance of the complex waveforms. By this approach, the coherent part of the signal is subtracted from the incoherent averaged waveform. It can be useful, for example, in scenarios where reflected signal is contaminated by the direct signal, as ground-based scenarios or in future experiments such as GEROS-ISS due to the nearby multipath of direct signals.

Radio-frequency interference (RFI) seriously affects the retrieval of geophysical parameters from the measurements of microwave radiometers. An accurate geolocation of the RFI is crucial to effectively switch off illegal transmitters. In this letter, a new RFI localization method is proposed to improve the achievable angular resolution by using beamforming and direction-of-arrival (DOA) estimation techniques. The proposed RFI localization techniques can be employed in synthetic aperture interferometric radiometers, such as the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. Two DOA estimation techniques are tested for the RFI localization: Capon and MUSIC. The feasibility of these methods is demonstrated with SMOS data. In the test results, the MUSIC beamforming shows a better performance of RFI localization than the SMOS Fourier imaging and the Capon, in terms of accuracy and resolution.

Global Navigation Satellite System-Reflectometry (GNSS-R) is a promising and innovative remote sensing technique. In this letter, an investigation on the polarimetric characteristics of GNSS-R signals is performed. The study is conducted by means of an ad hoc simulator based on a facet approach to approximate a 3-D time-domain generated sea surface. To best mimic a realistic configuration, the simulator uses both recorded GPS and Galileo signals as incident waves. The effect of the incoherent averaging in mitigating the speckle is shown. Finally, a relationship between the wind speed and the polarimetric ratio is analyzed.

Since 1993, when the European Space Agency (ESA) proposed the use of Global Navigation Satellite Systems reflected signals for sea mesoscale altimetry, a wide range of applications have appeared. This paper focuses on the retrieval of significant wave height (SWH) and the mean sea surface level (MSSL) from a ground-based experiment using the interference pattern technique (IPT). Two different observables on the IPT are analyzed: the oscillation frequency and the angle where coherency is lost. The point where coherency in the reflection process is lost can be related to the Rayleigh criterion for smooth surfaces and helps to determine the SWH. Spectral analysis on the interference pattern helps to determine the MSSL. A three-month field campaign was performed on the “Pont del Petroli” pier, Badalona, Spain, to see how the reflected GNSS signals were affected by coastal sea state and check previous assumptions. Results from this field experiment are shown, confirming that the SWH can be retrieved with accuracy of 6 cm and the MSSL with 4 cm. Estimations of both parameters are obtained every 30 min approximately.

Global Navigation Satellite Systems (GNSS)-Reflectometry (GNSS-R) has proved to be a useful technique for the estimation of Soil Moisture (SM). In the past 10 years, different techniques such as the Interference Pattern Technique (IPT), the Interferometric Complex Field (ICF) or power measurements of direct and reflected GNSS signals have been used. This work presents a reflectometer concept that can be used for air-borne, Unmanned Aerial Vehicle (UAV), car-borne, and ground-based measurements. It also presents the hardware implementation and the data acquisition scheme. An algorithm for the estimation of the reflectivity of the surface under observation has been developed and compared to concurrent radiometric measurements. Initial results from an airborne field experiment have shown a good correlation between both data sets.

Since 2003 several field experiments using Global Navigation Satellite Systems (GNSS)-Reflectometry (GNSS-R) have demonstrated the feasibility of retrieving Soil Moisture (SM) from GNSS-R observations. Different techniques such as the power difference between direct and reflected signals, the Signal to Noise Ratio (SNR)-analysis method, the Interference Pattern Technique (IPT) or the Interferometric Complex Field (ICF) have been used. The conventional IPT was first proposed in 2008, and consisted on forcing a single multi-path using a vertically polarized GNSS antenna with a rotationally symmetric pattern pointing to the horizon. In this work the conventional IPT is extended to dual-polarization, horizontal (H-pol) and vertical (V-pol), in attempt to increase the accuracy in the SM retrievals. In this case, the Brewster angle is estimated from the phase difference between the Hand V-Pol interference patterns. The use of dual-polarization measurements is not sensitive to surface roughness and it is more precise in the determination of the Brewster angle position. Results from a field experiment at the Yanco site, New South Wales, Australia, are shown to demonstrate the concepts proposed in this work.

Reflectometry using Global Navigation Satellite System's signals of opportunity (GNSS-R) was originally conceived in the early 90s for mesoscale altimetry, and since then, many studies have shown its applicability to other remote sensing applications. In 2002, the Remote Sensing Lab at the Universitat Politècnica de Catalunya started working in Global Navigation Satellite Reflectometry (GNSS-R) to perform a more accurate correction of the sea state when retrieving sea surface salinity from L-band microwave radiometer data (e.g. SMOS). During these 12 years, the lab has developed ground-based, airborne, balloon, and spaceborne GNSS-R payloads for a number of different applications. This paper provides an overview of the families of instruments, and the design choices made.

The Synthetic Aperture Interferometric Radiometer (SAIR) Performance Simulator (SAIRPS) is a very versatile software tool developed by Deimos Engenharia (Portugal) and the Universitat Politècnica de Catalunya-Barcelona Tech (Spain) for the European Space Agency to simulate and compute the figures of merit of the performance of arbitrary SAIRs, using the OpenSF framework. The description and developments of this simulator were previously reported in the open literature and previous IGARSS. This paper summarizes the developments in this third year of the project, including the end-to-end validation tests, and the radiative transfer module that has been added to improve the versatility of the tool.

Universitat Politècnica de Catalunya (UPC) - BarcelonaTech Remote Sensing Laboratory focus on the development of breakthrough concepts. The3Cat-2 mission is on the synergy of GNSS reflectometry and the CubeSat concept. Scientifically valuable mission data will improve our understanding on the Earth's environment. In particular, ocean currents need further investigation because of the spatio-temporal evolution of the mesoscale phenomena. The spacecraft development work includes two stratospheric flights in the frame of the REXUS/BEXUS programme coordinated by the European Space Agency (ESA). Coherent and incoherent scattering of Earth-reflected GNSS signals as sensed by the P(Y) & C/A ReflectOmeter (PYCARO) payload will be evaluated using the main state-of-the art Global Navigation Satellite Systems Reflectometry (GNSS-R) methodologies. Future spaceborne activities could take advantage of it, including the GNSS REflectometry, Radio Occultation and Scatterometry on-board International Space Station (GEROS-ISS) experiment.

Forests partially reduce climate change impact but, at the same time, this climate forcing threatens forest's health. In recent decades, droughts are becoming more frequent and intense implying an increase of forest decline episodes and forest fires. In this context, global and frequent soil moisture observations from the ESA's SMOS mission could be useful in controlling forest exposure to decline and fires. In this paper, SMOS observations and several climate variables are analyzed together with decline and fire inventories, to study the effect of soil moisture on forest decline during an important drought on summer 2012, and on forest fires in the period 2010-2013. Results show that SMOS-derived soil moisture is a complementary variable in forest decline models. Some of the studied tree species exhibit high probability of decline occurrence under dry conditions. First results showed burned areas to be drier than unburned ones previous to the fire occurrences.

This paper presents the computed electromagnetic (EM) bias predicted for GNSS-R altimetry systems. First, a synthetic time-evolving sea surface is generated satisfying an ocean surface height spectrum. Then, a direct GNSS signal recorded by an up-looking antenna is used as the GNSS signal “illuminating” the synthetic sea surface, and the scattered waves are simulated using the Geometric Optics (GO) method. Finally, using the fundamental definition of the EM bias, it is estimated and compared to the limited existing analytical methods.

Typhoon motoring using Global Navigation Satellite System (GNSS) signals is a new application of the GNSS-R technique, with increasing interest in the last years. Examples of that can be the CYGNSS (Cyclone Global Navigation Satellite System) spaceborne mission proposed by NASA, or the TIGRIS (Typhoon Investigation using GNSS-R Interferometric Signals) experiment, conducted in the framework of ESA-China cooperation. This paper focuses on the TIGRIS experiment, presenting the preliminary results obtained using the interferometric GNSS-R (iGNSS-R) technique.

The Microwave Interferometric Reflectometer (MIR) is a new airborne reflectometer being developed at Catalonia Polytechnic University (UPC) that uses Global Navigation Satellite Systems (GNSS) to retrieve mainly altitude over sea, but also soil, ice and sea parameters such as soil moisture, vegetation water content, sea surface roughness and wind speed. It uses two frequency bands and two different beams per band in order to improve the sensor capabilities. These beams point to the estimated specular reflection area and to the desired satellites considering the position of the platform.

Surface Soil Moisture (SSM) affects the soil surface energy balance and thus affects the Land Surface Temperature (LST), and viceversa. Currently, LST and SSM are remotely sensed using TIR sensors and L-band radiometers, respectively. The NASA's Terra/Aqua missions provide full coverage of LST measurements under clear sky conditions using MODIS. The ESA's SMOS mission is the first satellite providing frequent SSM and ocean salinity observations at global scale. In this paper, a sensitivity study about the relationship of the LST and SSM is performed using in-situ measurements from the REMEDHUS network and spaceborne observations from MODIS and SMOS. Results show that the correlation between SSM and LST (both in-situ and remotely sensed) is highest using the daily maximum LST. This could help improving SSM algorithms and deriving new SSM products at higher resolution from the synergy of microwave and TIR observations.

Global Navigation Satellite System Reflectometry (GNSS-R) has been emerged as a new opportunity for satellite remote sensing. From the bistatic measurement of GNSS-R, various geophysical parameters can be retrieved. This work shows the possibility of GNSS-R to monitoring the tropical cyclone. For case study, the GEROS mission is considered, which plans to install GNSS-R instrument on International Space Station. From the simulated DDM, the scattering coefficient maps are restored, and the eye of cyclone is detected.

This paper describes the Microwave Interferometric Reflectometer (MIR) instrument back-end for GNSS reflectometry (GNSS-R) applications. MIR uses the interferometric (iGNSS-R) and conventional (cGNSS-R) techniques for altimetry and scatterometric observations. MIR is intended for low altitude aircrafts, and can track up to 4 pairs of satellite/reflection points simultaneously. The back-end computes real-time 2D cross-correlations in the delay/Doppler plane and performs incoherent averaging after proper delay and Doppler tracking. Initial results from ground measurements are shown.

RFI (Radio Frequency Interference) signals are a threat for GNSS-R (Global Navigation Satellite Systems - Reflectometry) due to the very low power of the navigation signals. Interference signals coming from computers or high-speed buses may affect GNSS-R measurements introducing a bias or even corrupting them completely. These RFI may be in-band or near-band and can desensitize GNSS and GNSS-R receivers. A number of techniques are currently under development to mitigate the effect of RFI signals.

Hyperspectral indices from the Compact Airborne Spectrographic Imager (CASI 550), together with land surface temperatures from the Thermal Airborne Spectrographic Imager (TASI 600) and brightness temperatures from a L-band radiometer were combined in a semi-empirical model to obtain soil moisture at very high spatial resolution (3.5 m). The airborne imagery was acquired in an experiment performed over the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in 2012, including intensive field measurements. The feasibility of the hyperspectral optical bands from CASI acting as soil moisture proxy is tested through the cross-correlation between every possible two-CASI bands combination and in situ soil moisture. Next, the potential of combining the selected hyperspectral indices with thermal and microwave observations for soil moisture mapping is explored, synergistically with thermal and microwave observations, for obtaining soil moisture.

Global Navigation Satellite Systems - Reflectometry (GNSS-R) represents an innovative tool for remote sensing. In this paper a facet-based simulator is presented to investigate the polarimetric characteristics of GNSS L-band signals reflected off sea surface. The aim of the proposed technique is to improve the classical simulation technique based on the Zavorotny-Voronovich (Z-V) approach. Therefore, the scattered power and the Delay-Doppler Map are obtained using a time-evolving random 3D sea surface and the Kirchhoff Approximation (KA) of the Physical Optics (PO) to describe the scattering mechanisms. Finally the polarization ratio (PR) between co- and cross-polar scattered signal is fully investigated against different sea surface conditions, i.e. wind speed.

Soil moisture (SM) is a key parameter in the climate studies at a global scale and a very important parameter in applications such as precision agriculture at a local scale. The Global Navigation Satellite Systems Interference Pattern Technique (IPT) has proven to be a useful technique for the determination of SM, based on observations at vertical polarization (V-Pol) due to the Brewster angle. The IPT can be applied at both V-Pol and horizontal polarization (H-Pol) at the same time, observing the Brewster angle only at V-Pol. This letter presents a measurement technique based on tracking the phase difference between V-Pol and H-Pol interference patterns to improve the accuracy of the Brewster angle determination and, consequently, that of the SM retrievals. This technique benefits from the different phase behavior of the reflection coefficients between H-Pol and V-Pol in the angular observation range. To be sensitive to the phase difference, the Rayleigh criterion for smooth surfaces must be accomplished. This technique is not sensitive to topography as it is intrinsically corrected. Experimental results are presented to validate the proposed algorithm.

This work focuses on the relationship between real and synthetic aperture radiometers giving a general and common mathematical framework for both of them. It will be demonstrated that a real aperture radiometer array can be understood as a synthetic aperture one, with a high level of redundancy. Therefore, all the recent results from synthetic aperture radiometry can be translated to real aperture radiometer arrays. This fact can be used to create beamforming arrays in a new way. To do this, the beamforming array equation will be written in terms of cross-correlations between all pair of antenna signals (visibility samples). By properly combining the visibility samples, a new type of digital beamforming radiometer can be implemented, which will benefit from the high calibration accuracy achieved in synthetic aperture radiometers (e.g., The Y-shaped MIRAS radiometer used in the Soil Moisture and Ocean Salinity mission of the European Space Agency) and the flexibility to electronically steer the beam.

This paper revises the precision of altimetric measurements made with signals of the Global Navigation Satellite Systems (GNSS) reflected (GNSS-R) off the sea surface. In particular, we investigate the performance of two different GNSS-R techniques, referred to here as the clean-replica and interferometric approaches. The former has been used in GNSS-R campaigns since the late 1990s, while the latter has only been tested once, in 2010, from an 18-m-high bridge in static conditions and estuary waters. In 2011, we conducted an airborne experiment over the Baltic Sea at 3-km altitude to test the interferometric concept in dynamic and rougher conditions. The campaign also flew a clean-replica GNSS-R instrument with the purpose of comparing both approaches. We have analyzed with detail the data sets to extract and validate models of the noise present in both techniques. After predicting the noise models and verifying these with aircraft data, we used them to obtain the precision of altimetric measurements and to extrapolate the performance analysis to spaceborne scenarios. The main conclusions are that the suggested noise model agrees with measured data and that the GNSS-R interferometric technique is at least two times better in precision than a technique based on using a clean replica of the publicly available GPS code. This represents a factor of at least four times finer along-track resolution. A precision of 22 cm in 65-km along-track averaging should be achievable using near-nadir interferometric GNSS-R observations from a low earth orbiter.

Reflectometry of signals of opportunity such as those emitted by a global navigation satellite system, known as GNSS-R, has been developed over the past years as a technique with great potential for ocean scatterometry, among other applications. Different approaches have been proposed to use GNSS-R for remote sensing of ocean surface roughness. One of them is based on deriving some descriptor/metric from the measured delay-Doppler map (DDM) and directly relating it to a geophysical property of the scattering surface. For instance, different descriptors have been proposed in the literature to measure the DDM spreading caused by the increase in ocean surface mean square slopes due to surface winds. In this paper, a new descriptor based on the DDM is proposed for wind direction retrieval. This descriptor, designated as the skewness angle φ1,skew, measures the asymmetry in the DDM power distribution along the Doppler frequency axis, and it was modeled as a function of wind direction by means of a simulation study. Then, that model was validated using real GNSS-R data from an airborne experiment. After validation, the DDM skewness model was successfully used for wind direction retrieval, with a resulting rms error on the order of 20 °.

Earth-reflected Global Navigation Satellite System (GNSS) have become an attractive tool to be employed in low-Earth-orbit spaceborne missions, for applications such as ocean mesoscale altimetry and scatterometry. For such techniques, several on-board processing strategies have been proposed, either based on the correlation with on-board generated signal or based on ‘blind’ interferometric processing, which involves the correlation between received direct and reflected signals. This paper will provide a comparison of these two proposed GNSS-R processing techniques, highlighting the possible achieved performance for a typical spaceborne scenario. The performance comparison will be carried out by analyzing the widely used Cramer-Rao Bound, which takes into account the full statistical properties of the reflected signals and provides accurate comparison.

This work presents a novel technique for the determination of soil moisture obtaining 2-D Soil Moisture (SM) information with a single instrument. Both the instrument and the retrieval algorithm used, which is based on inferring the reflection coefficient of the terrain by direct and forward scattering polarimetric measurements of Global Navigation Satellite Systems (GNSS) Signals, are briefly described. Some preliminary results of a field campaign performed on La Pobla de Mafumet (Tarragona, Spain) are presented. This instrument and retrieval algorithm can be used for different applications, such as, an input for the irrigation algorithm (Smart-Irrigation) or forest fire prevention among others.

Reflectometry using Global Navigation Satellite System's signals of opportunity (GNSS-R) was originally conceived for mesoscale altimetry, but it can be applied to sea state determination (i.e. wind speed), among many other applications. The feasibility to create scattering coefficient and wind speed maps over the ocean using the Delay-Doppler Maps (DDMs) has also been analyzed. In this work a case study is presented to show the evolution of some features of the DDM (shape and peak) as seen from a PAU-like GNSS-R instrument aboard the International Space Station (ISS), while overpassing the Sandy hurricane on October 27th, 2012 in front of the coast of Florida.

ESA's SMOS mission has been a cornerstone in passive Earth Observation, since it has provided for the first time ever with global and frequent observations of Soil Moisture and Ocean Salinity (where the mission acronym comes from). SMOS' single payload is MIRAS: the Microwave Imaging Radiometers by Aperture Synthesis in two dimensions. After SMOS' success, today, a number of instruments are planned or under study: the GeoStar instrument is the baseline payload for the Precipitation and All-weather Temperature and Humidity (PATH) mission from NASA (USA), the Geostationary Atmospheric Sounder (GAS) instrument is under study for post-MSG operational satellites observations (Europe), and the Geostationary Interferometric Microwave Sounder (GIMS) instrument from NSSC-CAS (China). The study of the instrument performance in terms of angular resolution and radiometric performance (radiometric sensitivity and accuracy), and the optimization of this new type of instruments is a complex task that requires dedicated ad-hoc tools. In this work the SAIRPS (Synthetic Aperture Interferometric Radiometer Performance Simulator) is presented. This complex simulator allows to analyze arbitrary receiver topologies, arbitrary array geometries, and includes noise injection calibration algorithms, and new external robust calibration algorithms, and image reconstruction algorithms that allow to evaluate the performance of almost any instrument. At present, the full simulator has been coded in Matlab, translated into C++, and integrated in OpenSF. This work summarizes the key aspects of the architecture of this simulator, completing the presentation made in IGARSS 2012, and presents a few examples of the simulation results using different instrument configurations.

Reflectometry using Global Navigation Satellite System's signals of opportunity (GNSS-R) was originally conceived for mesoscale altimetry [1], although its applicability to sea state determination, soil moisture, vegetation, snow monitoring... has already been demonstrated. In December 2012 the Phase A studies of ESA's PAssive Reflectometry and Interferometry System In-orbit Demonstration (PARIS IoD) mission ended. In conventional GNSS-R the GNSS signals scattered over the Earth's surface are cross-correlated with a locally generated replica of the transmitted signal shifted in frequency (Δfd) and in delay (Δτ). PARIS is called an interferometric GNSS-R (iGNSS-R) system because the direct and the scattered signals are cross-correlated in order to use the whole signal's bandwidth, and improve the altimetric precision, despite the large bandwidth signals are not publicly available. This work presents a methodology to evaluate the performance of iGNSS-R altimeters. It is then applied to a PARIS IoD-like case, in which the receivers' bandwidths have been optimized in terms of altimetric resolution.

Soil moisture is required to improve meteorological and climate predictions. Global soil moisture maps are nowadays produced daily from SMOS satellite data, with a basic spatial resolution of ∼50 km. Recently, using data fusion techniques between SMOS and MODIS data, an operational service has been implemented at the SMOS-Barcelona Expert Center to downscale SMOS data down to 1 km over the Iberian peninsula [1]. However, despite SMOS operates in the passive microwave “protected” band from 1400 to 1427 MHz, radio frequency interference may degrade the quality of the soil moisture (and sea salinity) retrievals or even prevent them [2]. Signals of opportunity transmitted from Global Navigation Satellite Systems (GNSS) can be used for soil moisture, vegetation, snow, water level… monitoring after reflection (GNSS-R) on the Earth's surface. In principle, even though these signals can also be jammed, their structure and the way they are processed, makes them more robust in front of radio-frequency interference, while at the same time -in principle- can achieve also a better spatial resolution. In this work, the few different GNSS-R techniques are first revised, including their pros and cons. Then a few applications are revised, with special emphasis -but not exclusively- in those in which the UPC Remote Sensing Lab has been working.

This study presents the feasibility of sea waves monitoring using the P(Y) & C/A ReflectOmeter (PYCARO) instrument which employs a novel Global Navigation Satellite Sys tems Reflectometry (GNSS-R) correlation technique. To that end, an experiment over the wave channel surface of the civil engineering school of UPC (Barcelona, Spain) is performed using a signal generator to simulate the GPS satellites at different elevation angles. Results are compared to these derived from a wave probe sensor. The experimental evidence of sea waves determination opens a new scientific application of this remote sensing methodology.

3Cat-2 is a 6 U CubeSat mission designed to perform ocean altimetry by means of Global Navigation Satellite Systems Reflectometry (GNSS-R). In this paper3Cat-2 mission is presented. The main payload is the novel dual-band altimeter P(Y) & C/A ReflectOmeter (PYCARO) which has already been tested from a pier and from an aircraft over the Mediterranean Sea. In addition, an experiment from the DLR/SNSB Balloon-borne EXperiments for University Students (BEXUS) stratospheric balloon in North Sweden will be performed on October 2013 for the ultimate optimization of the payload parameters.

A powerful real time Radio-frequency interference (RFI) mitigation back-end system is presented in this work. The interfering signals are estimated by using the wavelet transform to utilize its effective denoising capability. The estimated RFI signals are then subtracted from the total received signal, and ultimately the RFI-mitigated signal is obtained. Here, the hardware implementation and the system tests are demonstrated, showing sinusoid, chirp, and PRN interference mitigation under different Interference to Noise Ratios (INRs).

The use of Global Navigation Satellite Signals (GNSS) in reflectometric applications (GNSS-R) is highly extended in remote sensing applications, such as sensing sea state, soil moisture or ice layer characterization. There are two main techniques in this field: the conventional GNSS-R, that achieves high SNRs by cross-correlating the received signal with a replica of the transmitted one but, with a low resolution due to narrow bandwidth of the signals, and the interferometric GNSS-R, which achieves better resolutions but with lower SNRs since it directly correlates the direct and reflected signals. The Microwave Interferometric Reflectometer (MIR) is a new sensor that will use high-directivity, multiband and steerable arrays to achieve higher SNRs in the interferometric technique. Additionally, it will also apply the conventional technique. All these capabilities will be applied simultaneously to two beams at each band (L1 and L5 / E1 and E5). This work describes this concept instrument and the first prototypes will be presented at the conference.

The spectral window at L-band (1.400 - 1.427 GHz) is reserved for passive microwave remote sensing. This band is well-suited to retrieve soil moisture and ocean salinity due to emissivity of soil and seawater decreases with moisture and salinity, respectively, affecting microwave radiation of the Earth's surface. Nowadays, there are two space missions devoted to Earth observation with L-band radiometers on-board: the SMOS mission from the ESA and the Aquarius/SAC-D mission from the NASA and CONAE. Both missions are providing the first TB measurements of the Earth's surface at 1.413 GHz. Thus, it is a great opportunity to compare SMOS and Aquarius TBs and verify the continuity and consistency of the data. This inter-comparison is a key requirement needed to use data of both radiometers for meteorological, hydrological and climatological studies on a long term.

PAU/PARIS End-to-end Performance Simulator (P2EPS) has been developed for studying and design tools for GNSS-R space mission. For the purpose of accuracy and various scenario, P2EPS has recently improved in many features. Especially, the wide range of the simulation inputs enables to simulate the upcomming GNSS-R space missions. This paper presents the recent improvement of the P2EPS.

In the last years, the GPS signals have been validated as opportunity signals in a wide range of reflectometry-based remote sensing applications for sea, land or ice scenarios. Recently, the first Galileo satellites have started transmitting the new signal E1, which shares the band with the GPS L1 signal. This work compares the performance of both signals in space and airborne scenarios for sea state and altimetry determination.

An innovative downscaling approach to obtain fine-scale soil moisture estimates from 40 km SMOS observations has been developed. It optimally blends SMOS multi-angular and full-polarimetric information with MODIS visible/data into high resolution soil moisture maps. The core of the algorithm is a model that linksmicrowave/optical sensitivity to soilmoisture and linearly relates the two instruments across spatial scales. This algorithm has been implemented at SMOS-BEC facilities and near real-time maps of disaggregated soil moisture over the Iberian Peninsula are being distributed. In this work, the temporal and spatial variability of these maps is evaluated through comparison with ground-basedmesurements acquired at the REMEDHUS soil moisture network, in the central part of the Duero basin, Spain. Results from a two-year time-series comparison show that downscaled soil moisture maps compare well with in situ data and nicely reproduce soil moisture dynamics at a 1 km spatial scale.

Tracking, in radar altimetry, is the positioning of the waveforms in the correlation window. This letter presents a tracking strategy in spaceborne altimetry using global navigation satellite system reflectometry. First, the tracking procedure is illustrated, and the tracking parameters are discussed one by one: the determination of the correlation window, the accuracy of specular delay guess, and the tracking-refresh period. Based on the results, the proposed European Space Agency Passive Reflectometry and Interferometry System In-Orbit Demonstrator tracking case study is examined.

This work has been conducted in the framework of several projects devoted to assess the performance of the Soil Moisture and Ocean Salinity (SMOS) mission full-pol measurement mode. Since its launch in November 2009, SMOS is producing dual-polarization brightness temperature synthesized images that are yielding a high scientific return. However, these images are affected by a non-negligible spatial amplitude error, the so-called spatial bias (SB), that degrades geophysical parameter retrieval. This effect is particularly detrimental in SMOS polarimetric images where spatial bias is masking the polarimetric physical signature to a large extend. This paper presents a method to mitigate SMOS spatial bias by taking into account the co- and cross-polar antenna patterns in the image reconstruction algorithm through the, so called, full-pol G-matrix (FPG). The method is validated by producing spatial bias maps out of the comparison between SMOS full-pol images and an accurate polarimetric brightness temperature model of the ocean. This model has been provided to SMOS ESLs (Expert Support Laboratories) by LOCEAN (Laboratoire d'Océanographie et du Climat, France) as a test bench to validate and improve SMOS Level 1 (L1) data. Finally, a radiometric performance summary table comparing spatial bias and radiometric sensitivity between this new FPG approach and SMOS current co-polar G-matrix approach (CPG) is provided. This paper presents the best quality SMOS polarimetric images, which may lead a breakthrough in the science returns of the mission.

The 14 papers in this special issue cover a combination of software and hardware solutions to the Radio Frequency Interference (RFI) problem, detail the challenges in monitoring RFI, and attempt to quantify the impact that interference has on measurements.

Microwave radiometers are very sensitive passive sensors that measure the power of the thermal noise within a determined bandwidth. Therefore, any other signal present in the band modifies the value of the measured power, and the corresponding estimated antenna temperature, from which the geophysical parameters are retrieved. Due to the high sensitivity and accuracy required for these instruments, radio frequency interference (RFI) is becoming more and more a serious problem. On one hand, ground-based or global RFI surveys are helping to understand the occurrence and types of RFI sources. If RFI does not necessarily affect the whole bandwidth, or it is not present during the whole integration time, the application of either frequency blanking, time blanking or signal spectrogram techniques can be applied. However, it would be desirable to apply techniques to estimate the RFI signal so that it can be subtracted from the received signal itself so that some useful measurements are still possible. Such a real-time system is currently being developed for RFI detection and mitigation. This work focuses however in the description and performance of a wavelet-based RFI-mitigation technique implemented in a FPGA hardware back-end. The interfering signal is estimated by using the powerful denoising capabilities of the wavelet transform, and it is then subtracted from the total received signal to obtain a RFI-mitigated noise signal.

Radio-frequency interference (RFI) is probably today's most serious limitation to the accurate retrieval of geophysical parameters from microwave radiometric measurements. Strong RFI inducing a change in the detected power larger than the natural variability is simple to detect. Moderate or weak RFI can be masked by the natural variability of the measurements, passing undetected and corrupting them. A number of techniques have been devised in the past years to detect and, eventually, mitigate RFI present in microwave radiometry measurements: 1) time domain; 2) frequency domain; 3) spectrogram techniques looking for anomalously high power peaks; 4) statistical techniques testing the hypothesis of Gaussianity of the received signal; 5) polarimetric techniques looking for anomalous signatures in the third and fourth Stokes parameters; or 6) wavelet techniques to estimate the RFI signal and cancel it (if any). In this paper, the first four techniques are evaluated with real data gathered with a multifrequency microwave radiometer. It will be shown how spectrogram techniques can detect RFI signals concentrated in narrow frequency bands and/or time intervals that may pass undetected with time-domain and/or frequency-domain techniques alone or with statistical methods. A combined approach is proposed to take advantage of the best performance of each technique. On one side, for strong localized RFI, the approach is spectrogram blanking or, if it is too demanding in terms of computational resources, simple time- and frequency-domain blanking. On the other side, for weak RFI, the approach is the Kurtosis statistical test, which exhibits the best performance among the ten normality tests evaluated, in conjunction with the Anderson–Darling test to detect potential RFI in the blind spots of the Kurtosis test.

Global navigation satellite system (GNSS) reflectometry has emerged recently as a promising remote sensing tool to retrieve various geophysical parameters of the Earth's surface. GNSS-reflected signals, after being received and processed by the airborne or spaceborne receiver, are available as delay correlation waveforms or as delay-Doppler maps (DDMs). In the case of a rough ocean surface, those characteristics can be related to the rms of the L-band limited slopes of the surface waves and, from there, to the surface wind speed. The raw GNSS-reflected signal can be either processed in real time by the receiver or recorded and stored on board and postprocessed in a laboratory. The latter approach leveraging a software receiver allows more flexibility while processing the raw data. This work analyzes DDMs obtained as a result of processing of the data collected by the Global Positioning System (GPS) data logger/software receiver on board the National Oceanic and Atmospheric Administration Gulfstream-IV jet aircraft. Thereafter, the DDMs were used to retrieve surface wind speed employing several different metrics that characterize the DDM extent in the Doppler frequency-delay domain. In contrast to previous works in which winds have been retrieved by fitting the theoretically modeled curves into measured correlation waveforms, here, we do not rely on any model for the determination. Instead, the approach is based on a linear regression between DDM observables and the wind speeds obtained in simultaneous GPS dropsonde measurements.

This work presents the flight model of a PAU version to be flown in INTA's (Instituto Nacional de Técnicas Aeroespaciales, Spanish Aerospace Center) MicroSat-1 and the tests performed. PAU is a hybrid GNSS-Reflectometer / Microwave Radiometer developed to assess the impact of the sea state on the L-band brightness temperatures and on the GNSS-R observables (the Delay Doppler Maps or DDMs). The DDMs can be used in the future to perform the sea state correction in the L-band radiometric measurements, and therefore, improve the quality of the sea surface salinity retrievals. This work also describes a sensitivity test, a Doppler test, and a preliminary field experiment to evaluate the performance.

Reflectometry using Global Navigation Satellite System's (GNSS) opportunity signals was originally conceived for altimetry [1]. However, when the GNSS signals scatter over the Earth's surface (or propagates through the atmosphere), the amplitude and polarization characteristics change, which can be used for many other remote sensing applications such as sea state, water level, soil moisture, vegetation height, snow depth monitoring, atmospheric profiling... ([2], [3] among many others). In [4] and [5], recent experimental results from an ESA-sponsored airborne campaign carried out in June and November 2011 in the Baltic sea are presented using conventional and interferometric GNSS-R, respectively. In this work the properties of the measured interferometric GNSS-R waveforms are studied, and the wavelet transform is applied to compress and denoise the waveforms. This is of interest for future missions, such as ESA's proposal of PARIS IoD, to reduce the amount of data to be stored on board and downloaded to Earth.

ESA's SMOS mission has demonstrated that Synthetic Aperture Interferometric Radiometers can be a useful tool for Earth observation. Today, the NASA's GeoStar instrument (USA), ESA's GAS instrument (Europe), or CSSAR's GIMS instrument (China) are just three examples of planned geostationary millimeter-wave sounders using synthetic aperture interferometric radiometry for continuous atmospheric monitoring with a much improved spatial resolution. The study of the performance in terms of angular resolution and radiometric performance, and the optimization of this new type of instruments is a complex task that requires dedicated ad-hoc simulators. The SAIPRS (Synthetic Aperture Interferometric Radiometer Performance Simulator) is an ESA project aiming at the development of a generic simulator in terms of receiver architecture and array configuration, including moving antennas (and eventually changing polarization reference frames). This work summarizes the key aspects of the architecture of this simulator.

The ultimate accuracy and precision of conventional1 and interferometric2Global Positioning Satellite System Rerflectometry (GNSS-R) techniques for mesoscale ocean altimetry are still a matter of debate in the scientific community. The results obtained depend on the techniques used to identify the point of the specular delay in the waveform, and to perform the different delay corrections to derive the geometric delay. Also, the geometric model assumed for the scenario determines the accuracy of the altimeter range. This work presents the results of two ESA-sponsored airborne experiments using conventional GNSS-R showing subdecimeter altimetric precision with the Global Positioning System (GPS) L1 C/A code only. The Relative Mean Dynamic Topography (RMDT) obtained in both experiments is compared with results derived from traditional radar altimetry provided by Jason 2. The Root Mean Square (RMS) of the RMDT difference between both measurement systems is 48 cm for the first flight, and 198 cm for the second flight. Additionally, results from the second flight experiment show the capability of the proposed technique to retrieve sea slope measurements by superposing the ground track with EM96 geoid undulations.

The PARIS (Passive Reflectometry and Interferometry System) in orbit demonstrator is a novel altimeter concept recently proposed by the European Space Agency. PARIS has the primary objective of observing ocean mesoscale features using Global Navigation Satellite Systems (GNSS) reflected signals, with an expected sea surface height estimation accuracy of about 20 cm, and with a very high simultaneous spatial-temporal sampling of the ocean. To date, the altimetry performance has been preliminary predicted by means of simple analytical methods, which rely on a number of assumptions on the estimators, not predicting the optimum performance. This paper will present a prediction of the height precision based on the statistical analysis of the well-known Cramer-Rao estimation bound.

Radio-frequency interference (RFI) is a serious problem in microwave radiometry. In order to process the received data in real time, a powerful back-end for RFI detection and mitigation system must be implemented. This system includes many techniques against RFI, like time, frequency domain and spectrogram analysis. A wavelet-based RFI-mitigation technique is implemented in hardware. The interfering signal is estimated by using the powerful denoising capabilities of the wavelet transform. Then, this estimate of the RFI signal is subtracted from the total received signal to obtain a RFI-mitigated noise signal. Statistical analysis helps to validate the mentioned methods.

Microwave radiometers are very accurate passive sensors that have been successfully used in Earth remote sensing during the last decades. Microwave radiometers measure thermal noise, therefore any other signal (radio-frequency interference or RFI) present in the band modifies the value of the measured power, and the corresponding estimated antenna temperature, from which the geophysical parameters will be retrieved. An on-going RFI survey shows how corrupted is the spectrum “protected” L-band in the city of Barcelona. This type of studies should help to validate or discard the measurements made there, and in other locations, and also to take further actions against RFI. Detection and mitigation techniques are used to validate results.

This paper introduces UPCSat-1, the first project of the Universitat Politecnica de Catalunya to build and launch a pico-satellite. Its main scope is to develop, construct, assembly, test and launch into a Low Earth Orbit a CubeSat that will carry a simple optical payload and some technological demonstrators. On one hand, this is mainly an educational project in which the development of some of the subsystems is carried out by Master Thesis students. On the other hand, the satellite becomes an optimum platform to perform small scientific experiments, and to demonstrate some of the new technologies that it incorporates.

Earth-reflected Global Navigation Satellite System (GNSS) signals have become an attractive tool for remote sensing. Recently it has been proposed as an alternative to the conventional altimetry to estimate the surface height However GNSS-R altimetry offers lower bandwidths and signals power compared to radar altimeters. This implies a poorer altimetry precision, accuracy and resolution per pulse.Also the altimetry precision will be impacted by the impact of the thermal, and speckle noise mainly. This paper gives a critical review of the cross-correlation waveform model.

The compensation of the Doppler frequency error is important to accurately estimate the sea height in spaceborne GNSS-R altimetry. This work presents the impact of the residual Doppler error on the altimetric performance. The GNSS-R altimetric observables (waveforms) are simulated with various observation parameters, and the degradation is analyzed vs. the Doppler errors. It is shown that the Doppler error causes a bias, a decrease of the waveform value, and consequently the altimetric performance degradation in accuracy and precision.

A downscaling algorithm for SMOS which combines MODIS Visible/Infrared data and SMOS horizontal brightness temperatures at 42.5° incidence angle into high-resolution soil moisture maps has been shown to nicely reproduce soil moisture dynamics at a 1 km spatial scale. The core of this algorithm is a linking model that depicts the synergy between SMOS and MODIS observations and their sensitivity to soil moisture. In this work, the impact of adding SMOS observations at horizontal and vertical polarizations and at multiple incidence angles to this linking model has been evaluated using 6 months of observations over the Murrumbidgee catchment, South-East Australia, and a robust alternative formulation is proposed. Results show that adding SMOS observations at multiple incidence angles and both polarizations the algorithm is more stable over time and its minimization error is reduced. By comparing with in situ data, a remarkable improvement of the linear regression between downscaled and in situ data is also observed (slope of 0.95).

This paper presents calibration and the radiometric performance of the Passive Advanced Unit Synthetic Aperture (PAU-SA) in order to verify the instrument's characterization.

We report preliminary results of an aircraft experiment aimed to proof the PARIS Interferometric Technique. The experiment was performed in the Gulf of Finland during a two hours flight. We installed a PARIS Interferometric Receiver together with a GOLD-RTR instrument to collect reflected C/A, P(Y) and M-code GPS signals. The collected data has been analyzed to produce altimetric observables with both techniques.

This paper describes the relationships found between remotely sensed soil moisture, in situ observed soil moisture, and spatial distribution of soil and climatic factors. For the comparison between remote and in situ soil moisture, soil moisture map series at high resolution, obtained by applying a downscaling approach that combines Soil Moisture and Ocean Salinity (SMOS) and MODIS imagery is extracted. The in situ soil moisture series are obtained from the Soil Moisture Measurement Stations Network (REMEDHUS) in Spain. For the spatial analysis, factors such as topography, precipitation, and land uses were mapped from the climatic and cartographic database of REMEDHUS. The comparison between downscaled and in situ soil moisture data resulted in correlation coefficient (R) values between 0.40 and 0.70, bias between -0.04 and 0.16 m3m-3, and root mean squared difference (RMSD) between 0.07 and 0.19 m3m-3. Regarding the spatial correlations between downscaled and spatial factors, no clear patterns were found when considering the topography (Topographic Wetness Index, TWI), and the land uses (Landsat classification). Nevertheless, the downscaled soil moisture was more related with the spatial distribution of precipitation (Antecedent Precipitation Index, API), with significant correlations varying between 0.24 and 0.55.

In recent years GNSS-R has been proposed for ocean remote sensing, both for altimetry and scatterometry. For the later, two main approaches are available: fitting the measurements with a model, or using some observable from the measurements (either the waveform or the delay-Doppler Map) to be directly linked to ocean surface's roughness. For ground-based and airborne experiments, several direct observables have been proposed to describe ocean surface's roughness, and they have been used assuming that the observation geometry had little impact on them. However, it may not be true for a spaceborne scenario, since the dynamics are higher. In this work an extensive simulation study is performed, and the first results on how the observation geometry does impact different GNSS-R direct observables is presented.

Global Navigation Satellite System Reflectometry (GNSS-R) has emerged recently as a promising remote sensing tool to retrieve various geophysical parameters of Earth's surface. GNSS-reflected signals, after being received and processed by the airborne or space-borne receiver, are available as delay correlation waveforms or as delay-Doppler maps. In the case of a rough ocean surface, those characteristics can be related to the RMS of L-band limited slopes of the surface waves, and from there to the surface wind speed. The raw GNSS-reflected signal can be processed either in real time by the receiver, or can be recorded and stored onboard and post-processed in a laboratory. The latter approach leveraging a software receiver allows more flexibility while processing the raw data. This work analyzes Delay Doppler Maps (DDM) obtained as a result of processing of the data collected by the GPS data logger/software receiver onboard the NOAA Gulfstream-IV jet aircraft. Thereafter, the DDMs were used to retrieve surface wind speed employing several different metrics that characterize the DDM extent in the Doppler frequency-delay domain. In contrast to previous works in which winds have been retrieved by fitting the theoretically modeled curves into measured correlation waveforms, here we do not rely on any model for the determination. Instead, the approach is based on a linear regression between DDMs observables and the wind speeds obtained in simultaneous GPS dropsonde measurements.

The Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2nd, 2009 aiming at providing sea surface salinity (SSS) estimates over the oceans with frequent temporal coverage. The detection and mitigation of residual instrumental systematic errors in the measured brightness temperatures are key steps prior to the SSS retrieval. For such purpose, the so-called ocean target transformation (OTT) technique is currently used in the SMOS operational SSS processor. In this paper, an assessment of the OTT is performed. It is found that, to compute a consistent and robust OTT, a large ensemble of measurements is required. Moreover, several effects are reported to significantly impact the OTT computation, namely, the apparent instrument (temporal) drift, forward model imperfections, auxiliary data (used by forward model) uncertainty and external error sources, such as galactic noise and Sun effects (among others). These effects have to be properly mitigated or filtered during the OTT computation, so as to successfully retrieve SSS from SMOS measurements.

Interpolation strategies for calibration of the Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency are tested and compared. Calibration strategy (how and how often) is critical in achieving the required performance of any instrument, but it is even more important in very complex instruments such as the new family of synthetic aperture interferometric radiometers and, in particular, in the Microwave Imaging Radiometer by Aperture Synthesis instrument aboard the SMOS mission. On one hand, frequent calibration reduces the available observation time. On the other hand, the calibration requirements for soil moisture applications are more relaxed than those for ocean salinity, so the intercalibration time requirements are very different. Since SMOS drifts are stationary, half-orbit information is available to perform different interpolation strategies. In this letter, these approaches are tested to estimate the calibration parameters between consecutive calibrations. The average root-mean-square phase error is then used to find the optimum interpolation strategy and intercalibration time. On the other side, in real-time instruments, the “future” calibration data are not available at the time of taking the measurements, and predictors are required to estimate the evolution of the calibration parameters from past data only. For these systems, the extended Kalman filter can be used. The intercalibration time in a real-time instrument is evaluated, and the requirements and performances are compared to offline instruments.

Global navigation satellite system reflectometry has already shown its potential to perform retrievals of a number of geophysical parameters, including soil moisture, vegetation height, etc. This letter focuses on the study of snow-covered soils using the interference pattern technique (IPT) with the Soil Moisture Interference-pattern GNSS Observations at L-band (SMIGOL) reflectometer, a ground-based instrument. Snow effects are analyzed, and an algorithm has been developed for this type of surfaces. From November 2010 to May 2011, a long-term field experiment was carried out at the Pyrenees (Val d'Aran, Lleida, Spain) to test the IPT and the retrieval algorithms on snow-covered soils.

Global Navigation Satellite Systems (GNSS) opportunity signals reflected at or near the Earth's surface have already shown their potential to perform retrievals of a number of geophysical parameters. Radio occultations using GNSS signals are also used for atmospheric sensing. This letter presents a GNSS technique to retrieve vegetation water content (VWC). This technique measures the received powers of the GPS signals in open sky and under the vegetation layer. From these two powers, the attenuation due to the vegetation is computed, which is related to the VWC. This letter presents the results obtained after deploying the instrument in a walnut-tree stand for 11 months.

In recent decades, atmospheric scientists have been interested in measuring thermodynamic variables such as tropospheric water vapor and temperature with increasing temporal and spatial resolution due to their importance on the climate modeling. For this purpose, microwave radiometers have been used to measure columnar integrated water vapor. The radiative transfer equation (RTE) has been used to retrieve the contributions of individual atmospheric layers, assuming a stratified atmosphere. In recent years, significant advances have been made toward retrieval of these parameters in 2-D, 3-D and 4-D (3-D + time) distributions. This work presents a new radiometric concept to directly measure the contribution of each pixel (avoiding the use of the RTE inversion) by using pencil-beam antennas and interferometric techniques. This new approach has the potential to improve the quality of the retrieved thermodynamic variables to meet the research goals of atmospheric science.

Global Navigation Satellite System (GNSS) Reflectometry was originally proposed for mesoscale altimetry, but today its feasibility for sea state, soil moisture, vegetation and snow height has already been demonstrated. This paper describes a GNSS-Reflectometer develop to perform the sea state correction in L-band radiometric measurements, and therefore, improve the quality of the sea surface salinity retrievals. The instrument is currently being developed as a secondary payload to be launched aboard INTA (Instituto Nacional de Técnicas Aerospaciales, Spanish Aerospace Center) MicroSat-1. The basic principles of operation and the instrument development status are presented.

Reflectometry using Global Navigation Satellite System's (GNSS) opportunity signals was originally conceived for altimetry [1], but it has been demonstrated today that it can be applied to sea state determination, soil moisture, vegetation, snow monitoring... So far, GPS signals scattered over the Earth's surface have been cross-correlated with a local replica of the transmitted signal (typically the only GPS available: the C/A code) shifted in frequency (Δfd) and in delay (τ). However, to achieve the full potential (bandwidth) of these signals the direct and reflected signals can be directly cross-correlated, but in this case, high gain antennas are required to increase the signal-to-noise ratio. Therefore these antennas must be steerable so as to track the direct satellite and the specular reflection point. This is the concept of the European Space Agency PARIS In Orbit Demonstrator (IoD) mission [2], a new concept that was demonstrated experimentally for the first time in 2010 [3]. This work deals with the analysis of the preliminary instrument error budget of this mission to address the critical design parameters.

Radio-frequency interference (RFI) is a serious problem in microwave radiometry. If the RFI produces a detectable increase in the power detected by the radiometer it can be flagged and data discarded, but when the RFI is comparable or smaller than the natural variability, it is difficult to detect, and the retrieved geophysical parameters become erroneous [1]. A wavelet-based RFI-mitigation technique is implemented in hardware. The interfering signal is estimated by using the powerful denoising capabilities of the wavelet transform. Then, this estimate of the RFI signal is subtracted from the total received signal to obtain a RFI-mitigated noise signal [2]. The system data output rate is the same as the data input rate, allowing continuous mode operation without saturating memory.

HUMID is a regional level airborne soil moisture mission. The envisioned product combines two key characteristics. On one hand, it provides robustness in presence of surface roughness and vegetation. On the other hand, it improves the current spatial resolution offered by L-band radiometers using a data fusion approach, where the data from an L-band radiometer developed by the Remote Sensing Lab at the Universitat Politecnica de Catalunya will be combined with data from a thermal and a VNIR hyperspectral sensors from the Institut Cartografic de Catalunya.

So far, GNSS-R studies on sea surface wind retrieval from Delay-Doppler Maps (DDM) have focused on the best fitting of the model to the measurements. This paper proposes a new approach to retrieve the sea surface wind using DDM deconvolution. The proposed method is based on the direct restoration of scattering coefficients from two measured DDMs. Using the restored scattering coefficient images, the sea surface slope is estimated, and eventually the wind speed is retrieved from the sea surface slope. This paper presents the performance of the proposed method with the simulation results, and the advantages of the method.

A downscaling algorithm to improve the spatial resolution of SMOS soil moisture estimates using higher resolution visible/infrared (VIS/IR) data is presented. The algorithm re- lates VIS/IR parameters such as the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (Ts) to SMOS soil moisture estimates using the "universal triangle" concept, and gracefully combines the high accuracy of SMOS radiometric observations with the high spatial resolution of VIS/IR data into an optimal soil moisture product. In preparation for the SMOS launch, the algorithm was tested using acquisitions of the UPC Airborne RadlomEter at L-band (ARIEL) over the REMEDHUS soil moisture monitoring network in Zamora, Spain, and LANDSAT imagery. After SMOS launch, the algorithm was applied to a set of SMOS images acquired during the commissioning phase over the OZnet soil moisture monitoring site, in South-Eastern Australia, and MODIS NDVI/Tsdata. Results from comparison with in situ soil moisture measurements showed that the soil moisture variability was effectively captured at 10 and 1 km spatial scales, without a significant degradation of the root mean square error. The potential application of this downscaling approach to generate high resolution soil moisture maps over the Iberian Peninsula in near-real time is now being as- sessed.

This paper presents the Passive Advanced Unit Synthetic Aperture instrument (PAU-SA), and some of the first experimental results: the use of pseudo-random noise signals for a complete baseline calibration and receivers' frequency response characterization, the use of a Kalman filter to better estimate the receivers' phase between internal calibrations, the imaging of an artificial point source at different angles and polarizations and, the first images of the GPS constellation satellites as they move in the instrument's field of view.

GNSS-R techniques are currently being studied to remotely sense a number of geophysical parameters over different types of surfaces [1-6]. The Interference Pattern Technique (IPT) is based on the measurement of the interference pattern of the GPS direct and reflected signals, after reflecting over the surface, as the GPS satellites move. This paper extends previous studies [7-11], in which water level was monitored [7] and land areas were observed retrieving soil moisture, topography and vegetation height for different kinds of crops (wheat, barley and maize) [8-10], to a snow- covered soils studies.

In the last decade GNSS-R techniques have started to be used for Earth remote sensing [1-7]. In previous studies, the Interference Pattern Technique (IPT) was used to observe land surfaces using a ground-based instrument: soil moisture over bare soils [8], surface's topography, soil moisture and vegetation height of wheat and barley fields [9], and a maize field (very dense and tall vegetation) [10], and snow height [11]. This work extends previous studies to the observation of the water level in a reservoir. Other GNSS-R techniques have addressed this problem previously, but it is shown that the extremely simple IPT outperforms them.

A new Radio Frequency Interference (RFI) detection algorithm has been implemented and it is presented in this work. The algorithm treats the radiometric signal's spectrogram (time-frequency representation) as an image, and applies image processing techniques such as 2D wavelet filtering to detect the presence of RFI. Several experimental measurements are presented to evaluate the performance of this algorithm.

Global Navigation Satellite Systems' signals refiectometry, originally envisioned for ocean altimetry, has become a promising remote sensing technique. Among other applications, it has been proposed for ocean scatterometry for sea state determination. Most of the proposed scatterometry techniques have as a product an average sea state descriptor over the whole observed ocean region. However, a Delay-Doppler Map (DDM) measurement is actually collecting the scattered power in different points of the observed surface so it has been recently proposed to invert the DDM measurements in order to obtain the scattering coefficient (σ0) distribution over the observed surface. In this paper, this technique is applied to the numerical simulation of oil slicks detection.

The radiometric resolution of a motion-induced synthetic aperture radiometer (MISAR) is analytically obtained from the standard deviation of a baseline response, an observation scenario, and the imaging method of the MISAR. The intrinsic long integration time given by the whole dwell time on moving platforms improves the radiometric resolution compared with the snapshot resolution of other nominal synthetic aperture radiometers. In addition, it is illustrated that the MISAR imaging holds the tradeoff relationship between the radiometric and spatial resolutions.

Global navigation satellite system reflectometry of signals is a promising technique to remotely sense a number of Earth's geophysical parameters, and it has been proposed for ocean monitoring applications such as mesoscale altimetry or sea state monitoring. So far, the following two main approaches have been considered to retrieve a sea state descriptor from measured delay waveforms or delay-Doppler (DD) maps (DDMs): 1) fitting the measurements to a model tuned with the desired parameter or 2) directly linking a property of the measurements with the parameter to be retrieved (e.g., volume of the normalized DDM). However, these approaches provide a single sea state descriptor related to the overall glistening zone where sea state conditions may not be homogeneous. In this letter, the relationship between the physical space and the DD domains is exploited, and a method to retrieve the bistatic scattering coefficient distribution over the observation ocean surface from measured DDMs is proposed.

A downscaling approach to improve the spatial resolution of Soil Moisture and Ocean Salinity (SMOS) soil moisture estimates with the use of higher resolution visible/infrared (VIS/IR) satellite data is presented. The algorithm is based on the so-called “universal triangle” concept that relates VIS/IR parameters, such as the Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (Ts), to the soil moisture status. It combines the accuracy of SMOS observations with the high spatial resolution of VIS/IR satellite data into accurate soil moisture estimates at high spatial resolution. In preparation for the SMOS launch, the algorithm was tested using observations of the UPC Airborne RadIomEter at L-band (ARIEL) over the Soil Moisture Measurement Network of the University of Salamanca (REMEDHUS) in Zamora (Spain), and LANDSAT imagery. Results showed fairly good agreement with ground-based soil moisture measurements and illustrated the strength of the link between VIS/IR satellite data and soil moisture status. Following the SMOS launch, a downscaling strategy for the estimation of soil moisture at high resolution from SMOS using MODIS VIS/IR data has been developed. The method has been applied to some of the first SMOS images acquired during the commissioning phase and is validated against in situ soil moisture data from the OZnet soil moisture monitoring network, in South-Eastern Australia. Results show that the soil moisture variability is effectively captured at 10 and 1 km spatial scales without a significant degradation of the root mean square error.

In the past years, the scientific community has placed a special interest in remotely sensing soil moisture and vegetation parameters. Radiometry and radar techniques have been widely used for years. Global Navigation Satellite Systems opportunity signals Reflected (GNSS-R) over the earth's surface are younger, but they have already shown their potential to perform these observations. This paper presents a GNSS-R technique, based on Global Positioning System (GPS) measurements, that allows the retrieval of several geophysical parameters from land surfaces. This technique measures the power of the interference signal between the direct GPS signal and the reflected one after scattering over the land, so it is called Interference Pattern Technique (IPT). This paper presents the results obtained after applying the IPT for topography, soil moisture, and vegetation height retrievals over vegetation-covered soils.

Sea surface salinity is a key oceanographic parameter that can be measured by means of L-band microwave radiometry. The measured brightness temperatures over the ocean are influenced by the sea state, which can entirely mask the salinity signature. Sea-state corrections parameterized in terms of wind speed and/or significant wave height have proven not to be fully satisfactory. In 2003, it was proposed to use reflectometry using navigation opportunity signals [Global Navigation Satellite System Reflectometer (GNSS-R)] for sea-state determination and correction of the measured L-band brightness temperature changes associated to the sea state. The novelty of the approach relies in the measurement of the whole Delay-Doppler Map that captures the scattering of the GNSS signals in the whole glistening zone. In this framework, the “Advanced L-BAnd emissiviTy and Reflectivity Observations of the Sea Surface” (ALBATROSS) field experiments were undertaken in 2008 and 2009, collecting an extensive data set of collocated radiometric and reflectometric measurements over the Atlantic Ocean, as well as oceanographic and meteorological data. In this paper, the experimental results and conclusions of the ALBATROSS 2009 field experiment are compiled and presented, showing the great potential of this technique to perform the necessary corrections in future salinity missions. Empirical relationships are derived among measured brightness temperature variations due to the sea-state effect and direct GNSS-R observables, and the sea surface correlation time at L1 band, a key parameter for GNSS-R data processing since it determines the maximum coherent integration time, was experimentally determined.

Global Navigation Satellite System's (GNSS) opportunity signals reflected over the surface of the Earth can be used for altimetry, sea state determination... So far, GPS signals scattered over the Earth's surface have been cross-correlated with a local replica of the transmitted signal (typically the GPS available C/A code) shifted in frequency (Δfd) and in delay (τ). However, it is not possible to achieve the full potential of these signals due to the unavailability of the large bandwidth codes. To overcome this problem, the direct and reflected signals can be cross-correlated. However, to improve the SNR high gain antennas are required and they must be steerable so as to point to the direct satellite and to the specular reflection point. Differences between the receivers' frequency responses impact the overall frequency response, and may distort the ideal observables. This work presents the analytical computation of the GNSS reflectometer observables in the above conditions and will compare them to the ideal ones, so as to be able to properly specify the receivers' frequency response.

SMOS imagery has been analyzed to study: 1) radio-frequency interference (RFI) detection and mitigation algorithms, and 2) the statistical properties of RFI. Results show that with a high probability of detection (~0.75), the probability of false alarm is very high as well (~0.68), and most snap-shots seem to be contaminated, even though the estimated RFI value is so weak, and the impact in the SMOS imagery is not even noticeable. Results of the detection and mitigation algorithm are presented, with the statistical analysis of more than 13000 L1b snap-shots.

A proof-of-concept instrument for PARIS-IOD, based on a full-custom dedicated GNSS-Reflections receiver has been developed, and the novel interferometric concept tested in several experiments using signals generated by a SPIRENT equipment. The preliminary analysis has resulted in the detection of 1-cm delay-jumps, using 33 group-delay observables of 1 second, with an associated dispersion of the order of 2-cm. A signal-to-noise degradation of the order of ~3 dB with respect to the one expected for the Bridge Experiment would keep the 1-second sigma error below 3-cm.

This paper presents an end-to-end simulator to assess the performances of Global Navigation Satellite System Reflectometry (GNSS-R) space missions for altimetry or sea state determination. The presented simulator is capable of simulating the GNSS-R observation scenario including the states of the transmitting and receiving satellites, the full instrument modeling, and scattering physical modeling based on an actual geophysical database. It provides useful tools for research and development on GNSS-R remote sensing.

This paper presents the current state of the Passive Advanced Unit Synthetic Aperture's instrument (PAU-SA), its installation in the transportation truck, and the preliminary tests that have been performed to verify the instrument's status.

The Global Navigation Satellite Signals Reflections (GNSS-R) techniques have been widely used for remote sensing purposes retrieving geophysical parameters over different types of surfaces. Over the ocean, altimetry or sea state can be retrieved. Over land, soil moisture can be inferred and over ice, altimetry, and ice age are also retrieved. This paper presents the results of using GNSS-R techniques to retrieve altimetry from the measurements of an airborne GNSS-Reflectometer.

The use of Global Navigation Satellite Signals Reflections (GNSS-R) techniques to retrieve geophysical parameters from surfaces has been increased in the recent years. These techniques have resulted in suitable tools to obtain information about the sea state of oceans, which is very useful to improve the ocean salinity retrieval, and also, information about the soil moisture of lands. The present work focuses on the use of the Interference Pattern Technique (IPT), a particular type of GNSS-R technique, to study vegetation-covered soils. The IPT consists mainly of the measurement of the interference pattern between the GPS direct and reflected signals (the interference power), after they impinge over the ensemble soil surface and vegetation layer. The measured interference signal provides information on the soil moisture of the surface and also, on the vegetation height.

Preliminary results obtained during the commissioning phase of the Soil Moisture and Ocean Salinity (SMOS) mission are described, devoting special attention to the characterization of the systematic errors found in the measurements and the corresponding impact in the retrieved salinity product. The identified issues and objectives to consolidate and improve the processing chain are also described.

Soil Moisture and Ocean Salinity (SMOS) mission is the second of European Space Agency's (ESA) Living Planet Programme Earth Explorer Opportunity Missions. SMOS's objective is to provide global and frequent Soil Moisture and Sea Surface Salinity maps. The single payload embarked on SMOS is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS), it is a 2D interferometric radiometer operating at the protected L-band with a nominal frequency of 1413.5 MHz. Since SMOS is the first 2D interferometric radiometers put in orbit so far, the characterization of the interferometrically measured brightness temperatures is an attractive topic for the scientific community. This study is focused on the estimation of the systematic antenna-based pattern in the measured brightness temperatures. Two improvements to the currently used method (Ocean Target Transformation) are proposed: 1) The elimination of the use of any forward model in the estimation of the bias. 2) The homogenization of the geophysical parameters distribution within the SMOS Field of View. Ocean Target Transformation is introduced in section 2, the proposed model-free methodology is described in section 3, while the effect of homogenizing the geophysical parameters distribution inside the FOV is assessed in section 4. The main conclusions are presented in section 5.

In this work a combined frequency and time-domain algorithm has been developed to detect the presence of Radio Frequency Interference (RFI). The radiometric signal's spectrogram is treated as an image, and therefore image processing techniques can be applied in it; and hence RFI can be detected by 2D filtering. A series of Monte-Carlo simulations have been performed to evaluate the performance of this algorithm.

Within the ESA's SMOS CAL/VAL activities, the GPS and Radiometric Joint Observations (GRAJO) field experiment was conducted. Apart from contributing to the SMOS CAL/VAL, the main purpose of the GRAJO experiment was to study the synergy of L-band radiometry and GNSS Reflectometry for soil moisture retrieval. Long-term experiments under controlled conditions. During one of these intensive short term experiments, the griPAU instrument (a Delay-Doppler Map GNSS-R receiver) was set up. The first results derived from the griPAU measurements are presented.

Global Navigation Satellite Systems (GNSS) signals can be used to infer geophysical data related to the surface where they scatter. When dealing with the sea surface, its state influences the GNSS scattered signals and, therefore, the GNSS reflectometry (GNSS-R) observables. The aim of the Advanced L-band Emissivity and Reflectivity Observations of the Sea Surface 2008 field experiment was to gather experimental data to study the relationship of the GNSS-R delay-Doppler maps (DDMs) and the sea state. This work describes the field campaign and the main results obtained, where among them is the use of the DDM volume as a roughness descriptor weakly affected by the GPS satellite geometry.

The feasibility of the Global Navigation Satellite Signal Reflectometry (GNSS-R) techniques has been proven for remote determination of sea state. When using GNSS-R techniques, coherent integration time is limited by the correlation time of the surface under observation which, in the case of the ocean, depends on sea state. In this letter, a new technique to retrieve the sea correlation time at L-band using GNSS-R coherent data is presented along with experimental results.

Soil Moisture and Ocean Salinity, European Space Agency, is the first satellite mission addressing the challenge of measuring sea surface salinity from space. It uses an L-band microwave interferometric radiometer with aperture synthesis (MIRAS) that generates brightness temperature images, from which both geophysical variables are computed. The retrieval of salinity requires very demanding performances of the instrument in terms of calibration and stability. This paper highlights the importance of ocean salinity for the Earth's water cycle and climate; provides a detailed description of the MIRAS instrument, its principles of operation, calibration, and image-reconstruction techniques; and presents the algorithmic approach implemented for the retrieval of salinity from MIRAS observations, as well as the expected accuracy of the obtained results.

The Soil Moisture and Ocean Salinity mission will provide sea surface salinity maps over the oceans, beginning in late 2009. In this paper an ocean salinity error budget is described, an analysis needed to identify the magnitude of the error sources associated with the retrieval. Instrumental, external noise sources, and geophysical errors have been analyzed, stressing their relative impact. This paper includes results from previous studies, addressing the impact of multisource auxiliary sea surface temperature and wind speed data on the final salinity error. It provides, moreover, a sensitivity analysis to the uncertainty of the auxiliary salinity field. Salinity retrieval has been addressed in a wide set of configurations of the inversion algorithm.

A light-weight L-band radiometer has been designed and implemented at the RS Lab of the Universitat Politecnica de Catalunya (UPC) to gather radiometric airborne data sets. The design of the compact L-band Dicke radiometer (ARIEL) and a general overview of the self-designed aircraft were described in. Preliminary results of the avionics and the radiometer have proven the system performance and capabilities for soil moisture and sea surface salinity retrieval applications. Among other activities, ARIEL will be involved in the SMOS CAL/VAL activities that will take place in the REMEDHUS site (Salamanca, Spain) during the second half of 2009.

The Passive Advanced Unit (PAU) for ocean monitoring is a novel instrument that combines, in a single receiver and without time multiplexing, a microwave radiometer at L-band (PAU-RAD) and a GPS-reflectometer (PAU-GNSS/R). These two sensors together with an infra-red radiometer (PAU-IR) will simultaneously provide the sea surface temperature and the sea state information needed to accurately retrieve sea surface salinity. This paper presents the developed PAU-RAD hardware and software that will allow to obtain a digital radiometer with digital beamformer. After the introduction to the PAU concept presented in section 1, in secton 2 the concept of Digital Beamforming is detailed. Section 3 is devoted to the developement of PAU-RAD and, finally, the main conclusions of this work are resumed in section 4.

The performance analysis of microwave radiometers is often simplified assuming that most/all circuits are perfectly matched and that all are at the same physical temperature. However, the increasing performances demanded to future instruments makes necessary to assess their performance including all these effects: actual complex S-parameters of the different subsystems (non-zero insertion losses, finite matching, finite isolation....), physical temperature gradients etc. in view to devise a software correction, when it is not possible to warrant them by design. A full noise-wave analysis is presented in this paper.

The SMOS in-orbit commissioning phase will start at launching and will last about 6 months. During this phase an extensive activity will start, particularly aimed at providing full confidence on the products that the mission will produce during its operational phase. As part of this activity, the payload MIRAS will be fully characterized using specific orbits dedicated to check all instrument modes. First, the procedures carried out during the on-ground characterization will be repeated so as to obtain a realistic temperature characterization and updated internal calibration. Additionally, a new concept of external calibration, consisting of periodic sky looks, will be tested providing the first-ever absolute instrument calibration parameters. Finally, the imaging capability of the instrument both in dual polarization and full polarimetric will be assessed using images of the Earth surface and the Galaxy. As a final result, the higher level overall performance parameters, such as stability, radiometric sensitivity, radiometric accuracy and absolute accuracy will be evaluated.

This work analyzes the properties of the Delay-Doppler Maps (DDM) derived from the limited experimental data freely available from the UK-DMC. The quality of the received signal over ocean, land and ice is evaluated. In this study the minimum number of incoherent integrations after 1 ms basic coherent integration that are needed to obtain a quality DDM over each surface type is determined using three different techniques: studying the volume of the DDM against the incoherent integration time, observing the signal-to-noise ratio of the signal, and finally analyzing two pixels of the DDM, the maximum and another one noisier, and performing histograms of these points as a function of the incoherent integration time.

GRAJO (GPS and RAdiometric Joint Observations) is a longterm field experiment over land which is being conducted since November 2008 at the REMEDHUS site, Zamora, Spain. REMEDHUS has been identified as a cal/val site for ESA's SMOS mission. The objectives of GRAJO are multiple: (i) validate and calibrate SMOS-derived soil moisture, (ii) study the variability of soil moisture within the SMOS footprint, (iii) test pixel disaggregation techniques to improve the spatial resolution of SMOS observations, (iv) determine the optical depth and vegetation water content of barley and grass and assess their influence on soil moisture estimates from radiometric and GNSS-R measurements, and (v) characterise the soil roughness factor. This paper presents an overview of the GRAJO experiment, describing the setup and measurements strategy.

This paper presents the current state and preliminary results of the Passive Advanced Unit Synthetic Aperture's instrument (PAU-SA) validation tests. The performed test has been focused on the implementation of the PAU-SA's interface in order to test extended source simulations. Moreover, the currently status of the hardware instrument is presented.

Global Navigation Satellite Signals Reflections (GNSS-R) techniques are currently being used for remote sensing purposes retrieving geophysical parameters over different types of surfaces. Over the ocean, sea state information can be retrieved to improve the ocean salinity retrieval. Furthermore, over land these techniques can be used to retrieve soil moisture. This paper presents the theoretical and experimental results of using GNSS-R to retrieve soil moisture when vegetation is present. The particular technique being applied in this study is the Interference Pattern Technique (IPT) that measures the interference pattern of the GPS direct and reflected signals, after reflecting over the surface.

Nowadays, GNSS-R techniques are being used in many remote sensing applications, as altimetry and sea state retrievals over the ocean, soil moisture retrievals over the land, and ice age or altimetry retrievals over the ice. This work describes how the Interference Pattern Technique (IPT), a kind of GNSS-R technique, can be used to perform surface topography measurements over land.

Simulated SMOS salinity retrievals in different algorithm configurations have been studied with the aim of analysing the possible meridional variability due to the increased sampling at mid-high latitudes. Geophysical effects oppose to this potential improvement, therefore a trade-off study has been performed in realistic scenarios. Preliminary results indicate that the cost function settings are significantly determining the evolution of the performances with the latitude. The balancing of the cost function different terms will have to be a mandatory step in the forthcoming satellite commissioning phase analysis.

So far a number of models have been developed to estimate the emission of the sea surface at L-band as a function of different key physical variables, such as the Sea Surface Temperature (SST), the Sea Surface Salinity (SSS) and the roughness as well as the presence of sea foam, but none has demonstrated to clearly perform better than the others. An important contribution in that direction will be given by the Soil Moisture and Ocean Salinity (SMOS) mission in the next future, when global and frequent measurements of the ocean will be available and, jointly with in-situ measurements collected by buoys or vessels, will permit further studies. To rehearse and optimally prepare the future analyses, two field experiments (the Advanced L-BAnd Transmission and Reflection Observations of the Sea Surface - ALBATROSS 2008 and 2009 -) have been carried out in one of the SMOS Calibration and Validation sites: The North Atlantic Subtropical Gyre. Brightness temperature measurements, using L-band real aperture radiometers, jointly with the reflected GPS signal, and in-situ measurements of SSS, SST, wind speed, and wave spectrum were collected during these experiments. The measurements have been analyzed and the first results of this analysis are presented. After an introductory section, the campaign set-up and the measurement procedure is described in section II, while the data processing is explained in section III. Finally, section IV is devoted to the presentation of the preliminary results of the study.

This work describes the design and implementation of a multiband radiometer covering the L, S, C, X, K, Ka and W bands. To add flexibility and simplify the design, a spectrum analyzer is used as IF stage, filter and power detector for all the radiometric measurement bands. Moreover, the instrument includes a thermographic camera operating in the Thermal InfraRed (IR) range (8-14 ¿m) and a multi-spectral camera with four spectral bands: Red, Green, Blue and Near InfraRed (NIR). The purposes of this research are the study of RFI detection and mitigation techniques, and its use to test data fusion techniques to take advantage of all the simultaneous measurements performed.

The sea surface salinity (SSS) retrieval using microwave radiometry is seriously affected by the sea surface roughness. Global Navigation Satellite Signals Reflected (GNSS-R) have been proposed to perform this roughness correction. The selected observable is the volume of the normalized delay-Doppler map (maximum amplitude equal to one) above a threshold. This observable is related to the extension of the glistening zone, which is related to the sea state. Its validity to account for the surface roughness in terms of significant wave height (SWH) was proved during the ALBATROSS 2008 measurement campaign. In the following ALBATROSS 2009 campaign collocated measurements of instantaneous radiometric brightness temperatures and GNSS-R volumes are obtained by two antennas pointing exactly to the same spot with the same beamwidth and beam properties. This work described the preliminary results of these field experiments.

Brightness-temperature retrieval techniques for synthetic aperture radiometers are reviewed. Three different approaches to combine measured visibility and antenna temperatures, along with instrument characterization data, into a general equation to invert are presented. Discretization and windowing techniques are briefly discussed, and formulas for reciprocal grids using rectangular and hexagonal samplings are given. Two known techniques are used to invert the equation, namely, inverse Fourier transform andG-matrix pseudoinverse. The proposed preprocessing approaches combined with these two inversion methods are implemented with real data measured by an airborne Y-shaped interferometric radiometer over land and water, and are compared. The images indicate that best results are obtained when inverting an incremental visibility obtained after substracting a term that includes the individual antenna temperatures, the physical temperatures of the receivers, and a flat-target response directly measured from cold-sky looks.

The on-ground characterization of the synthetic aperture radiometer onboard the Soil Moisture and Ocean Salinity mission is described. Characterization includes basic functionality, internal calibration, thermal cycling, response to point and flat sources, self-radio-frequency interference, and others. The description of the different tests performed as well as the detailed results are provided. The results show that the instrument is very stable and has all gains and offsets consistent with the ones obtained at subsystem level. On the other hand, the phase of the visibility has a larger variation with temperature than expected, a small signal leakage from the local oscillators is present, and a small interference from the X-band transmitter during short periods of time has been detected. The implementation of internal-calibration procedures, along with the accurate thermal characterization performed, have been used to produce highly accurate brightness-temperature values well within specifications.

A new and efficient algorithm to compute delay-Doppler maps is presented. It improves by more than an order of magnitude the required computation time and memory resources. This approach is based on the derivation of explicit expressions of the space coordinates as a function of the delay offset and Doppler shift. Using this technique, the limitation posed by the number of sampling points of the observed surface is drastically attenuated, and a wide range of scenarios from low- to medium-height airborne-to-spaceborne scenarios can now be simulated with standard desktop computers.

A deconvolution-based model has been developed in an attempt to improve the spatial resolution of future soil moisture and ocean salinity (SMOS) data. This paper is devoted to the analysis and evaluation of different algorithms using brightness temperature images obtained from an upgraded version of the SMOS end-to-end performance simulator. Particular emphasis is made on the use of least-square-derived Lagrangian methods on the Fourier and wavelet domains. The possibility of adding suitable auxiliary information in the reconstruction process has also been addressed. Results indicate that, with these techniques, it is feasible to enhance the spatial resolution of SMOS observations by a factor of 1.75 while preserving the radiometric sensitivity simultaneously.

A change detection algorithm has been developed in order to obtain high-resolution soil moisture estimates from future Soil Moisture Active and Passive (SMAP) L-band radar and radiometer observations. The approach combines the relatively noisy 3-km radar backscatter coefficients and the more accurate 36-km radiometer brightness temperature into an optimal 10-km product. In preparation for the SMAP mission, an observation system simulation experiment (OSSE) and field experimental campaigns using the Passive and Active L- and S-band Airborne Sensor (PALS) have been conducted. We use the PALS airborne observations and OSSE data to test the algorithm and develop an error budget table. When applied to four-month OSSE data, the change detection method is shown to perform better than direct inversion of the radiometer brightness temperatures alone, improving the root mean square error by 2% volumetric soil moisture content. The main assumptions of the algorithm are verified using PALS data from the soil moisture experiments held during June-July 2002 (Soil Moisture Experiment 2002) in Iowa. The algorithm error budget is estimated and shown to meet SMAP science requirements.

This paper presents a new technique to retrieve soil moisture using global navigation satellite signals reflected over the soil surface using the measurement of the power fluctuations of the signal created by the interference between the direct GPS signal and the one reflected over the soil surface. As a function of the elevation angle, power fluctuations at vertical polarization pass through a notch, which is related to the soil moisture content, while horizontal polarization exhibits a very weak dependence. Experimental results of the measurements obtained over a bare soil field are presented and discussed.

The Soil Moisture and Ocean Salinity (SMOS) Mission is the second of the European Space Agency's Living Planet Program Earth Explorer Opportunity Missions, and it is scheduled for launch in July 2009. Its objective is to provide global and frequent soil-moisture and sea-surface-salinity (SSS) maps. SMOS' single payload is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) sensor, an L-band 2-D aperture-synthesis interferometric radiometer. For the SSS, the output products of SMOS, at Level 3, will have global coverage and an accuracy of 0.1-0.4 psu (practical salinity units) over 100 times 100-200 times 200 km2in 10-30 days. During the last few years, several studies have pointed out the necessity of combining auxiliary data with the MIRAS-measured brightness temperature to provide the required accuracy. In this paper, we propose and test two techniques to include auxiliary data in the SMOS SSS retrieval algorithm. Aiming at this, pseudo-SMOS Level-3 products have been generated according to the following steps: 1) A North Atlantic configuration of the NEMO-OPA ocean model has been run to provide consistent geophysical parameters; 2) the SMOS end-to-end processor simulator has been used to compute the brightness temperatures as measured by the MIRAS; 3) the SMOS Level-2 processor simulator has been applied to retrieve SSS values for each point and overpass; and 4) Level-2 data have been temporally and spatially averaged to synthesize Level-3 products. In order to assess the impact of the proximity to the coast at Level 3, and the effect of these techniques on it, two different zones have been simulated: the first one in open ocean and the second one in a coastal region, near the Canary Islands (Spain) where SMOS and Aquarius CAL/VAL activities are foreseen. Performance exhibits a clear improvement at Level 2 using the techniques proposed; at Level 3, a smaller effect has been recorded. Coastal proximity has been found to affect the retrieval of up to 150 and 300 km from the coast, at Levels 2 and 3, respectively. Results for both scenarios are presented and discussed.

In ESA's SMOS mission it is expected to improve the sea surface salinity retrieval using multi-angular information to correct for sea state effects. A multi-beam, digitally steered radiometer with polarization synthesis has been proposed for the first time as a part of the PAU instrument (Passive Advanced Unit for the ocean monitoring project). This paper provides theoretical and experimental results of extending the calibration algorithms of a digital radiometer obtained in to any number of receivers in order to achieve the digital beamforming.

The SMOS (Soil Moisture and Ocean Salinity) mission is an Earth Explorer Opportunity Mission from the European Space Agency that will be launched in Spring 2009. SMOS' single payload is a new type of radiometer called MIRAS (Microwave Imaging Radiometer by Aperture Synthesis) operating at L-band in which brightness temperature (BT) images are formed by a Fourier synthesis technique. The spatial resolution of the retrieved BT (~50 km), although appropriate for climate studies, is insufficient for most hydrological studies. Current pixel disaggregation approaches are based on iterative procedures in the BT images or in image deconvolution A technique is presented to improve the spatial resolution of the BT imagery by adding the information in the Fourier domain, instead of An updated overview of the current status of the image reconstruction algorithms in 2D Aperture Synthesis Radiometers for Earth Observation, with special emphasis in the SMOS mission is presented.

Dual polarization brightness temperature retrievals from the small Airborne MIRAS (AMIRAS) are provided. Single snapshots at instrument polarization frame and also at horizontal and vertical polarization are shown. Maps of first Stokes parameter using concatenated snapshots are presented for land areas (including geo-location), sea water and fresh water. Finally, brightness temperature of single points on ground as a function of incidence angle are given. They show relatively high dispersion due to the limited performance of the instrument, especially its low resolution and low snapshot rate.

A software package to fully process SMOS data from level 0 up to brightness temperature at antenna plane (level 1B) is described. The raw data downloaded from the payload are converted to correlations and voltages; then to calibrated visibility and antenna temperature; and finally to brightness temperature. Results at different levels are saved in separate files for further post-processing and a visualization tool is provided in order to check the data at various levels, as well as producing brightness temperature maps in the (xi, eta) domain. The software has been developed independently from the official SMOS-mission Level-1 processor but a detailed process of cross-checking of data at all levels is being carried out in order to consolidate the end product of both.

The reflections of Global Navigation Satellite systems such as GPS can be used to retrieve geophysical parameters. A promising application is to use them for the sea surface roughness-induced corrections in the brightness temperature to retrieve the Sea Surface Salinity. A tandem campaign to obtain simultaneous radiometer and reflectometer data has been conducted on the North-West coast of the Gran Canaria Island (Canary Islands, Spain). The first results after processing the GNSS-R data are presented.

Sun radio emission can be an important interference source for future satellite missions such as NASA/CONAE Aquarius-SAC-D, and ESA's SMOS, which will measure soil moisture and sea surface salinity. Radiometric measurements can be interfered by Sun directly, by leakage through the antenna side lobes, or reflected on the Earth's surface towards the antenna. Direct and reflected Sun measurements were acquired from a ground-based radiometer during the T-REX 2006 experiment. Direct observations of Sun induced an increment of 130 K on the brightness temperature, which is in accordance with theoretical estimates. However, no significant increment on the land brightness temperature due to Sun glint has been detected in measurements at the range of incidence angles from 90deg to 60deg respect to nadir, which seems to indicate that scattered Sun may not be a problem in SMOS, where scattering angles are larger than 45deg, and typically around 65deg.

The SMOS REFLEX 2006 field experiment aimed to measure vines emission during their phenological cycle and to study the impact of vegetation and rocks on the emission. Rocks were kept in half the vineyard (from 40% to 80% of surface rock fraction) and were partially removed in the other half (from 6% to 30% of surface rock fraction). Since rocks have a low and constant dielectric permittivity, their presence masks the soil dielectric increase due to soil moisture. This leads to an almost constant relationship between the measured emission and ground-truth soil moisture and, thus, to a subestimation of the soil moisture by the retrieval algorithms: i.e. the soil appears drier to the radiometer than it actually is.

Different deconvolution algorithms have been developed to explore the possibility of improving the spatial resolution of future Soil Moisture and Ocean Salinity (SMOS) products. An exhaustive test of these methods has been performed over brightness temperature images obtained from an upgraded version of the SMOS End-to-end Performance Simulator (SEPS). Particular emphasis is made on the use of Wiener filter derived methods on the Fourier and on the wavelet domain. The possibility of including suitable auxiliary information in the reconstruction process has also been addressed. Results show that with these techniques it is feasible to improve the spatial resolution (DeltaS) of SMOS observations whereas preserving its radiometric sensitivity (DeltaT), especially in the areas of the field of view far away from nadir. The product DeltaTmiddotDeltaS can be improved by a 49% over soil pixels and by a 30% over sea pixels.

This paper presents the first results of a hybrid synthetic aperture interferometric radiometer-GPS reflectometer called Passive Advanced Unit-Synthetic Aperture (PAU-SA). These tests are focused in the PAU-SA's performance with preliminary results at baseline level in order to characterize the radiometer part. The tests performed are: characterization of the radiometer noise and its stability through "Allan's variance", radiometric sensitivity, calibration of the offset errors and baseline response to a point source at different angles and polarizations.

GNSS-R techniques have been used to retrieve altimetric and sea state information in ocean scenarios. This paper provides theoretical and experimental results of using Global Navigation Satellite Signals reflected (GNSS-R) over bare soil and vegetation-covered soil to retrieve soil moisture using the measurement of the power of the interferent field (direct + scattered).

The Soil Moisture and Ocean Salinity mission will provide from 2009 onwards sea surface salinity maps over the oceans. In this paper an ocean salinity error budget is described. Instrumental, external noise sources and geophysical errors have been analysed, stressing their relative degree of impact. With the aim of improving this study, an extended version of this analysis provides an overall vision of the salinity retrieval in a wider set of configurations.

A few months before the foreseen SMOS launch date (first half of 2009), the interests of the scientists working on the Ocean Salinity Level 2 definition are mainly focused on improving the accuracy of the retrieval algorithm. The algorithm used is based on an iterative procedure in which a "cost function", that relates models, measurements, and auxiliary data, is minimized. For this reason most of the efforts are currently centered in the analysis and the optimization of the cost function. Within this frame, the study proposed represents a contribution to assess one of the pending issues in the definition of the cost function: The optimal weight to give to the MIRAS measurements. To do this a set of simulations have been run using: - The Océan PArallélisé (OPA) Model [1] as source of geophysical parameters; - The SMOS End-to-end Performance Simulator (SEPS) [2] as source of synthetic SMOS-like brightness temperatures imagery; - SMOS-Level2 Processor Simulator (SMOS-L2PS) [3] to perform the retrieval. Results are presented and discussed.

This paper presents the description and first results on the performance of a light-weight airborne L-band radiometer. This radiometer will be used in the SMOS CAL/VAL activities in the REMEDHUS site (Salamanca, Spain). First, a brief description of the overall system is presented. The first results obtained during a field experiment over the Ebro river mouth are then presented. These include flights over the sea shore (fresh and sea water) and land (dry and irrigated rice fields). This field experiment has also served to test the aircraft and the data processing algorithms that will be used in future campaigns with PAU-ORA, a reduced airborne demonstrator version of the PAU instrument.

MIRAS is the single payload of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite mission, and it will be the first synthetic aperture radiometer for Earth observation from space. It consists of an array of 69 antennas uniformly spaced along three arms forming a Y-shaped antenna array. This work analyzes the angular resolution improvement, and the radiometric performance degradation when the spacing between antennas is geometrically increased.

The 38 papers in this special issue were originally presented at the 2007 International Geoscience and Remote Sensing Symposium (IGARSS'07), held in Barcelona during July 2007.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission aims at producing global and frequent maps of SMOS and will be launched in 2008. SMOS' single payload is a new type of radiometer called Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) operating at L-band in which brightness temperature images are formed by a Fourier synthesis technique. However, in the alias-free field of view where the brightness temperature images are reconstructed, a bias is present which has been found to be higher for high-contrast brightness temperature scenes (coastlines) and lower for homogeneous scenes (all oceans or lands). This scene-dependent bias will ultimately limit the achievable accuracy of the retrieved geophysical parameters, and it is particularly critical for the retrieval of sea surface salinity. This paper presents a general analysis of the origin of this bias, which is found to be actually due to the different measurement errors in the instrument observables (visibility samples). An improvement of the image reconstruction algorithm is then presented to mitigate it. As compared with the previous algorithm versions, the proposed improved reconstruction algorithm further decomposes the visibility samples into some new terms: ocean and land/iced sea, instead of just the Earth's disk over the sky background. This decomposition aims at further reducing the contrast (high-frequency components) in the differential image and, therefore, minimizes the impact of multiplicative errors, improving the radiometric accuracy. In addition, this approach proves to perform the image reconstruction in part of the alias regions and improves the quality of the reconstruction close to the coastlines.

This paper presents an efficient procedure based on 2-D convolutions to obtain delay-Doppler maps (DDMs) of Global Navigation Satellite Signals reflected (GNSS-R) over the sea surface and collected by a spaceborne receiver. Two DDM-derived observables (area and volume) are proposed to link the sea-state-induced brightness temperature to the measured normalized DDM. Finally, the requirements to use Global Positioning System reflectometry to accurately correct for the sea state impact on the L-band brightness temperature (quantization levels, decimation, truncation, and noise impact) are analyzed in view of its implementation in the Passive Advanced Unit instrument of the Spanish Earth Observation Satellite (SeoSAT/INGENIO) project.

This paper describes AMIRAS, an airborne demonstrator of the Microwave Imaging Radiometer with Aperture Synthesis, which is the instrument onboard ESA's Soil Moisture and Ocean Salinity (SMOS) mission. The main electrical, mechanical, thermal, and control elements of the demonstrator are shown, together with its capabilities and performances as demonstrator of the spaceborne instrument. AMIRAS main tests inside an anechoic chamber, field ground experiments, and its first two maiden flights are reported, and some results of these tests are highlighted. AMIRAS will further be used in some calibration and validation campaigns of the SMOS mission.

This paper presents the calibration of the PAU-RAD instrument: a novel pseudo-correlation radiometer with digital beamforming, it consist on a Wilkinson power splitter and two receiving chains whose outputs are cross-correlated. To types of calibration are required: an internal hardware, relative calibration to perform the phase and an amplitude correction, and internal radiometric calibration. The simplified and unified calibration procedure is presented using internal well-known temperatures.

The main goal of this work is to present a system that gives to PAU-RAD instrument an easy and friendly interface where the user can modify acquisition parameters, operation mode or access to the stored data, a full remote control with everywhere connection.

The development of new concepts in remote sensing may improve the retrieval of geophysical parameters, an particularly the sea surface salinity. PAU (passive advanced unit for ocean monitoring), a new hybrid, L-band radiometer, GNSS- reflectometer and IR radiometer. This instrument will include a receiving antenna array of 4times4 elements, each one with its corresponding dual-polarization receiver. Since the calibration procedure for the whole ensemble needs to be thoroughly verified, the implementation of single-receiver PAU sensors has been undertaken to obtaining the first experimental data and to allow to compare them with simulation results. Since both ground-based and airborne measurements will be obtained with the full version of PAU, ground-based and airborne one-receiver demonstrators have being implemented. These sensors do not incorporate the IR radiometer included in the full PAU instrument, but are representative of the hybrid radiometer- reflectometer concept. This paper describes both instruments and its future applications.

MIRAS has two types of in-orbit calibration: external, in which the instrument makes a maneuver to point to the cold sky, and internal, in which noise is injected to the receivers. This one in turn has two modes, one of short duration carried out periodically interspersed with scene measurements, and other lasting a full orbit and used to obtain the more stable parameters and the sensitivity of all parameters with temperature. These procedures have already been implemented and have been tested at EADS-CASA in the fully integrated instrument. The results show that the in-orbit internal calibration accurately retrieves the needed parameters and will allow to obtain high quality data.

In this paper, some results of the MIRAS-SMOS demonstrator campaigns are presented. The follow two campaigns were held: an image validation campaign at IRTA side and a flight demonstrator campaign at Finland. The ultimate objective of the airborne demonstrator is to demonstrate its imaging capabilities by applying improved calibration and inversion algorithms to natural low-contrast targets.

The assumed potential of Global Navigation Satellite System Reflections (GNSS-R) to retrieve geophysical parameters has yet to be fully shown. The implementation of a GNSS-R receiver to compute real-time Delay-Doppler Maps (DDM) will be a significant step forward. In the PAU instrument these DDMs will be obtained along with co-located data of brightness temperature. To relate both measures a parameterization of the DDM is performed. Preliminary results have been obtained.

The impact of topography on soil emissivity at L-band is not well known. In order to provide data to assess this issue, the Topography effects on RadiomeTry at L-band Experiment (TuRTLE) 2006 was carried out in a mountainous area about 50 km North of Barcelona (Spain). Radiometric measurements covering the mountain slope, and up to the sky were acquired. Concurrently, ground-truth and meteorological data were registered. Radiometric measurements have been compared to the emissivity obtained by simulation using a facet model which considers the high resolution digital elevation model and land cover map of the area. Polarization mixing due to surface tilting and integration over the antenna pattern have also been included in the simulator, and results agree with the radiometric measurements. The largest discrepancies occur for an almost bare soil at large local incidence angles and H-polarization, close to the radiometer, which suggests that further modeling work is still needed.

Existing L-band land emission models are being improved with radiometric data to be applied in the Soil Moisture and Ocean Salinity mission retrieval algorithms. A set of experiments have been carried out in the past years by the Universitat Politecnica de Catalunya to assess the impact of different soil and vegetation properties on the L-band radiometric signal: soil texture and moisture profile, surface roughness, vine canopy, and topography. This paper presents and discusses their results.

In remote-sensing applications the inclusion of subgrid-scale variability in coarse resolution data still remains an elusive challenge. This paper is devoted to the development of an appropriate downscaling technique for future Aperture Synthesis Radiometer’s images. A comparative study of different deconvolution algorithms has been performed and particular emphasis is made on the use of least-squares Lagrangian methods and Fourier Wiener filtering. Results show that with this technique it is feasible to improve the spatial resolution of brightness temperature images from the Spatial Sensor Microwave Imager (SSM/I) radiometer and from an upgraded version of the Soil Moisture and Ocean Salinity (SMOS) End-to-end Performance Simulator (SEPS).

This paper describes some potential improvements that could be eventually implemented for future MIRAS (Microwave Imaging Radiometer by Aperture Synthesis) payloads of the follow-on missions of the ESA's SMOS (Soil Moisture and Ocean Salinity) mission. A ground-based instrument concept demonstrator has been designed and it is being implemented to validate these improvements. Both MIRAS and the (Synthetic Aperture Passive Advanced Unit, SA-PAU) are Y shaped arrays, but the receiver topology and the processing unit are significantly different. This paper identifies the elements in the MIRAS's design that could be improved and presents a new instrument (Synthetic Aperture PAU) that could be used to test some potential improvements for future SMOSops (SMOS operational system).

This work describes the PAU-IR instrument, an infrared radiometer used to estimate the sea surface temperature for sea surface salinity retrievals. The results of numerical simulations to predict the impact of different effects (rain, clouds, visibility) is presented with the theory to accurately retrieve the sea surface temperature. Finally, the results of a field experiment carried out 40 km South of Barcelona are presented.

The soil moisture and ocean salinity mission will deliver from 2008 onwards global sea surface salinity estimations over the oceans. In this paper, efforts have been devoted to the analysis of horizontal retrieved salinity variability and to the capabilities of the SMOS-derived data to resolve different-scale observed oceanographic features. Statistics of the error have been provided using input/auxiliary data at increasingly higher spatial resolution.

The SMOS (Soil Moisture and Ocean Salinity) mission will provide from 2008 onwards global sea surface salinity estimations over the oceans. This work summarizes several insights gathered in the framework of salinity retrieval studies, aimed to address an overall salinity error budget. The paper covers issues ranging from the impact of auxiliary data on SSS error to the potential exploitation of GNSS-R signals as surface's roughness descriptor, and establishes several guidelines to approach a quasi-realistic post-launch retrieval scenario. Having defined a retrieval setup, an error budget scheme has been built, listing the different contributions to the final retrieved SSS error. On-going activities refer to the fulfillment of the pending issues of the error budget, which are mostly relevant to residual bias mitigation techniques, and Sun and Faraday rotation effect characterization.

The SMOS (Soil Moisture and Ocean Salinity) Mission is the second of the ESA's Living Planet Programme Earth Explorer Opportunity Missions and it is scheduled for launch in 2008. Its objective is to provide global and frequent soil moisture (SM) and sea surface salinity (SSS) maps. SMOS' single payload is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) sensor, an L-band two-dimensional aperture synthesis interferometric radiometer. To help in the retrieval process, auxiliary data will be used in combination with the brightness temperatures measured by MIRAS. In the salinity retrieval case, the main data sources are the sea state, the sea surface temperature, and the salinity values (even at low density sampling), which would allow a more accurate retrieval and a better quality product. One of the obvious candidates of auxiliary data is the ARGO buoy array. In this study, two different algorithms are proposed to use the ARGO salinity measurements in the retrieval procedure and provide coherent SSS maps with these measurements: The first algorithm is referred to as the "external brightness temperature calibration" [1] and aims at eliminating the bias introduced by the instrument errors and the image reconstruction process at the brightness temperature level; The second algorithm is referred to as the "external salinity calibration", and aims at correcting the biases introduced by the retrieval algorithm [2] as well, due to an imperfect dielectric constant model and an imperfect sea state parameterization in the emissivity model. Using the SMOS End-to-end Performance Simulator (SEPS) [3] as a source of SMOS-like brightness temperatures and the ARGO data as the source of auxiliary parameters, a series of simulations have been performed both in open ocean, and in coastal regions using a SMOS Level 2 Processor Simulator (L2PS) developed by the Universitat Politecnica de Catalunya (UPC). The performances of both algorithms are compared, and simulation results are presented and discussed.

This paper describes the implementation of a K-Band radiometer capable to operate as a Total Power Radiometer (TPR), a Dicke Radiometer (DR), and a Noise Injection Radiometer (NIR). This radiometer has been developed for academic purposes in a remote sensing course that is taught at UPC. The objectives include the illustration of the different components of a radiometer and their task in the performance of the overall system. It also can be used to explain the different performances of the TPR DR and NIR.

Sea surface salinity can be remotely measured by means of L-band microwave radiometry. However, the brightness temperature also depends on the sea surface temperature and on the sea state, which is probably today one of the driving factors in the salinity retrieval error budgets of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission and the NASA-Comision Nacional de Actividades Espaciales Aquarius/SAC-D mission. This paper describes the Passive Advanced Unit (PAU) for ocean monitoring. PAU combines in a single instrument three different sensors: an L-band radiometer with digital beamforming (DBF) (PAU-RAD) to measure the brightness temperature of the sea at different incidence angles simultaneously, a global positioning system (GPS) reflectometer [PAU-reflectometer of Global Navigation Satellite Signals (GNSS-R)] also with DBF to measure the sea state from the delay-Doppler maps, and two infrared radiometers to provide sea surface temperature estimates. The key characteristic of this instrument is that both PAU-RAD and the PAU-GNSS/R share completely the RF/IF front-end, and analog-to-digital converters. Since in order to track the GPS-reflected signal, it is not possible to chop the antenna signal as in a Dicke radiometer, a new radiometer topology has been devised which makes uses of two receiving chains and a correlator, which has the additional advantage that both PAU-RAD and PAU-GNSS/R can be operated continuously and simultaneously to perform the sea-state corrections of the brightness temperature. This paper presents the main characteristics of the different PAU subsystems, and analyzes in detail the PAU-radiometer concept.

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The impact of topography and mixed pixels on L-band radiometric observations over land needs to be quantified to improve the accuracy of soil moisture retrievals. For this purpose, a series of simulations has been performed with an improved version of the soil moisture and ocean salinity (SMOS) end-to-end performance simulator (SEPS). The brightness temperature generator of SEPS has been modified to include a 100-m-resolution land cover map and a 30-m-resolution digital elevation map of Catalonia (northeast of Spain). This high-resolution generator allows the assessment of the errors in soil moisture retrieval algorithms due to limited spatial resolution and provides a basis for the development of pixel disaggregation techniques. Variation of the local incidence angle, shadowing, and atmospheric effects (up- and downwelling radiation) due to surface topography has been analyzed. Results are compared to brightness temperatures that are computed under the assumption of an ellipsoidal Earth.

SMOS, acronym for Soil Moisture and Ocean Salinity, is the second selected opportunity mission on "The Living Planet Program: Earth Explorer Opportunity Missions" of the European Space Agency (ESA) [1]. The MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) is the single SMOS pay- load, which is compounded of 69 L-band receivers to form a Y shape interferometer with full on-board calibration capability. The Spanish company EADS-CASA is currently leading the development of a Small scale Airborne prototype of the MIRAS (SAM), in the frame of the MDPP-3 project (MIRAS Demonstrator Pilot Project, stage 3) sponsored by the ESA. Several European institutions, including the Polytechnic University of Catalonia, are participating in the development of the MDPP-3 instrument and test campaigns. The demonstrator consists of 12 receivers called LICEF (4 per arm), 1 reference radiometer (NIR) and includes the same internal calibration system scheme foreseen for MIRAS. The instrument has been designed to be flown in the Skyvan of the LST/HUT (Laboratory of Space Technology, Helsinki University of Technology), where several airborne campaigns over land and sea will be undertaken in order to asses the capability of SMOS calibration and retrieval algorithms to provide soil moisture and ocean salinity.

In [1] a general overview of the PAU system is provided. This work describes in more detail the implementation of the polarimetric and pseudo-correlation radiometer (PAU-RAD) that measures the four Stokes parameters.

This work summarizes the main findings of the activities carried out in the past years by the Microwave Radiometry Team at the Technical University of Catalonia (UPC) in the field of sea surface salinity retrieval within the frame of the SMOS mission in collaboration with the Institute of Marine Sciences (ICM/CMIMA-CSIC). They cover the measurement of the dielectric permittivity of the sea water at L- band, the impact on the sea emissivity of the surface roughness (waves, swell, currents, rain ...) and oil spills, and its comparison with numerical models, as well as the development of sea surface salinity retrieval algorithms for SMOS.

The SMOS (soil moisture and ocean salinity) mission is an earth explorer opportunity mission from the European space agency that will be launched in September 2007. It aims at producing global and frequent maps of soil moisture and ocean salinity to improve the climate modeling. SMOS' single payload is a new type of radiometer called MIRAS (microwave imaging radiometer by aperture synthesis) operating at L-band in which brightness temperature images are formed by a Fourier synthesis technique. Prior to the image reconstruction process, it was first proposed by Camps in 1996 to decompose the calibrated visibilities in several terms: a contribution from the sky, computed from the L-band brightness temperature map of the sky, a contribution from a constant brightness temperature within the Earth at "TEarth", and the differential visibilities (AV), whose value at the origin is zero, and therefore only account for deviations with respect to the constant brightness temperature value within the Earth. This decomposition significantly reduced the Gibbs phenomenon (ringing) near the Earth aliases and accelerated the convergence towards the solution of iterative methods. However, despite these improvements, the retrieved brightness temperature images still present a scene-dependent bias which is found to be higher for high contrast brightness temperature scenes (coastlines), and lower for homogeneous scenes (all ocean or all land), which will ultimately limit the achievable accuracy of the retrieved salinity. This work presents an improvement of the image reconstruction algorithm for 2D aperture synthesis radiometers to mitigate these biases, among other advantages.

The use of global navigation satellite systems (GNSS) reflected signals has been pinpointed as a promising technique for the retrieval of geophysical parameters. This article describes the implementation of a GPS reflectometer to obtain real-time delay-Doppler maps (DDM) to infer sea-state data, needed to correct the L-band brightness temperature and measure sea salinity. This instrument will be based on a FPGA-embedded system, and is part of the PAU concept.

Soil roughness is an important parameter whose effects on soil emissivity at L-band have not been satisfactorily quantified so far. This paper presents the preliminary results of the Terrain-Roughness Experiment (T-REX), which was carried out in Agramunt, Spain, during the fall of 2004. Four identical sets, each of them having four plots with different plough but the same soil type, were measured. Two profiles per soil plough were acquired, one parallel and the other perpendicular to the antenna frame. Since the purpose of T-REX 2004 was to analyze the impact of surface roughness on the received signal, the soil was not irrigated. Results show that the soil emissivity increases as the soil roughness increases. The horizontal polarization seems to be more sensitive to roughness than the vertical polarization for fields with a tillage direction perpendicular to the antenna reference frame. Emissivity at horizontal polarization decreases as the incidence angle increases, while the trend for the vertical polarization is almost constant when the soil is dry.

This paper describes the receiver that has been designed for the PAU instrument [1]. The design's main challenge has been the integration in a single 7 cm x 11 cm x 3 cm box of a receiving unit consisting of 4 channels (two per polarization) and merging two different sub-systems: an L-band radiometer (PAU-RAD) and a GNSS-Reflectometer (PAU-GNSS-R) [2].

Accurate measurement of the antenna voltage patterns of large-aperture synthesis radiometers is critical in order to achieve good radiometric accuracy, and a very time consuming and expensive task. Measurement requirements and a tradeoff study relating radiometric accuracy degradation and number of elements to be measured are presented.

Aiming to provide sea surface salinity (SSS) maps with a spatiotemporal averaged accuracy of 0.1 psu (practical salinity units), the Soil Moisture and Ocean Salinity (SMOS) community is increasingly focusing on the determination of a robust inversion scheme to enable SSS retrieval from L-band brightness temperature data. In the framework of the Synergetic Aspects and Auxiliary Data Concepts for Sea Surface Salinity Measurements from Space project, efforts have been oriented toward a quantitative analysis of SSS retrieval using different auxiliary data sets. This paper aims to contribute to the assessment of the SMOS salinity retrieval error budget in view of the upcoming SMOS mission ground segment development. Aiming to do that, different models and auxiliary data to simulate and invert the brightness temperature data have been used. An estimation of the different auxiliary parameters' influence has been performed to quantitatively predict at what extent it is reasonable to expect to retrieve salinity once the brightness temperatures are directly measured by the sensor. Statistical distributions of the spatiotemporal averaged errors are provided

This paper reviews the relative calibration of an interferometric radiometer taking into account the experimental results of the first batch of receivers developed in the frame of the European Space Agency's Soil Moisture and Ocean Salinity mission. Measurements show state-of-the-art baseline performance as long as the system is capable of correcting the effect of orbital temperature swing. A method to validate internal calibration during in-orbit deep-sky views and to correct linearity errors is also presented.

Electromagnetic emission and scattering models use the sea surface spectrum or its statistics to model the surface roughness. However, in some cases it is desirable to apply the model over a surface height function directly, or to include other effects. The technique described consists of the numerical generation a random surface whose directional spectrum corresponds to that of a wind-driven sea surface, including or not swell waves, and eventually perturbed by rain. The time- dependence is introduced taking into account the deep-water or shallow-water dispersion relationships. Finally, foam patches are distributed over the sea surface according to the surface slopes and taking into account the foam statistical distribution and its extinction time. This moving sea surface generator can be easily applied to various remote sensing applications as emission and monostatic or bistatic scattering including the effect of the footprint size, and properly selecting the lower and upper cut-off wavenumbers, the impact of different spectra, swell, fetch, rain effects etc can be studied.

Aiming to provide sea surface salinity (SSS) maps with a spatio-temporal averaged accuracy of 0.1 psu, the SMOS community is increasingly focusing on the determination of a robust inversion scheme to enable SSS retrieval from L-band brightness temperature data. In the framework of the "Synergetic Aspects and Auxiliary Data Concepts for Sea Surface Salinity Measurements from Space" project, efforts have been oriented towards a quantitative analysis of SSS retrieval once different auxiliary data are plugged into the minimization procedure, providing statistical distributions of the spatio-temporal averaged errors.

This paper reviews the relative calibration of an interferometric radiometer taking into account the experimental results of the first batch of receivers developed in the frame of the European Space Agency's Soil Moisture and Ocean Salinity mission. Measurements show state-of-the-art baseline performance as long as the system is capable of correcting the effect of orbital temperature swing. A method to validate internal calibration during in-orbit deep-sky views and to correct linearity errors is also presented.

The brightness temperature measured from land is related to the soil moisture through the dielectric constant, which depends, among other parameters, on soil composition and porosity. The aim of MOUSE campaign was to establish the brightness temperature dependence on the soil type and moisture content (1). For this study and for the retrieval of the soil moisture content from radiometric measurements, it is necessary to use accurate models for the soil complex dielectric constant. This paper presents a technique to measure the complex dielectric constant using a microwave stripline setup, and its application to the soil samples measured during MOUSE 2004 campaign. The measured dielectric constants of each soil type versus volumetric soil moisture (0% to 40%) are compared with the values obtained using models already appeared in the literature. Keywords-Soil moisture; dielectric constant.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission will be the first one using two-dimensional aperture synthesis radiometry for Earth observation. This study presents the formulation that relates instrument observables and brightness temperature maps including cross-polar antenna voltage patterns, which may be also different from element to element. Finally, the radiometric accuracy degradation if cross-polar patterns are neglected in the image reconstruction is studied.

The effect of the frequency dependence of the antenna voltage patterns within the bandwidth of an aperture synthesis interferometric radiometer is studied and quantified using actual antenna voltage patterns measured at different frequencies for the first nine receivers of the Microwave Imaging Radiometer with Aperture Synthesis instrument aboard the European Space Agency's Soil Moisture and Ocean Salinity mission. A technique is proposed to mitigate this effect.

The Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Opportunity Mission was selected in May 1999 by the European Space Agency Earth Observation Programme Board to provide global and frequent soil moisture (SM) and sea surface salinity (SSS) maps. SMOS' single payload is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) sensor, an L-band two-dimensional aperture synthesis interferometric radiometer with multiangular and polarimetric imaging capabilities. The definition of the SMOS Level 2 Processor requires the selection of the optimum operation mode (dual-polarization or full-polarimetric) for each application, the specification of the required auxiliary data, and the optimum retrieval algorithms. Using the SMOS simulator and based on the experience gained in previous works, this paper presents a study of the SM and SSS retrieval capabilities over homogeneous pixels, in the two modes of operation with different auxiliary data. It is found that SSS retrievals using the first Stokes parameter measured in the dual-polarization mode perform somewhat worse than using the vertical (T/sub vv/) and horizontal (T/sub hh/) brightness temperatures measured in the full-polarimetric mode, and the performance degrades for cold waters due to the lower sensitivity of the brightness temperature to SSS at low sea surface temperature (SST). Due to the larger angular variation of T/sub hh/ and T/sub vv/, SM retrievals using T/sub hh/ and T/sub vv/ measured in the full-polarimetric mode exhibit a significant better performance over bare soils than over vegetation-covered soils. Over vegetation-covered soils vegetation parameters (opacity and albedo) can be inferred over a 550-km swath width in the full-polarimetric mode. However, since the first Stokes parameter is independent of both geometric and Faraday rotations, it is very robust in the presence of instrumental and geophysical errors. In the SSS retrieval problem and in the SM retrieval problem (with T/sub hh/ and T/sub vv/ measured in the full-polarimetric mode), the performance of the retrieval algorithms tested is not significantly altered if the model parameters are not exactly known, but are left as adjustable parameters in the optimization process.

Sea surface salinity can be measured by microwave radiometry at L-band (1400-1427 MHz). This frequency is a compromise between sensitivity to the salinity, small atmospheric perturbation, and reasonable pixel resolution. The description of the ocean emission depends on two main factors: (1) the sea water permittivity, which is a function of salinity, temperature, and frequency, and (2) the sea surface state, which depends on the wind-induced wave spectrum, swell, and rain-induced roughness spectrum, and by the foam coverage and its emissivity. This study presents a simplified two-layer emission model for foam-covered water and the results of a controlled experiment to measure the foam emissivity as a function of salinity, foam thickness, incidence angle, and polarization. Experimental results are presented, and then compared to the two-layer foam emission model with the measured foam parameters used as input model parameters. At 37 psu salt water the foam-induced emissivity increase is /spl sim/0.007 per millimeter of foam thickness (extrapolated to nadir), increasing with increasing incidence angles at vertical polarization, and decreasing with increasing incidence angles at horizontal polarization.

End-to-end calibration of the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) radiometer refers to processing the measured raw data up to dual-polarization brightness temperature maps over the earth's surface, which is the level 1 product of the Soil Moisture and Ocean Salinity (SMOS) mission. The process starts with a self-correction of comparators offset and quadrature error and is followed by the calibration procedure itself. This one is based on periodically injecting correlated and uncorrelated noise to all receivers in order to measure their relevant parameters, which are then used to correct the raw data. This can deal with most of the errors associated with the receivers but does not correct for antenna errors, which must be included in the image reconstruction algorithm. Relative S-parameters of the noise injection network and of the input switch are needed as additional data, whereas the whole process is independent of the exact value of the noise source power and of the distribution network physical temperature. On the other hand, the approach relies on having at least one very well-calibrated reference receiver, which is implemented as a noise injection radiometer. The result is the calibrated visibility function, which is inverted by the image reconstruction algorithm to get the brightness temperature as a function of the director cosines at the antenna reference plane. The final step is a coordinate rotation to obtain the horizontal and vertical brightness temperature maps over the earth. The procedures presented are validated using a complete SMOS simulator previously developed by the authors.

A complete and systematic noise analysis of radiometer front-ends, including both total power and correlation measurements, is presented. The procedure uses the concepts of noise waves and S-parameters, widely used in microwave systems design and takes into account full noise characterization of receivers including mismatch effects. The general formulation is compatible with known total power radiometer analysis and is specially appropriate in correlation radiometers for which the effect of nonideal components, such as input isolators, is analyzed. Along with numerical simulations, simple formulas are given to compute the measured visibility in nonideal conditions. The analysis is validated using experimental results consisting of correlation measurements of four receivers placed inside an anechoic chamber. Good agreement between theoretical predictions and experimental data is observed.

The goal of the Soil Moisture and Ocean Salinity mission over land is to infer surface soil moisture from multiangular L-band radiometric measurements. As the canopy affects the microwave emission of land, it is necessary to characterize different vegetation layers. This paper presents the Reference Pixel L-Band Experiment (REFLEX), carried out in June-July 2003 at the Vale/spl grave/ncia Anchor Station, Spain, to study the effects of grapevines on the soil emission and on the soil moisture retrieval. A wide range of soil moisture (SM), from saturated to completely dry soil, was measured with the Universitat Polite/spl grave/cnica de Catalunya's L-band Automatic Radiometer (LAURA). Concurrently with the radiometric measurements, the gravimetric soil moisture, temperature, and roughness were measured, and the vines were fully characterized. The opacity and albedo of the vineyard have been estimated and found to be independent on the polarization. The /spl tau/--/spl omega/ model has been used to retrieve the SM and the vegetation parameters, obtaining a good accuracy for incidence angles up to 55/spl deg/. Algorithms with a three-parameter optimization (SM, albedo albedo, and opacity) exhibit a better performance than those with one-parameter optimization (SM).

The SMOS(Soil Moisture and Ocean Salinity) Mission was selected in May 1999 by the European Space Agency to provide global and frequent soil moisture and sea surface salinity maps. SMOS' single payload is MIRAS(Microwave Imaging Radiometer by Aperture Synthesis), an L-band 2D aperture synthesis interferometric radiometer with multiangular observation capabilities. To achieve the ultimate goal of 0.1 psu error in ocean salinity retrievals, most studies assume the independence of measurements both in time and space so that the standard deviation of the retrieval errors decreases with the inverse of square root of the number of measurements being averaged. However, biases in the brightness temperature images originated from errors in the noise injection radiometers. Sun contributions to the antenna temperature, and imaging under aliasing conditions need to be compensated before attempting to retrieve the sea surface salinity. This work presents an external calibration suitable to perform salinity retrievals with an acceptable error. This study has been performed using the SMOS End-to-end Performance Simulator(SEPS) including thermal noise, all instrumental error sources, current error-correction and image reconstruction algorithms, and correction of atmospheric and sky noises. Simulation results show a retrieved salinity error of /spl sim/1 psu in one overpass over open ocean, which can be reduced afterwards by spatio-temporal averaging, much better than the -5 psu error that appears when image biases are not corrected.

Drastically different sea states result for a growing, decaying, or a fully-developed sea due to the presence of a wind field. In addition, the sea state is altered by the superposition of oceanic currents on the wave field created by the wind. This study presents a numerical analysis of the change in the sea surface brightness temperature at L-band computed using the SSA model and a modified Kitaigorodskii-Pierson-Moskowitz sea surface spectrum to account for the presence of surface water currents. Their impact on the brightness temperature variations is evaluated and compared to the brightness temperature fluctuations observed from a tower-based field experiment. The consequences for salinity retrievals from space using L-band radiometry are discussed

The SMOS (Soil Moisture and Ocean Salinity) Mission was selected in May 1999 by the European Space Agency to provide global and frequent soil moisture and sea surface salinity maps. SMOS single payload is MIRAS (Microwave Imaging Radiometer by Aperture Synthesis), an L-band 2D aperture synthesis interferometric radiometer with multi-angular observation capabilities and dual-polarization and full-polarimetric capabilities. The impact of thermal drifts in the SMOS rms radiometric accuracy during a complete orbit is evaluated by means of the SMOS End-to-end Performance Simulator (SEPS) [1,2,3] for three different cases of interest: the so-called "cold", "hot", and "LAP" (Low Available Power) cases, that correspond to the extreme thermal conditions in a year, and the case where there is not enough power to keep the thermal control. Errors originated by receivers' frequency response mismatch, and their thermal drifts are included, while antenna voltage patterns errors are assumed to be the same, and not temperature-dependent. Current error correction and image reconstruction algorithms are applied to obtain the synthetic brightness temperatures. To avoid scene-dependent effects on the rms radiometric accuracy drifts, it is computed using a 150 K constant brightness temperature in the directions occupied by the Earth, and external sources such as the Sun, the Moon and the sky (cosmic and galactic) radiations are not modeled by switching them off in SEPS. The inter-calibration period is estimated according to the maximum error drift that can be accepted in the different cases

A complete systematic analysis of correlation radiometer front-ends is presented. It takes into account full noise characterisation of the individual receivers and mismatch effects between different subsystems. The results are applied to the case where the front-ends include input isolator both ideal and imperfect. Along with numerical simulations, simple formulas are given to compute the visibility in these conditions, which is here defined as system visibility. The analysis is validated using experimental results consisting of correlation measurements of four receivers placed inside an anechoic chamber. Good agreement between theoretical predictions and experimental data is observed

The microwave Doppler radiometer can potentially allow satellite-based radiometers to operate with improved resolution than is currently available (30-40 km) and with a much-reduced number of sensing antennas (as few as 3). Such a radiometer relies on the motion of the satellite to create an image based on the cross-correlations of focused signals collected from multiple receivers. The angular resolution of the array is based on the reduction in the correlation of signals from the two antennas at various angles away from the point of interest. This work presents the simulation and data processing results of an indoor experiment performed inside an anechoic chamber using a three-element microwave receiving system that collected data from a microwave noise source mounted on an arm that described a circular arc. This microwave source effectively simulated natural Earth-based emissions that the satellite would detect. The experimental results were quite consistent with the simulation results, although for this particular setup there was a very broad beam along one angle and a less broad beam along the other. Although this system has a different geometry than an Earth-observing satellite, it illustrates the potential applicability of such a radiometer for high-resolution satellite-based measurements on the Earth

Soil Moisture and Ocean Salinity (SMOS) is an Earth Explorer Opportunity Mission from the European Space Agency with a launch date in 2007. Its goal is to produce global maps of soil moisture and ocean salinity variables for climatic studies using a new dual-polarization L-band (1400-1427 MHz) radiometer Microwave Imaging Radiometer by Aperture Synthesis (MIRAS). SMOS will have multiangular observation capability and can be optionally operated in full-polarimetric mode. At this frequency the sensitivity of the brightness temperature (T/sub B/) to the sea surface salinity (SSS) is low: 0.5 K/psu for a sea surface temperature (SST) of 20/spl deg/C, decreasing to 0.25 K/psu for a SST of 0/spl deg/C. Since other variables than SSS influence the T/sub B/ signal (sea surface temperature, surface roughness and foam), the accuracy of the SSS measurement will degrade unless these effects are properly accounted for. The main objective of the ESA-sponsored Wind and Salinity Experiment (WISE) field experiments has been the improvement of our understanding of the sea state effects on T/sub B/ at different incidence angles and polarizations. This understanding will help to develop and improve sea surface emissivity models to be used in the SMOS SSS retrieval algorithms. This paper summarizes the main results of the WISE field experiments on sea surface emissivity at L-band and its application to a performance study of multiangular sea surface salinity retrieval algorithms. The processing of the data reveals a sensitivity of T/sub B/ to wind speed extrapolated at nadir of /spl sim/0.23-0.25 K/(m/s), increasing at horizontal (H) polarization up to /spl sim/0.5 K/(m/s), and decreasing at vertical (V) polarization down to /spl sim/-0.2 K/(m/s) at 65/spl deg/ incidence angle. The sensitivity of T/sub B/ to significant wave height extrapolated to nadir is /spl sim/1 K/m, increasing at H-polarization up to /spl sim/1.5 K/m, and decreasing at V-polarization down to -0.5 K/m at 65/spl deg/. A modulation of the instantaneous brightness temperature T/sub B/(t) is found to be correlated with the measured sea surface slope spectra. Peaks in T/sub B/(t) are due to foam, which has allowed estimates of the foam brightness temperature and, taking into account the fractional foam coverage, the foam impact on the sea surface brightness temperature. It is suspected that a small azimuthal modulation /spl sim/0.2-0.3 K exists for low to moderate wind speeds. However, much larger values (4-5 K peak-to-peak) were registered during a strong storm, which could be due to increased foam. These sensitivities are satisfactorily compared to numerical models, and multiangular T/sub B/ data have been successfully used to retrieve sea surface salinity.

The Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) is the single payload of the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Opportunity mission. MIRAS will be the first two-dimensional aperture synthesis radiometer for Earth observation. Two-dimensional aperture synthesis radiometers can generate brightness temperature images by a Fourier synthesis process without mechanical antenna steering. To do so and have the necessary wide swath for Earth observation, the array is formed by small and low directive antennas, which do not attenuate enough bright noise sources that may interfere with the measurements. This study analyzes the impact of the radio-frequency emission from the Sun in the SMOS mission, reviews the basic image reconstruction algorithms, and proposes a technique to minimize Sun effects.

The fundamental equation of interferometric aperture synthesis radiometry is revised to include full antenna pattern characterization and receivers' interaction. It is shown that the cross correlation between the output signals of a pair of receivers is a Fourier-like integral of the difference between the scene brightness temperature and the physical temperature of the receivers. The derivation is performed using a thermodynamic approach to account for the effects of mutual coupling between antenna elements. The analysis assumes that the receivers include ferrite isolators so that the noise wave passing from the receiver toward the antenna can be modeled as uncorrelated ambient noise. The effect of wide beamwidth antennas on the polarization basis of the retrieved brightness temperature is also discussed.

The results from two field experiments in the Mediterranean Sea are used to study the wind speed dependence of brightness temperature at L-band. During the EuroSTARRS airborne experiment, an L-band radiometer made measurements across a large wind speed gradient, enabling us to study this dependence at high wind speed. We compare our results with a two-scale emissivity model using several representations of the sea state spectrum. While the results are encouraging, unfortunately the accuracy of the measurements does not permit us to distinguish between the so-called twice Durden and Vesecky spectrum and the Elfouhaily spectrum above 7 m/spl middot/s/sup -1/. The effect of foam is certainly small. During the WISE 2001 field experiment carried on an oil rig, we studied this dependence at low wind speed, finding an abrupt decrease of the wind speed effect on the brightness temperature below 3 m/spl middot/s/sup -1/.

The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission aims at obtaining global maps of soil moisture and sea surface salinity from space for large-scale and climatic studies. It uses an L-band (1400-1427 MHz) Microwave Interferometric Radiometer by Aperture Synthesis to measure brightness temperature of the earth's surface at horizontal and vertical polarizations (T/sub h/ and T/sub v/). These two parameters will be used together to retrieve the geophysical parameters. The retrieval of salinity is a complex process that requires the knowledge of other environmental information and an accurate processing of the radiometer measurements. Here, we present recent results obtained from several studies and field experiments that were part of the SMOS mission, and highlight the issues still to be solved.

The SMOS Earth Explorer Opportunity Mission was selected in May 1999 by the European Space Agency to provide global and frequent soil moisture and sea salinity maps. SMOS' single payload is MIRAS (Microwave Imaging Radiometer by Aperture Synthesis), an L-band two-dimensional aperture synthesis interferometric radiometer with multi-angular observation capabilities. Two-dimensional brightness temperature images are obtained by processing the cross-relations measured by pairs of signals collected by each of the 72 elements of a Y-shaped array. Due to the significant differences with respect to its radio-astronomy predecessors (larger number of receiving channels and wider field-of-view) during the past decade a detailed instrument error model was studied, as well as new calibration and image reconstruction techniques. The analysis of the overall performance of this complex instrument required the implementation of an end-to-end performance simulator (Camps, 1996; Corbella et al., 2003; Camps et al., 2002), which has been and it is currently being used in the technical design trade-off of the instrument. This software tool was also designed to help both the engineers working in the instrument and mission design and the scientific community in the implementation of new multi-angular algorithms for soil moisture and ocean salinity retrieval. This paper briefly describes the main functionalities and capabilities of the present and next versions of the SMOS End-to-end Performance Simulator (SEPS) and presents examples of application to the soil moisture (SM) and sea surface salinity (SSS) retrieval problems.

Peer Reviewed

A reduced Y-shape interferometric radiometer has been used to simulate the performance of MIRAS, the single payload of the ESA SMOS mission. The hardware has been designed according to initial MIRAS specifications using first versions of antennas, L-band receivers, digital correlators and calibration subsystems. A complete set of experiments to assess basic operation, calibration methods and instrument performance has been successfully carried out and is described in this paper.

The European Space Agency SMOS (Soil Moisture and Ocean Salinity) mission aims at obtaining global maps of both variables from space for large scale climatic studies. In uses an L-band microwave interferometric radiometer with aperture synthesis (MIRAS) to measure brightness temperature (T/sub B/) emitted by the Earth surface and then compute from it the two geophysical parameters. The retrieval of salinity is a complex process that requires the knowledge of other environmental information and an accurate processing of the radiometer measurements. Here we present the recent results obtained from different studies and campaigns as part of the SMOS mission and highlight the different issues still to be solved.

Up to now, no space mission has attempted to measure the Sea Surface Salinity (SSS) due to technological challenges. The European Space Agency's SMOS (Soil Moisture and Ocean Salinity) mission, to be launched in 2007, aims at the generation of global SSS maps with a spatial and temporal resolution adequate for climatic and ocean general circulation studies. The sensor aboard SMOS is an L-band interferometric radiometer with full polarimetric capability called MIRAS (Microwave Imaging Radiometer by Aperture Synthesis). At low frequencies the brightness temperature measured by a radiometer presents maximum sensitivity to surface salinity, and optimal conditions are found at L-band (1400 MHz-1427 MHz), which it is a band protected for passive measurements. Even though the sensitivity is maximum at this frequency, it is not very high: 0.5 K/psu for sea temperature of 20/spl deg/C, decreasing to 0.25 K/psu for 0/spl deg/C. Furthermore, other variables influence the brightness temperature, as the sea roughness (mainly caused by the wind speed), sea foam, and also sea surface temperature. When SMOS will be in orbit it will be necessary to have accurate measurements of the sea roughness and surface temperature as much coincident as possible -both in time and space- to SMOS acquisitions, because errors on these ancillary data produce large errors on the retrieved salinity. This paper presents a study of the effect on the retrieved salinity of inaccuracies in the wind speed and wave height values. It compares the retrieved salinities when using different sources of these ancillary data: satellite measurements and meteorological models. It also investigates the possibility of allowing the algorithm to retrieve an effective wind speed, in addition to the sea surface salinity.

One of the goals of ESA's SMOS mission is the measurement of global sea surface salinity maps using MIRAS, a synthetic aperture interferometric L-band radiometer with full-polarimetric capabilities. To do so, sea state effects on the brightness temperature must be compensated, but coincident measurements of wind speed and/or significant wave height are uncommon. The objective of this work is to estimate the sea surface spectrum at a given time and location from the previous temporal and spatial evolution of the wind conditions. To do so, a neural network has been trained to estimate the spectrum parameters (height variance, spectral deviation /spl sigma//sub x,y/ and spectral peak of swell k/sub xm,ym/, wind velocity V/sub 10/') from the measured spectra. Different measured spectra have then been used to validate the network design. Finally, the error in the L-band brightness temperature computed from the actual and the estimated spectra is studied.

This paper describes the hardware implementation and first tests of a 3 GPS-channels Doppler-delay receiver for remote sensing applications implemented by the Technical University of Catalonia (UPC) for the Institute of Space Studies of Catalonia (IEEC/CSIC). It can be configured for several applications. For altimetry applications: by looking at the differential delay between the correlation peaks ("waveforms") between the pseudo random noise (PRN) sequence from a visible GPS-satellite and the direct and reflected signals collected by one antenna with a broad beam pointing to the zenith, and another one pointing to the sea surface (not necessarily at nadir). For sea state determination: from the shape of the ambiguity function (both in the Doppler and delay). Additionally, the instrument can be configured for GPS occultation experiments, etc. This paper presents the description of the instrument implementation and two preliminary tests performed at the Garraf coast (South of Barcelona city, Spain) and at the Zeeland Bridge (The Netherlands).

This paper describes the deployment of a fully polarimetric K-band radiometer in the Rough Evaporation Duct (RED) experiment, which was conducted during August and September of 2001. The calibration of the four Stokes parameters is described, along with a comparison of the measurements with results of both the Klein-Swift and Ellison et al. sea surface dielectric models. The purpose of the experiment was to improve physical forward models of the ocean surface emission in order to improve wind vector retrieval algorithms.

The Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Opportunity Mission of the European Space Agency (ESA) aims to provide a global and precise soil moisture estimation over land and salinity content over oceans from the L-band radiometer MIRAS. The accuracy of salinity retrieval algorithms, which are iterative methods, directly depends on the accuracy of the sea surface emissivity models and on the sea surface spectrum applied for characterizing the sea roughness. This paper presents an inter-comparison study of the most popular emissivity models when they are used for estimating the brightness temperature of the sea surface at L-band. The influence of the spectrum model has been also studied.

Two field experiments named WISE (WInd and Salinity Experiment) were sponsored by ESA to better understand the wind and sea state effects on the brightness temperatures. They took place at the Casablanca oil rig located in the North Mediterranean Sea, 40 km off shore the Ebro river delta: WISE 2000 from November 25 to December 18 2000, and continued during the January 9 to 16 2001, and WISE 2001 from October 23 to November 22. During the spring of 2003, under Spanish National funds, a new field experiment named FROG (Foam, Rain, Oil slicks and GPS reflectometry) took place at the Ebro river delta, to measure the phenomena that were not completely understood during the WISE campaigns, mainly the effect of foam and rain. In order to achieve the objectives of the WISE field experiments the LAURA L-band fully polarimetric radiometer from the Technical University of Catalonia (UPC) was mounted on the Casablanca oil-rig at the 32 meters deck above the sea surface, pointing to the North and North-West, in the direction of the dominant winds. In this paper we present the results of the first study to determine the relationship between the brightness temperature and the sea foam coverage.

In May 1999, the European Space Agency (ESA) selected the Earth Explorer Opportunity Soil Moisture and Ocean Salinity (SMOS) mission to obtain global and frequent soil moisture and ocean salinity maps. SMOS' single payload is the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS), an L-band two-dimensional aperture synthesis radiometer with multiangular observation capabilities. At L-band, the brightness temperature sensitivity to the sea surface salinity (SSS) is low, approximately 0.5 K/psu at 20/spl deg/C, decreasing to 0.25 K/psu at 0/spl deg/C, comparable to that to the wind speed /spl sim/0.2 K/(m/s) at nadir. However, at a given time, the sea state does not depend only on local winds, but on the local wind history and the presence of waves traveling from far distances. The Wind and Salinity Experiment (WISE) 2000 and 2001 campaigns were sponsored by ESA to determine the impact of oceanographic and atmospheric variables on the L-band brightness temperature at vertical and horizontal polarizations. This paper presents the results of the analysis of three nonstationary sea state conditions: growing and decreasing sea, and the presence of swell. Measured sea surface spectra are compared with the theoretical ones, computed using the instantaneous wind speed. Differences can be minimized using an "effective wind speed" that makes the theoretical spectrum best match the measured one. The impact on the predicted brightness temperatures is then assessed using the small slope approximation/small perturbation method (SSA/SPM).

The US Salinity Temperature and Roughness Remote Scanner (STARRS) was exploited during an European campaign in 2001 (EuroSTARRS) supporting the scientific preparation of the Soil Moisture and Ocean Salinity (SMOS) mission. This paper is intended to introduce the EuroSTARRS experiment set-up and to provide an overview of all activities performed during this campaign.

In May 1999 the European Space Agency (ESA) selected the SMOS Earth Explorer Opportunity mission to provide global soil moisture and sea surface salinity (SSS) maps using the MIRAS L-band aperture synthesis interferometric radiometer. The sea salinity signature is very small and sea surface temperature (SST) and mainly wind speed (WS) effects must be accurately modelled. In order to achieve the required accuracy (0.1 psu), measurements must be averaged in time (10-30 days) and space (100 km). However, the geophysical parameters SSS, SST and WS change in time and space and the improvement factor by averaging remains open. We use the SMOS End-to-end Performance Simulator (SEPS) developed by EADS-CASA, GMV and UPC to simulate the SSS retrieval performance.

The WISE 2000 and WISE 2001 field campaigns were sponsored by ESA to gather experimental data to improve the knowledge of the L-band brightness temperature dependence with wind speed at different incidence angles and azimuth angles. The goal is to help the development of sea surface salinity retrieval algorithms for SMOS Earth Explorer Mission of the European Space Agency. The L-band AUtomatic RAdiometer (LAURA) plus other sensors to characterize the sea surface state were installed at the Casablanca oil rig, 40 km south east off the coast of Tarragona in Spain. During WISE 2000 wind conditions were low-to-moderate, but during WISE 2001 two strong storms beat the Catalonian coast with sustained winds higher than 100 km/h at the platform meteorological station (69 m height). The first results of the radiometric measurements (azimuth and elevation scans) acquired with LAURA during the WInd and Salinity Experiment (WISE-2001) are presented.

In May 1999 the SMOS Earth Explorer mission was selected by ESA to provide global soil moisture and sea salinity maps. SMOS payload is MIRAS: an L-band 2D aperture synthesis interferometric radiometer, that generates brightness temperature images by processing the cross-correlations measured between the pairs of signals collected by each array element. In this paper we compare the results of the error budget used in previous SMOS studies to those obtained from a simulation campaign for different scenarios (brightness temperature input scenes). The total radiometric error, which includes the radiometric sensitivity term mainly due to the finite integration time, and the radiometric accuracy due to instrumental imperfections as well as residual calibration error, is computed from a series of 20 Monte Carlo simulations for each instrument configuration and scenario for both the brightness temperatures retrieved in the antenna reference frame (X- and Y-polarizations) and the Earth reference frame (V- and H- polarizations at each pixel). Finally, intercalibration instrumental drifts due to in-orbit temperature variations are analyzed using a standard configuration.

Two campaigns were conducted in October and November 2001 with the support of ESA to check the validity of existing emissivity models in L-band and to assess the accuracy which can be expected for sea surface salinity retrieved from the Soil Moisture and Ocean Salinity (SMOS) satellite measurements. Taking into account the influence of the various fetch conditions encountered during these campaigns, we study the influence of the sea state on emissivity. We use sea state measurements to estimate the wave age. Then we use a sea wave spectrum, which is wave age dependent, to compute brightness temperatures (Tb) with a two-scale emissivity model. We compare the simulated Tb with measurements and determine the validity of the sea state parameterization.

Two campaigns were conducted in November 2001 to measure the brightness temperature of the sea in L-band and the sea surface environmental parameters. The first results are presented below, especially an example of airborne radiometer measurements made during an abrupt change in the wind speed.

In May 1999 the SMOS Earth Explorer Opportunity mission was selected by ESA to provide global soil moisture and sea salinity maps using the MIRAS L-band aperture synthesis interferometric radiometer. At this frequency there is a high dynamic range of the soil moisture signature and surface roughness effects are small, as well as vegetation effects. However, they cannot be neglected at large observation angles or if the vegetation cover is thick and/or dense. In this paper we have carried out a series of simulations to investigate the effect of the soil moisture content and the vegetation density, the influence of the vegetation structure (number, sizes and orientation of branches and leaves) and the influence of each component (trunk, branches, and leaves) on the four Stokes elements J, Th, U and V.

This paper models and analyzes theoretically the swell effect in the polarimetric brightness temperature at L-band. A two-scale model has been implemented to compute the emissivity of the ocean surface characterized by a sea surface spectrum. The total spectrum is a combination of the wind generated spectrum and a low-frequency narrow-band Gaussian swell spectrum. The dependence of the predicted emissivity at L-band with swell parameters has been studied and the conclusions are enclosed in this paper.

In preparation of the SMOS mission, synthetic L-band brightness temperatures are produced at the global scale and soil moisture retrieval algorithms are assessed.

Sea surface salinity can be measured by passive microwave remote sensing at L-band. In May 1999, the European Space Agency (ESA) selected the Soil Moisture and Ocean Salinity (SMOS) Earth Explorer Opportunity Mission to provide global coverage of soil moisture and ocean salinity. To determine the effect of wind on the sea surface emissivity, ESA sponsored the Wind and Salinity Experiment (WISE 2000). This paper describes the field campaign, the measurements acquired with emphasis in the radiometric measurements at L-band, their comparison with numerical models, and the implications for the remote sensing of sea salinity.

Compared to synthetic aperture radars (SARs), the angular resolution of microwave radiometers is quite poor. Traditionally, it has been limited by the physical size of the antenna. However, the angular resolution can be improved by means of aperture synthesis interferometric techniques. A narrow beam is synthesized during the image formation processing of the cross-correlations measured at zero-lag between pairs of signals collected by an array of antennas. The angular resolution is then determined by the maximum antenna spacing normalized to the wavelength (baseline). The next step in improving the angular resolution is the Doppler-Radiometer, somehow related to the super-synthesis radiometers and the Radiometer-SAR. This paper presents the concept of a three-antenna Doppler-Radiometer for 2D imaging. The performance of this instrument is evaluated in terms of angular/spatial resolution and radiometric sensitivity, and an L-band illustrative example is presented.

The recent use of along-track interferometry (ATI) in synthetic aperture radar (SAR) has shown promise for synoptic measurement of ocean surface currents. ATI-SARs have been used to estimate wave fields, currents, and current features. This paper describes and analyzes a dual-beam along-track interferometer to provide spatially resolved vector surface velocity estimates with a single pass of an aircraft. The design employs a pair of interferometer beams, one squinted forward and one squinted aft. Each interferometric phase is sensitive to the component of surface Doppler velocity in the direction of the beam. Therefore, a proper combination of these measurements provides a vector surface velocity estimate in one pass of the aircraft. The authors find that precise measurements dictate widely spaced beams and that the spatial resolution for the squinted SAR is essentially identical to the sidelooking case. Practical instrument design issues are discussed, and an airborne system currently in development is described. Through computer simulation, they observe the azimuthal displacement of interferometric phases by moving surfaces identical to those of conventional SAR and find that such displacement can bias the estimated surface velocity.

The correlation between the visibility samples' noise of an aperture synthesis radiometer are required for the computation of the recovered temperature noise of a given pixel and of the improvement introduced by baseline redundance. A general expression for this correlation and noise examples for a linear array are presented.

Current spaceborne radiometers do not achieve the required spatial resolution demanded by the scientific community due to antenna-size technological limitations. In recent years, several space agencies have been studying aperture synthesis interferometric radiometers as a way of overcoming these limitations, which are more evident at low microwave frequencies (e.g., at L-band), where sea surface salinity and soil moisture can be monitored. Interference is an important issue in any remote sensing instrument, but it is crucial in microwave radiometers, since the signal being measured is the spontaneous thermal noise emission. Interference analyses already exist for classic radiometers. The objective of this paper is the analysis of RF interference on interferometric radiometers. The study involves the analysis of possible interference sources that may affect the performance of such systems at L-band: (1) nearby emissions from radars, non-Geo-Stationary Orbit (GSO) and Mobile Satellite Services (GSO-MSS), (2) harmonics of lower frequency emissions, and (3) possible jamming.

This paper is concerned with the radiometric sensitivity computation of an aperture synthesis interferometric radiometer devoted to Earth observation. The impact of system parameters and the use of simultaneous redundant measurements are analyzed. The interferometric radiometer uncertainty principle is presented; it quantifies the relationship between radiometric sensitivity and angular resolution.

In Earth observation programs there is a need of passive low frequency (L-band) measurements to monitor soil moisture and ocean salinity with high spatial resolution 10-20 km, a radiometric resolution of 1 K and a revisit time of 1-3 days. Compared to total power radiometers aperture synthesis interferometric radiometers are technologically attractive because of their reduced mass and hardware requirements. In this field it should be mentioned the one-dimensional (1D) linear interferometer ESTAR developed by NASA and MIRAS a two-dimensional (2D) Y-shaped interferometer currently under study by European Space Agency (ESA). Interferometer radiometers measure the correlation between pairs of nondirective antennas. Each complex correlation is a sample of the "visibility" function which, in the ideal case, is the spatial Fourier transform of the brightness temperature distribution. Since most receiver phase and amplitude errors can be hardware calibrated, Fourier based iterative inversion methods will be useful when antenna errors are small, their radiation voltage patterns are not too different, and mutual coupling is small. In order to minimize on-board hardware requirements-antennas, receivers and correlators-the choice of the interferometer array shape is of great importance since it determines the (u,v) sampling strategy and the minimum number of visibility samples required for a determined aliasing level. In this sense, Y-shaped and triangular-shaped arrays with equally spaced antennas are optimal. The main contribution of this paper is a technique that allows the authors to process the visibility samples over the hexagonal sampling grids given by Y-shaped and triangular-shaped arrays with standard rectangular FFT routines. Since no interpolation processes are involved, the risk of induced artifacts in the recovered brightness temperature over the wide held of view required in Earth observation missions is minimized and signal to noise ratio (SNR) is preserved.

On-board calibration of bidimensional aperture synthesis radiometers with a large number of antennas by the standard correlated noise injection method is technologically very critical because of the stringent requirements on mass, volume, and phase equalization of the noise distribution network. A novel approach, which makes use of a set of uncorrelated noise sources uniformly distributed in the array, is proposed. Each noise source drives correlated noise only to a small set of adjacent antennas. These sets of antennas are overlapped in order to maintain phase and modulus track along the array. This approach reduces drastically mass and volume of the noise distribution network. Moreover, its phase matching requirement is strongly relaxed because it is only necessary within small sets of adjacent antennas. Power stability of the uncorrelated noise sources is also not a stringent requirement. This procedure allows independent phase and modulus calibration by making use of a reduced number of redundant correlations.