The sensitivity of Global Navigation Satellite System Reflectometry (GNSS-R) to ocean surface roughness is well-established within the scientific and technological communities. Missions like the CYclone GNSS (CYGNSS) have been specifically designed to detect and monitor hurricanes and other oceanic storms. Additionally, data from the Soil Moisture Active Passive (SMAP) mission’s radar, operating in receive mode, known as SMAP-Reflectometer or SMAP-R, provide opportunities to evaluate the polarimetric sensitivities of GNSS-R measurements to ocean surface roughness. This enables exploration of potential applications that can be achieved or enhanced through the use of polarimetric techniques. This study provides a sensitivity analysis of wind-driven changes in surface roughness using a comprehensive view of full polarization GNSS-R signals over the ocean. The results show an improvement of about 0.6% on ocean wind speed retrievals for dual polarization GNSS-R missions respect to single polarization GNSS-R missions. The results show an improvement of about 4.5% on ocean wind speed retrievals for full polarization GNSS-R missions respect to single polarization GNSS-R missions.

The EZIE mission is a first of its kind to measure the temporal and spatial characteristics of Earth’s ionospheric auroral electrojet currents remotely using a mm-wave radiometer called the Microwave Electrojet Magnetogram (MEM). EZIE measures the 118 GHz oxygen emission line that splits in frequency in the presence of a magnetic field. This effect is known as the Zeeman effect. From these measurements of the 50 km spatial resolution magnetic fields at 80 km altitude the ionospheric currents that caused them can be calculated. The EZIE mission consists of three MEM payloads on three spacecrafts, each MEM contains four polarimetric radiometer receivers with a polyphase filter bank spectrometer. MEM can indirectly measure magnetic field strength and direction. In this paper we present the unique calibration design of the MEM payload that does not include any internal or external calibration sources. We discuss the MEM payload and present results from pre-launch testing and calibration of the MEM system.

L-band radiometry for retrieving surface soil moisture is challenged by the confounding influence of surface roughness scattering and vegetation attenuation. This study presents a novel retrieval framework that uses GNSS reflectometry (GNSS-R) coherence time metrics from Soil Moisture Active Passive-Reflectometry (SMAP-R) data to characterize global surface roughness and vegetation parameters. By analyzing the decay of signal-to-noise ratio (SNR) across varying coherent integration times, we estimate temporal coherence time (τc) and derive surface roughness (σ) without ancillary topographic data. A polarization mixing parameter (Pmix), derived from the degree of polarization (DoP), is related to the polarization decoupling factor (Q) used in SMAP models. These variables feed into a τ−ω radiative transfer model to retrieve vegetation optical depth (τ) and single-scattering albedo (ω) directly from observations. The resulting global maps of σ,Q,τ, and ω show enhanced spatial structure and biome sensitivity. They can constrain the empirical SMAP static ancillary inputs used now for constraining the inversion of L-band brightness temperatures for soil moisture. To evaluate the impact of the new vegetation parameters, we apply a neural network trained on SMAP data to retrieve soil moisture using both the original and derived VOD inputs. Differences in soil moisture are spatially coherent and physically consistent, with lower values in high-biomass regions and improved retrieval in arid zones. This work demonstrates that GNSS-R coherence metrics can be integrated with L-band radiometry to produce observation-driven land surface parameters. The approach reduces dependence on empirical or static climatological inputs and supports improved soil moisture retrievals from passive microwave missions.

This manuscript explores the use of Global Navigation Satellite System–Reflectometry (GNSS-R) data collected by the Soil Moisture Active Passive (SMAP) mission to estimate antenna view angle offsets. A novel methodology for estimating antenna view angle offsets using GNSS-R data is proposed by comparing the received signal-to-noise ratio (SNR) of the reflected GNSS signal with SMAP’s antenna pattern measured prior to launch. To properly compare them, observation angles are defined with respect to the SMAP position and the specular point position, which are computed from the SMAP telemetry and the Global Positioning System (GPS) ephemeris data using a 1-km resolution digital elevation model (DEM) map. A methodology based on a second-order polynomial fit and a linear fit is proposed to estimate the offset by azimuthal sector and at different times of the year. Results are obtained for a total of six years, showing a consistent dependence on the antenna azimuthal look angle and on the time of the year, with an average antenna offset of 0.2° with a standard deviation of 0.06°, which produces a geolocation error of ~3.6 km. Further analysis proposes a nonlinear model to estimate the antenna offset directly from an azimuthal look angle and the time of the year. Model results show a Pearson correlation coefficient of$R$= 0.85 and$R$= 0.80 for descending and ascending passes, respectively. This model can be used by the SMAP team to further correct the antenna footprint location and pointing angle.

This article presents the mathematical background and modeling for full-polarimetric Global Navigation Satellite System Reflectometry (GNSS-R) receivers for nonnegligible cross-polar component in the scattered signal. A signal model is presented to retrieve the Stokes parameters using the Mironov model to estimate the soil surface’s dielectric constant. This article compares data collected by the SMAP-Reflectometry (SMAP-R) receiver and the proposed signal model, emphasizing the need to consider the cross-polar component ($S_{\mathrm {hv}}$). Simulations obtained without considering the cross-polar component have poor agreement in all Stokes parameters. A model is implemented using a complex cross-polar component resulting in notable improvements in the bias and unbiased root mean square difference (ubRMSD) between the modeled and measured SMAP-R Stokes parameters. Furthermore, a methodology is proposed for estimating the$S_{\mathrm {hv}}$component using SMAP-R and a soil moisture (SM) reference dataset. An analysis of$S_{\mathrm {hv}}$reveals a moderate correlation with vegetation water content (VWC) and surface roughness ($\sigma _{\mathrm {slp}}$). We developed an SM model by estimating$S_{\mathrm {hv}}$using both VWC and$\sigma _{\mathrm {slp}}$, showing the potential of full-polarimetric GNSS-R to provide SM estimates upon proper characterization of the cross-polar component. Results show the ubRMSD of 0.09 m3/m3 with respect to the in situ soil moisture network (ISMN). Finally, this study establishes that an RMSD better than 0.02 when estimating$S_{\mathrm {hv}}$is required for an SM product accuracy better than 0.07 m3/m3 for polarimetric GNSS-R SM retrievals.

The shift between frozen and thawed conditions on the Earth’s surface influences climate, hydrology, and ecology. A primary objective of the Soil Moisture Active Passive (SMAP) mission is to estimate the surface binary freeze/thaw (F/T) state for the northern hemisphere above 45° N latitude with a classification accuracy of 80% at 3-km spatial resolution and 2-day intervals. The objective was to be accomplished by the SMAP L-band radar measurements. In July 2015 the radar transmitter suffered an anomaly that prevented it from nominal operation. The mission partially met the goal exceeding the targeted 80% accuracy but at 36 km resolution. After the radar anomaly, the SMAP mission switched the radar receiver band pass filter frequency to GPS L2c, acting as a full-polarimetric Global Navigation Satellite System – Reflectometry (GNSS-R) receiver, known as SMAP-Reflectometer (SMAP-R). This work focusses on using SMAP-R signals to classify the F/T state over the Northern latitudes, within the limitations of the dataset. An algorithm based on the seasonal threshold approach that was originally envisioned for the SMAP radar is applied to the SMAP-R data (i.e., bistatic radar). Then the algorithm is evolved using a Random Forest algorithm to aid threshold selection from the discriminator built in the seasonal threshold approach. This algorithm is applied for years 2016 to 2022 over the Northern Hemisphere terrestrial cryosphere and shows an F/T classification accuracy agreement better than 97% with respect to the classification of the official SMAP F/T Radiometry product, proving the potential of polarimetric GNSSR to derive F/T.

Polarimetric Global Navigation Satellite System-Reflectometry (GNSS-R) is the next natural step for land monitoring using GNSS signals. The Soil Moisture Active Passive (SMAP) radar receiver has been retrieving polarimetric GNSS-R data using two orthogonal linearly polarized antennas since 2015, enabling the study of the polarimetric signature of GNSS-R signals on different Earth’s surfaces. Currently, new instruments and missions are using circularly polarized antennas to retrieve polarimetric GNSS-R information. In this manuscript, synthetic right and left-hand circularly polarized signals are reconstructed using the Stokes parameters of the SMAP L2C GNSS-R data. The signal-to-noise ratio (SNR) of the SMAP-R data at the equivalent RHCP, LHCP, H, and V polarization antennas is retrieved, and normalized to an arbitrary receiver with a noise figure of 2 dB. Appling the Albersheim model, we analyze the probability of detecting a reflection in a 0.5° Lat/Lon box. Results are presented for different configurations of coherent and incoherent integration times and antenna gains, for each possible antenna polarization. We present different receiver configurations capable of detecting more than 70% and 90% GNSS reflections over land. Results show that with a 10-dB antenna and a receiver with a coherent integration time of 4 ms, and an incoherent integration time of 1000 ms would suffice to detect 19.4%, 92.3%, 83.5%, and 79.4% for RHCP, LHCP, H-polarized, and V-polarized antenna, respectively. Detectability improves up to 57.4%, 99.3%, 96.3%, and 96.6% using a 14-dB antenna. Results are then generalized to L1 C/A GNSS-R signals.

Polarimetric Global Navigation Satellite System - Reflectometry (GNSS-R) is being proposed by different teams as a solution to directly estimate soil moisture. Up to date, two approaches using polarimetric GNSS-R have been proposed to estimate soil moisture, using the ratio between the right-hand and left-hand circularly polarized received signals, and using the ratio between the linear horizontal and vertical components at a set of incidence angles, known as Hybrid Compact Polarimetric (HCP) GNSS-R. In this manuscript, the necessary calibrations of a received HCP GNSS-R signal are presented. The Stokes parameters of the signal are required to compensate all non-idealities. A methodology to calibrate the receiver effects, scene-antenna polarimetric misalignment, Faraday rotation, and transmitter non-idealities is proposed. Finally, the calibration performance is evaluated using several statistical parameters and polarimetric ratio models based on two different soil moisture products. Results show a correlation coefficient increase of 4.7% with respect to the model derived from the Soil Moisture Active Passive (SMAP) soil moisture product, and a 6.5% improvement with respect to the model derived from the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA-5) monthly average soil moisture product. Furthermore, the proposed calibrations show an unbiased root-mean-square error reduction of 6.3% and 6.8% for the SMAP soil moisture model and the ERA-5 model, respectively. Due to the SMAP antenna and pointing design, SMAP-Reflectometry (SMAP-R) is implicitly insensitive to some of the corrections. More significant corrections should be expected for future polarimetric GNSS-R instruments as European Space Agency (ESA) HydroGNSS.

This manuscript provides the first Hybrid Compact Polarimetric (HCP) Global Navigation Satellite System – Reflectometry (GNSS-R) analysis of the cryosphere. By reconstructing the full-Stokes parameters and the child Stokes parameters from the reflectometry signals collected by the SMAP radar receiver, the sensitivity to the geophysical changes of the Earth’s landscapes of these opportunistic signals are analyzed. This analysis is focused over the Artic Sea ice formation zone, the northern latitudes presenting freeze and thawed transitions, and the Greenland ice sheet. We conduct a sensitivity analysis for each landscape. The signals analyzed over the Arctic Sea show correlation with sea ice concentration (SIC) of 0.77 for the second Stokes parameterS1and 0.7 for the child Stokes parameter χ, which describes the ellipticity of the wave. The signals analyzed over the northern latitudes present a sensitivity to the two states (frozen and thawed). The analysis over Alaskan landscape shows a total reflectivity Γ0difference between freeze and thaw states of ~8 dB with a standard deviation of ~1 dB. Greenland presents a challenge since available datasets are not from the same timeframe, despite of this our initial evaluation shows that there is a ~0.5 correlation between ice thickness and the third Stokes parameterS3and the relative phase δ between the two linear electromagnetic components, h-pol and v-pol. This manuscript provides an extensive view of the HCP GNSS-R signals over the cryosphere and finds initial sensitivities to the seasonal geophysical changes of the different areas.

The Smart Ice Cloud Sensing (SMICES) is an active/passive sensor. SMICES is sponsored by NASA Earth Science Technology Office (ESTO) under Instrument Incubator Program 19 (IIP-19) awarded to Northrop Grumman Corporation (NGC) and Jet Propulsion Laboratory (JPL). The instrument is designed to measure upper tropospheric and lower stratospheric cloud ice and water vapor. SMICES uses a suite of passive radiometers that are constantly conically scanning to locate ice clouds. The ice clouds are located using an artificial intelligence controller that identifies key labels related to the ice cloud. Once an ice cloud is identified, the artificial intelligence controller activates and targets the on-board radar. While the SMICES instrument is currently being developed for an airborne demonstration, the final goal is to deploy it as a small satellite (SmallSat) instrument in low-Earth orbit (LEO). The onboard AI controller will significantly reduce DC power consumption of the satellite mission. This will enable the SMICES system to be hosted on a smaller platform with fewer solar cells and significantly drive down mission costs while maintaining the quality of scientific data. This work presents the latest development on the SMICES microwave radiometer.

The Soil Moisture Active Passive - Reflectometry (SMAPR) is the first polarimetric L2C GNSS-R dataset collected from space. Given that the dataset consists of I/Q raw data, different processing techniques can be applied. In this paper we explore the calculation of the full polarization Stokes derived from the horizontal (H) and vertical (V) polarization measurements of the transmitted right-hand circularly polarized (RHCP) Global Positioning System (GPS) signal. This configuration is known as hybrid compact polarimetry (HCP) radar, in this case HCP of a bistatic radar or a GNSSR instrument. This work describes how to use the collected data to obtain the full Stokes parameters and presents a glimpse to its value in the GNSS-R field, in particular the value of the phase of the cross-correlation signal between the H and V polarization measurements.

Global Navigation Satellite System - Reflectometry has shown promising results to retrieve land-related parameters, such as soil moisture. Polarimetric radar measurements offer additional information about the scattering scenario. SMAP-R is the first polarimetric GNSS-R dataset collected from the space thanks to the reception of the Horizontal and Vertical components of an incident circular polarized signal, allowing the retrieval of the full-Stokes parameter of the reflection. The reflectivity derived from the first Stokes bistatic radar cross section is compared to the SMAP brightness temperature, showing their dependence with vegetated areas by means of a linear equation. Finally, a polarimetry analysis is conducted proposing different polarimetric ratios derived from SMAP-R Stokes parameters, and the MODIS-derived vegetation water content.

SMAP-R is the first polarimetric GNSS-R dataset collected from the space. The reception of the Horizontal and Vertical components of an incident circular polarized signal allows to retrieve the Full-Stokes parameter of this hybrid compact polarimetry radar configuration. In this manuscript, the Stokes parameters of the reflection scenario are defined and calculated. The first Stokes is calibrated to retrieve the normalized bistatic radar cross section as main observable. The SMAP-R dataset is validated by collocating it to CYGNSS measurements. The results show a good agreement with CYGNSS normalized bistatic radar cross section, with an unbiased root-mean-square error of 3.3 dB for a 6 months period, showing also larger sensitivity over vegetated areas.

The Smart Ice Cloud Sensing (SMICES) instrument aims at providing onboard smart autonomous observation of upper tropospheric water vapor and ice particle size distribution in clouds at various local times. SMICES is an active/passive combined sensor with sounding channels at 380 GHz, radiometric channels at 250, 310 and 670 GHz, and a radar instrument operating at 239 GHz. A low-noise, low-power radiometer command and data handling (C&DH) subsystem has been designed to acquire the 24 analog radiometer channels and 8 analog thermistor data. A radiometric power regulation system provides the required power supplies for the other radiometric subsystems of the SMICES instrument. An on-board FPGA provides command and control of other instrument subsystems, performs synchronous data acquisition. The radiometer electronics are designed to fit into less than 2U horizontal dimensions of a CubeSat instrument. An AI controller unit directly interfacing with radar and radiometer C&DH subsystems performs on-board artificial intelligence operations for full system autonomy. The AI unit will control the radar instrument depending on the system health conditions, including the battery level, and based on the observed scene through the radiometer instrument.

The Soil Moisture Active Passive (SMAP) is capable of performing Global Navigation Satellite System Reflectometry (GNSS-R) measurements. The SMAP-R global dataset extends from July 2015 to present and covers latitudes up to ∼82°, which benefit multiyear cryosphere studies. In this paper we perform an initial evaluation of the freeze/thaw (F/T) state over Alaska and the sea ice presence over the Beaufort and Chukchi seas area for the periods January to March 2018 and July to September 2018. This paper investigates the sensitivity of the current fully-calibrated SMAP-R dataset to the changes in the cryosphere.

Shortly after its launch, the Soil Moisture Active Passive (SMAP) mission started using its radar receiver to collect Global Positioning System (GPS) bistatic radar measurements by switching the band-pass center frequency of its radar receiver to the GPS L2C band. We refer to this functionality of the SMAP mission as SMAP-Reflectometry or SMAP-R. The collected SMAP-R dataset represents the first polarimetric Global Navigation Satellite System Reflectometry (GNSS-R) dataset from space and brings unique capabilities as compared to other co-existing GNSS-R missions, i.e., CYclone Global Navigation Satellite System (CYGNSS). This paper formally introduces the SMAP-R dataset in its more current status and provides a wide overview of its science capabilities.

Three newly developed microwave and millimeter-wave radiometers which employ the pseudo-correlation architecture are discussed. This architecture-also described as the “continuous comparison” radiometer [1]-employs a hybrid power splitter to divide antenna and reference signals between two receiver chains which are later recombined by a second hybrid to again separate the antenna and reference signals. This scheme offers an alternative to the traditional Dicke switched radiometer design which otherwise places a reference switch between the antenna and receiver. We have applied the pseudo-correlation design to three unrelated radiometers to solve three (also unrelated) problems of (1) how to share telecommunication and radiometer functions in a single receiver without interrupting the communication channel with a Dicke switch; (2) how to maintain valid reference signals in the presence of strong radio interference; and (3) how to improve the noise figure of an internally calibrated millimeter-wave radiometer-by eliminating an electrically lossy Dicke switch.

A spaceborne P-band signals of opportunity synthetic aperture radar concept is proposed for the remote sensing of terrestrial snow. We have completed a performance analysis assuming a formation flight of 3 to 5 SmallSats on one orbit plane. The spacing between the SmallSats is chosen so that their ground tracks will be separated by 50 to 100 m to allow the use of interferometric synthetic aperture radar processing technique to obtain a spatial resolution of a few hundred meters. A point system design has been completed to determine the antenna concept and to indicate the dependence of spatial resolution and signal to noise ratio on the number of receivers. The performance for range delay determination was analyzed to assess the impact of various error sources, including instrument receiver noise and ionospheric delay. The dominant error source is the ionospheric delay, which will be corrected using the split-spectrum algorithm. Our overall error budget analysis indicates that an accuracy of about 3 cm for the snow water equivalent in dry snow and 5 cm for the snow depth of wet snow can be achieved.

Global Navigation Satellite System – Reflectometry (GNSS-R) is a promising field with a diverse set of applications. Polarimetric GNSS-R has been shown to be sensitive to different land parameters, such as freeze/thaw, crop growth, or soil moisture. In this manuscript, the polarimetric scenario of a GNSS reflection is defined, and the theoretical basis and the algorithm to retrieve the Stokes parameters of a GNSS-R signal is proposed. The presented algorithms are validated using data collected by the two linear orthogonal polarization channels of the SMAP radar antenna working as GNSS-R, known as SMAP-Reflectometry (SMAP-R). The Stokes parameter retrieval is presented for the SMAP-R case, and the receiver phase offset and intensity are calibrated. The same calibration procedure proposed by the CYclone GNSS (CYGNSS) mission is applied to the SMAP-R first Stokes parameter, allowing the retrieval of the normalized bistatic radar cross section of such measurement. Finally, the SMAP-R data is compared to the CYGNSS Normalized Bistatic Radar Cross Section (NBRCS) over the ocean, showing an unbiased root-mean-square error of 3.3 dB, and a small bias.

Calibration plays an important role in improving the accuracy of the microwave and millimeter-wave radiometric measurements. Several calibration techniques have been used in radiometers including external calibration targets, vicarious sources, and internal calibrators such as noise diodes or matched reference load. A new calibration technique based on deep learning has recently been developed to calibrate microwave and millimeter-wave radiometers. The deep-learning calibrator has been previously demonstrated on a computer noise-wave modeled Dicke-switching radiometer. This article applies the new deep-learning calibration technique for the calibration of the high-frequency airborne microwave and millimeter-wave radiometer (HAMMR) instrument. A deep-learning neural network model is built to calibrate the 2014 West Coast Flight Campaign antenna temperature measurements of the HAMMR. The deep-learning calibrator antenna temperature estimates are obtained from the radiometric measurements. The deep-learning calibration results are compared with the existing conventional calibration techniques used in HAMMR 2014 field campaign. The results have shown that the deep-learning calibrator is in agreement with the conventional calibration techniques. In this article, it is demonstrated that the deep-learning calibrator can be employed for calibrating the radiometers with high accuracy.

A new wideband radiometer covering P/L-band was developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The instrument was deployed on the US Coast Guard Cutter Healy for an Arctic Ocean research cruise from September 13, 2018 to October 20, 2018. This work shows the first results relating sea ice thickness obtained from the measurements taken with the wideband P/L-band radiometer during the campaign. Results from the Artic cruise campaign were also used to study wideband spectral properties of salinity. In addition to this paper, Salinity and wide-band calibration challenges are presented in two other companion papers.

A new ultra-wide P/L-band radiometer instrument has been developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The Arctic field campaign performed with the instrument deployed on the US Coast Guard Cutter Healy from September 13, 2018 to October 20, 2018. A new calibration strategy is developed for the ultra-wide band instrument to minimize the mismatch related effects. A noise-wave model is built to analyze and validate the instrument behavior for the calibration. The calibration strategy is analyzed using the results from the cruise campaign. Sea-ice thickness and sea surface salinity are presented in two other companion papers.

Deep learning artificial neural network techniques can be applied for on-orbit calibration of microwave and millimeter-wave radiometer spaceborne instruments, including those for small satellites. The noise-wave model has been employed for noise characterization and validation of the proposed deep learning calibration technique for a synthetically generated Dicke-switching radiometer. The developed deep learning neural network radiometer calibrator produces high accuracy estimates of antenna temperatures from the measurements of radiometer output voltage and thermistor readings. Tests with noise-free and noisy samples of the developed model have shown that the proposed calibration method does not add any significant noise to the radiometer calibration. The performance of the proposed method does not degrade with increased nonlinearity for a radiometer, while nonlinearity is a challenging issue for conventional calibration techniques. The deep learning calibration model learns the radiometer noise characteristics from radiometer prelaunch measurements during thermal vacuum chamber testing. The neural network calibrator proposed in this paper has self-learning capability during the on-orbit operation of a radiometer that can be used to improve the performance of on-orbit calibration. The proposed technique is demonstrated by comparing the residual uncertainty of the deep learning calibration with the theoretical value. No numerical study is presented to compare the performance with conventional calibration techniques. The new method may be solely applied to calibrate the radiometer or applied along with conventional calibration techniques.

The Tropospheric Water and Cloud ICE (TWICE) millimeter and sub-millimeter radiometer instrument is being developed to enable global observations of upper tropospheric/lower-stratospheric water vapor and ice particle size distribution in clouds. Global observations using the TWICE instrument are critically needed to reduce uncertainties in weather and climate models. A low-noise, power-efficient command and data handling (C&DH) subsystem has been designed and tested to control TWICE data acquisition and other subsystems. Considering the limited power resources available on such platforms, a highly-efficient power regulation board has been designed to minimize power losses and reduce system noise. Furthermore, heavy-ion radiation testing has been performed for some critical commercial-off-the-shelf components to analyze radiation tolerance in low-Earth orbit. The C&DH prototype board meets the functional, noise and size, weight and power (SWaP) requirements for deployment on a 6U -Class satellite.

Global measurements of upper tropospheric/lower-stratospheric water vapor and ice particle size distribution in clouds are critically needed to reduce uncertainties in global weather and climate models. To address this need, the conically scanning Tropospheric Water and Cloud ICE (TWICE) millimeter and submillimeter radiometer instrument is being developed. A low-noise, power-efficient command and data handling (C&DH) subsystem has been designed to control TWICE data acquisition and other subsystems. The C&DH prototype board meets functional, noise and size, weight and power (SWaP) requirements for deployment in a 6U-class satellite. Considering the limited power resources available on such platforms, a highly-efficient power regulation board has been designed to minimize power losses and reduce system noise. Furthermore, all of the components have been tested for radiation tolerance in low-Earth orbit.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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).

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.

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 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.

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.

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.

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.

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.

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.

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].