The potential for applying in situ multispectralRrsdata from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) to validate satellite derived ocean color hyperspectralRrsproducts was investigated in the 400-700 nm interval. The analysis was performed using a comprehensive data set of simulated hyperspectralRrsin combination with an algorithm designed to re-construct hyperspectralRrsfrom multispectral ones. Results were assessed using in situ hyperspectralRrsrepresentative of diverse water types. Excluding waters dominated by a high concentration of colored dissolved organic matter, results indicate the capability of determining hyperspectralRrsfrom AERONET-OC multispectral data with mean relative and absolute uncertainties generally lower than 2% and 5 × 10−5sr−1, respectively, at a number of the key center-wavelengths of the Ocean Color Instrument (OCI) onboard the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE).

Marine data products from the Ocean and Land Color Instrument (OLCI-A), operated onboard the Copernicus Sentinel 3A satellite, were assessed using in situ reference measurements representative of various European Seas. Results from the evaluation of inherent optical properties indicated substantial accuracy for the satellite derived absorption coefficient of pigmented particlesaOLCI-Aph(λ), the diffuse attenuation coefficient of downward irradianceKOLCI-Ad(λ) and the back-scattering coefficient of marine particlesbOLCI-Abp(λ). Conversely, large underestimates were shown for the absorption coefficient of non-water constituentsaOLCI-Anw(λ) and the cumulative absorption coefficient of colored dissolved organic matter and of non-pigmented particlesaOLCI-Adg(λ).

Radiometric water products from the neural network (NNv2) in the alternative atmospheric correction (AAC) processing chain of Ocean and Land Colour Instrument (OLCI) data were assessed over different marine regions. These products, not included among the operational ones, were custom-produced from Copernicus Sentinel-3 OLCI Baseline Collection 3. The assessment benefitted ofin situreference data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) from sites representative of different water types. These included clear waters in the Western Mediterranean Sea, optically complex waters characterized by varying concentrations of total suspended matter and chromophoric dissolved organic matter (CDOM) in the northern Adriatic Sea, and optically complex waters characterized by very high concentrations of CDOM in the Baltic Sea. The comparison of the water-leaving radiances$L_{\text {WN}}(\lambda)$derived from OLCI data on board Sentinel-3A and Sentinel-3B with those from AERONET-OC confirmed consistency between the products from the two satellite sensors. However, the accuracy of satellite data products exhibited dependence on the water type. A general underestimate of${L}_{\text {WN}}(\lambda)$was observed for clear waters. Conversely, overestimates were observed for data products from optically complex waters with the worst results obtained for CDOM-dominated waters. These findings suggest caution in exploiting NNv2 radiometric products, especially for highly absorbing and clear waters.

The quantification of uncertainties affecting satellite ocean color products is a fundamental step to ensure their compliance with mission and science requirements. This work investigated a methodology relying on the use ofin situradiometric data with known uncertainties to determine those affecting matching satellite data. By exploitingin situradiometric data from the Ocean Color component of the Aerosol Robotic Network (AERONET-OC), an advanced method was applied to radiometric data products from the Ocean and Land Color Instruments onboard the Sentinel-3A satellite (OLCI-A) and the Visible Infrared Imager Radiometer Suite onboard the Suomi National-Polar Orbiting Partnership satellite (VIIRS-S). The results from the analysis support the relevance of the method proposed.

Remotely sensed measurements acquired by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite are processed in a uniform equal-area grid, the Icosahedral Snyder Equal Area (ISEA) 4H9. Brightness temperature measurements are projected onto that grid (the so-called Level 1C), as well as sea surface salinity and soil moisture estimates (Level 2). The ISEA grid has been chosen for its characteristics of equal area and almost uniform intercell spacing. Nevertheless, when considering the SMOS viewing geometry, the measurement footprint size, and the processing applied to those measurements, this choice may be revisited. With this objective, the ISEA 4H9 grid is compared to other equal-area grids with different sizes and orientations with respect to the satellite track. The best configuration resulted to be a 25-km-width grid symmetrical with respect to satellite track. This grid appeared to be better suited for improving SMOS Level 2 retrieval algorithms as well as to serve as input for higher level data production, since it best accounts for the instrument's viewing geometry and substantially reduces the correlation between adjacent grid cells.

The European Space Agency Soil Moisture and Ocean Salinity mission aims at estimating, over the oceans, sea surface salinity (SSS) with spatial and temporal coverage adequate for large-scale oceanography. Spatiotemporal averaging of the retrieved SSS [level-3 (L3) product] has to be properly performed in order to meet the challenging mission requirements. At high latitudes, the generally low sea surface temperature (SST) characterizing the ocean degrades the brightness temperature sensitivity to SSS, but conversely, an improvement in the L3 retrieved SSS performances should be expected due to an increased pixel sampling. This tradeoff between geophysical effects in cold seawater and the concomitant temporal oversampling has been addressed by analyzing the latitudinal trend of the retrieved salinity performances, in various retrieval configurations and settings, once a conservative and optimal data filtering strategy is applied. Quantitative rate of changes of the SSS retrieval performance with the SST variability is provided, together with the net oversampling contribution to the L3 SSS accuracy. The experiments carried out demonstrate that the high-latitude oversampling does not compensate for the SST-driven latitudinal degradation of the L3 SSS product quality.

This paper describes the first outcomes of an activity aiming at validating the H-SAF soil moisture products derived from Metop-ASCAT data. For this purpose, an extensive comparison between SMOS and ASCAT derived soil moisture retrievals has been accomplished by considering the 25 km resolution ASCAT products and the SMOS L2 products. Both Europe and Northern Africa have been considered and data acquired during 2010 have been used. The procedure that has been followed to accomplish the comparison is described together with the first results. The way the ASCAT soil moisture relative index has been converted into a volumetric moisture content, which represents a critical aspect of the comparison, is also described. Results have demonstrated that, after the conversion of the H-SAF estimates into absolute volumetric soil moisture, the two products show a relatively good degree of correlation. Additional factors, such as spatial property features are also preliminary investigated.

ESA declared the end of the EnviSat mission on 9thMay 2012, after 10 years of life (doubling the initial expected 5 years of operations). Monitoring of Near Real Time (NRT) data started in 2003 to assess the quality of measurement and calibration of the EnviSat Radar Altimeter instrument. Since then, NRT data has been stored to build a comprehensive database of altimeter parameters that can be now used not only to assess the instrument calibration and the processing data quality, but also as a starting point for defining specific performance objectives for NRT data for future altimeter missions.

A preliminary attempt of deriving a purely satellite-based Temperature-Salinity (T-S) diagram is presented, with the overall aim of assessing to what extent is possible, and in which geographical areas, to identify and trace water masses by satellite. This has been performed by using recent SMOS and Aquarius satellite SSS products in conjunction with spaceborne SST data. A baseline T-S diagram is arranged from climatology data, differentiating 7 ocean zones and mapping them into the T-S domain. Therefore, a comparison with satellite data is carried out, highlighting, for this preliminary test, which are the most challenging zones and where, in turn, they mutually agree in a reasonable way.

The importance of long-term, continuous, and homogenous time-series of satellite data is widely accepted and strongly fostered by the international scientific community. The various global projects and initiatives undertaken in the last few years are evidences of that effort. Among those are: the Long Term Data Preservation Working Group [1], the Permanent Access to the Records of Science in Europe (PARSE) [2], or the Global Climate Observing System (GCOS) [3]. One of the examples of long-term monitored variable is the wind vector. Since the European Remote-sensing Satellite (ERS)-1 launch in July 1991 and until ERS-2 decommissioning in July 2011, a continuous and consistent database of backscattering signal from the Earth surface has been built, and is now available. The Active Microwave Instrument (AMI) [4], which was one of the ERS-1 and ERS-2 payloads, provided radar backscattering coefficient measurements during the last 20 years by using its three nominal operational acquisition modes: Synthetic Aperture mode (SAR mode), Scatterometer mode (wind mode) and a special combination of the two over ocean where SAR and Scatterometer mode are interleaved (wind/wave mode). The main applications for data acquired in Scatterometer mode is related to the estimation of the wind vector over the sea surface. In that field the ERS-2 Scatterometer measurements give a very valuable contribution to the accuracy of the numerical weather forecast models, being assimilated in several meteorological weather forecast centers since the beginning of the mission. After the decommissioning of ERS-2, effort has been devoted to achieve a complete reprocessed database, including both ERS-1 and ERS-2 acquisitions [5]. The cross-calibration between these two satellites is a crucial task to obtain the homogeneousness of the wind vector database, and allow its long-term characterization. The approach followed by ESA in term of team organization, cross-calibration strategy and validation methodology towards this goal is presented in this paper as well as the preliminary results of the long-term characterization of the wind vector.

This paper presents an overview of the evolution of the European Remote-sensing Satellite (ERS)-2 scatterometer mission during the last 16 years, highlighting the changes in both satellite configuration and on-ground data processing algorithm. Instrument and on-ground data processor performances and evolutions are analyzed and commented; finally, future developments are emphasized. ERS-2 was launched in 1995 by the European Space Agency (ESA). Since then, the active microwave instrument, which is one of the ERS-2 payloads, is providing radar backscattering coefficient measurements by using its three nominal operational acquisition mode: synthetic aperture mode (SAR mode), scatterometer mode (wind mode), and a special combination of the two over ocean where SAR and scatterometer mode are interleaved (wind/wave mode). The main applications for data acquired in scatterometer mode are related to the estimation of the wind vector over the sea surface. In that field, the ERS-2 scatterometer measurements give a very valuable contribution to the accuracy of the numerical weather forecast models, being assimilated in several meteorological weather forecast centers since the beginning of the mission. Other applications of the ERS-2 scatterometer data are over land to retrieve information about the soil water content and over the sea-ice. A constant monitoring of the scatterometer performances is carried out since the beginning of the mission by ESA engineering teams located in ESTEC and ESRIN and the instrument manufacture (Dornier at launch time), in collaboration with several European research institutions, as the European Centre for Medium-range Weather Forecasts for product geophysical validation, the Belgian Royal Military Academy for data processing and calibration during the zero-gyro phase, and industrial partners, as Serco SpA for the routine data quality control activities since the beginning of operational phase. Results show outstanding performances even after the failure of several hardware components that has been properly compensated on-ground with evolution of the processor, and many years of operation, which permits the creation of a homogeneous database of wind vectors for the last 16 years (20 years if the ERS-1 mission is considered), in accordance with Global Climate Observing System recommendations.

SMOS salinity inversion consists of minimizing the residual between measured and modeled brightness temperatures. The minimization procedure is a great challenge and crucial step, but its success depends on the quality of the forward model. Consequently, we present an empirical update of pre-launch L-band emissivity forward models, where the essential improvement is related to the emissivity by a rough sea surface. The improvement is quantified in terms of retrieved salinity accuracy compared to the climatology.

The Soil Moisture and Ocean Salinity (SMOS) mission launched in November 2009 will provide, for the first time, satellite observations of sea surface salinity (SSS). At level 3 (L3) of the SMOS processing chain, the large amount of SSS data obtained by the satellite will be summarized in gridded products with the aim of synthesizing the information and reducing the error of individual SSS observations. In this paper, we present the algorithm adopted by the CP34 SMOS processing center to generate the SMOS L3 products and discuss the choices adopted. The algorithm is based on optimal statistical interpolation. This method needs the following: 1) the prescription of a background field; 2) a prefiltering procedure to reduce the data set size; 3) the definition of a suitable correlation model; and 4) the characterization of the observational error statistics. For the present initial stage, a monthly climatology is chosen as the best background field. The spatiotemporal correlations between the departures from the climatology are described using a bivariate Gaussian function. The correlation model parameters are obtained by fitting the function to the realistic ocean model data. The sensitivity experiments show that an accurate correlation model that permits local variations in the correlation parameters is the best option. The observational error statistics (bias, variance, and correlation) are addressed from the results of the SMOS level-2 processor simulator. Finally, several sensitivity experiments show that a bad prescription of observational errors in the L3 algorithm does result in a dramatic impact on the generation of L3 products.

SMOS (Soil Moisture and Ocean Salinity), launched in November 2, 2009 is the first satellite mission addressing the salinity measurement from space through the use of MIRAS (Microwave Imaging Radiometer with Aperture Synthesis), a new two-dimensional interferometer designed by the European Space Agency (ESA) and operating at L-band. This paper presents a summary of the sea surface salinity retrieval approach implemented in SMOS, as well as first results obtained after completing the mission commissioning phase in May 2010. A large number of papers have been published about salinity remote sensing and its implementation in the SMOS mission. An extensive list of references is provided here, many authored by the SMOS ocean salinity team, with emphasis on the different physical processes that have been considered in the SMOS salinity retrieval algorithm.

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

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

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.

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.

As part of the preparation for the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite mission, empirical sea-surface emissivity (forward) models have been used to retrieve sea-surface salinity from L-band brightness-temperature (TB) measurements. However, the salinity inversion is not straightforward, and substantial effort is required to define the most appropriate cost function. Various Bayesian-based configurations of the cost function are examined, depending on whetheraprioriinformation is used in the inversion. A sensitivity analysis ofTBto several geophysical parameters has been performed and has shown that the instrument has low sensitivity to the parameters that modulate theTB(including salinity). The SMOS end-to-end simulator is used to test the accuracy of different cost-function configurations. Currently, the general opinion in the SMOS community is that a partially constrained cost function, in which the salinity constraint is effectively removed, is the most appropriate for salinity retrieval. The purpose of this letter is to show that we found no evidence that such a configuration performs better than a fully constrained or a nonconstrained one. Moreover, in contrast to previous results, we found that the fully constrained inversion does not converge to the reference or auxiliary salinity value and produces the most accurate salinity retrievals of the tested configurations. Therefore, such a configuration should not be disregarded for future tests.

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.

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 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.

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.

The L-band interferometric radiometer onboard the Soil Moisture and Ocean Salinity mission will measure polarized brightness temperatures (Tb). The measurements are affected by strong radiometric noise. However, during a satellite overpass, numerous measurements are acquired at various incidence angles at the same location on the Earth's surface. The sea surface salinity (SSS) retrieval algorithm implemented in the Level 2 Salinity Prototype Processor (L2SPP) is based on an iterative inversion method that minimizes the differences between Tb measured at different incidence angles and Tb simulated by a full forward model. The iterative method is initialized with a first-guess surface salinity that is iteratively modified until an optimal fit between the forward model and the measurements is obtained. The forward model takes into account atmospheric emission and absorption, ionospheric effects (Faraday rotation), scattering of celestial radiation by the rough ocean surface, and rough sea surface emission as approximated by one of three models. Potential degradation of the retrieval results is indicated through a flagging strategy. We present results of tests of the L2SPP involving horizontally uniform scenes with no disturbing factors (such as sun glint or land proximity) other than wind-induced surface roughness. Regardless of the roughness model used, the error on the retrieved SSS depends on the location within the swath and ranges from 0.5 psu at the center of the swath to 1.7 psu at the edge, at 35 psu and 15degC. Dual-polarization (DP) mode provides a better correction for wind-speed (WS) biases than pseudofirst Stokes mode (ST1). For a WS bias of -1 mmiddots-1, the corresponding SSS bias at the center of the swath is equal to -0.3 psu in DP mode and to -0.5 psu in ST1 mode. The inversion methodology implicitly assumes that WS errors follow a Gaussian distribution, even though these errors should follow more closely a Rayleigh distribution. For this reason, the use of wind components, which typically exhibit Gaussian error distributions, may be preferred in the retrieval. However, the use of noisy wind components creates WS and SSS biases at low WSs (0.1 psu at 3 mmiddots-1). At a sea surface temperature (SST) of 15degC, the retrieved SSS is weakly sensitive to the SST biases, with the SSS bias always lower than 0.3 psu for SST biases ranging from -0.5degC to -2degC. In DP mode, biases in the vertical total electron content (TEC) of the atmosphere result in SSS biases smaller than 0.2 psu. The pseudofirst Stokes mode is insensitive to TEC. Failure to fully account for sea surface roughness scattering effects in the computation of sky radiation contribution leads to a maximum SSS bias of 0.2 psu in the selected configuration, i.e., a descending orbit over the Northern Pacific in February. To achieve SSS biases that are smaller than 0.2 psu, special care must be taken to correct for biases at low WS and to ensure that the bias on the mean WS (averaged over 200 km times 200 km and ten days) remains smaller than 0.5 mmiddots-1.

As part of the preparation for the European Space Agency SMOS (soil moisture and ocean salinity) satellite mission, empirical sea surface emissivity (forward) models have been applied to retrieve sea surface salinity from L-band brightness temperature (TB) measurements. However, the salinity inversion is not straightforward and an important effort is required to define the most appropriate cost function (inversion algorithm). Different Bayesian-based configurations of the cost function are examined, depending on whether prior information is used in the inversion or not. It is important to properly balance all the terms of the cost function, as well as to have a good knowledge of the quality of the prior information. A sensitivity analysis shows that the instrument has low sensitivity to the geophysical parameters that modulate the Tb (including salinity). As such, the inversion needs to be constrained with prior information. Simulations are also performed using the SMOS simulator to assess the retrieval errors produced by the different cost function configurations. In line with the sensitivity analysis, the errors are very large when no prior information is used in the cost function. The lowest errors are obtained when the inversion is constrained with the full prior information, i.e., information from all the auxiliary (geophysical) parameters. As such, it is concluded that the use of prior information is essential for a successful salinity retrieval from SMOS measurements.

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

The Soil Moisture and Ocean Salinity (SMOS) mission (launch scheduled for 2008) aims at obtaining global maps of soil moisture and sea surface salinity (SSS). It uses an L-band (1.4 GHz) microwave interferometric radiometer to obtain brightness temperatures (Tb) at the Earth surface at horizontal and vertical polarizations. They will be used to retrieve both geophysical variables, following specifically designed algorithms that will be applied when the satellite field-of-view is covering land or ocean surfaces respectively. The retrieval of salinity is a complex process that requires the knowledge of environmental information and an accurate processing of the radiometer measurements, because of the narrow range of ocean Tb and the strong impact on the measures of geophysical parameters (such as sea state). Here we present the baseline approach chosen to retrieve sea surface salinity from SMOS data, as developed and implemented by the joint team of scientists and engineers responsible for the SMOS Salinity Level 2 Prototype Processor. We present academic tests conducted over homogeneous scenes with the prototype. In these configurations, external perturbation sources (sky radiation, sun glint, ...) are not taken into account. Roughness is the main sea surface signal disturbing SSS retrieval.

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.