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.

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.

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.

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.

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.

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.

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.

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.

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.

Mosquitoes are the most important vectors of human diseases in tropical countries. In particular, Aedes aegypti is the main vector for Chikungunya, Dengue and Zika in South America and Asia. In recent years, transmission of these diseases has increased predominantly in urban areas and has become a major public health problem. This study presents an algorithm for dengue risk prediction in Brazil. It is based in machine learning techniques and uses environmental and socioeconomic variables to predict the risk of dengue propagation all over Brazil, at municipality level. Data from 2010 to 2013 of monthly dengue episodes distribution have been obtained from the Notifiable Diseases Information System (SINAN), developed by the Brazil ministry of health. In addition, environmental data had been obtained from the distributed products of earth observation satellite missions that acquire globally and periodically data. Three types of machine learning algorithms have been applied and compared. The best results have been obtained when using random forests.

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.

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.

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).

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.

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.

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.

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.

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.

L-band radiometry has been considered the preferred technique for global soil moisture (SM) remote sensing, as the Soil Moisture and Ocean Salinity (SMOS) and the Soil Moisture Active Passive (SMAP) missions proved. Owing the limited spatial resolution of current L-band radiometers, several downscaling algorithms have been developed to enhance the SMOS and SMAP SM resolutions. In this regard, the apparent thermal inertia (ATI) may be used for indirectly estimating SM, not only for disaggregation. In this study, l-km ATI-derived SM was obtained from March 31, 2015 to December 31, 2016, using Moderate Resolution Imaging Spectroradiometer (MODIS) as an alternative to L-band radiometry. The ATI-derived SM was validated over the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS). Comparisons with in situ SM showed slightly lower correlation (~0.62), but similar error (~0.043 m3·m-3) and lower bias (~0.027 m3·m-3) than the existing SMOS and SMAP products, whilst improving the resolution.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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 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 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.

The basis functions that are most commonly used in the method of moments (MoM) for the simulation of rough surface scattering have a small support in terms of the wavelength. Complementary, the density of points per wavelength in the surface mesh has to be high enough to guarantee a good accuracy and this implies a computational effort which can often be excessive. Several factors contribute directly to the number of unknowns of the matrix to be inverted in the MoM: the value of the permittivity for the medium below the surface for a dielectric case, its dimensionality and size and the incident or observation angle, depending on the active or passive character of the observables to be calculated. One way of overcoming the difficulty of the exponential growth of unknowns is to use basis functions with a global support over a region larger than a wavelength. If the basis functions are a good representation of the surface fields and currents, a fewer number of terms is needed to represent them than when using a basis of very localized functions. In this work we present an alternative to the classical method of moments with pulse functions and point matching, based on a spectral representation in terms of Bessel functions.

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 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).

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 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.

The Integral Equation Model (IEM) was developed originally by Fung and Pan (1986). The assumptions inherent to the model were amended in later publications but there were several important points that remained unclear and that propagated across all subsequent versions of the model. One of the authors of this paper proposed an improved IEM, called IEM2M (Alvarez-Perez, 2001), which removed these unnecessary assumptions and unified the small perturbation model (SPM) and the Kirchhoff approximation (KA) for bistatic scattering. This model was first criticized by Fung (2002) but later adopted by him and co-workers in 2003 and renamed as AIEM (advanced IEM). In contrast to other versions of IEM, IEM2M (integral equation model for second-order multiple scattering) is fully consistent with the SPM. This limit arises naturally from the second-order scattering terms when they produce an extra first-order contribution due to the local non-flatness, and therefore non-Kirchhoff character, of the surface. A central role in the IEM2M is played by a rigorous use of the Weyl representation of the retarded Green function through a homogeneous medium as well as a more physically based selection of the Fresnel coefficients. A further assessment of the model is its implementation for the problem of emissivity calculation. The difference between emissivity from a flat surface and a rough sea-like surface is very small and therefore demands an accurate description of the scattering coefficient, which must be integrated for all angles. Whereas an accuracy of 25% or 1 dB is sufficient for active remote sensing, passive remote sensing calculations require that energy conservation has to be typically within 0.3% error, which is the order of the aforementioned difference for flat and rough, sea-like surface emissivities.

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.

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.

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.

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

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 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.

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.

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.

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. In this paper we focus on ocean application of the SMOS mission. The brightness temperature of the sea surface with small or moderate roughness is computed with the IEM method, using an analytical n-dependent power-law spectrum that describes the sea-state conditions. Then, the accuracy of the brightness temperature computation with the number of terms of the n-power spectrum is determined. Furthermore, its variation with wind speed is also analyzed.

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).

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 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.

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.