This research focuses on the impact of Radio Frequency Interference (RFI) on the accuracy of Sea Surface Salinity (SSS) measurements obtained from the Soil Moisture Ocean Salinity (SMOS) mission. RFI can affect the accuracy of SSS measurements in regions that are crucial for understanding ocean dynamics and climate change. The extent of RFI contamination in SMOS SSS data varies based on the location of SSS across the swath. This variability is exploited in our study by comparing SSS fields constructed from different swath locations. We employ Principal Component Analysis (PCA) and regression techniques to correct RFI signatures in SMOS SSS data. The effectiveness of this correction is validated through comparison with independent SSS data derived from an in-situ dataset (global SSS field), and with RFI probability (CESBIO dataset). Our results show that this approach significantly improves the accuracy of SSS data in regions affected by RFI. In particular, the correction procedure is able to restore the SSS variability associated with El Nino Southern Oscillation (ENSO) using a method based solely on the anomalies of the SMOS measurements. We also explore two correction methods: a regional correction (RM) and a pointwise correction (PM). While PM allows for independent correction of RFI contamination at each location without needing prior information about the RFI source or affected area, RM is more effective in areas with high SSS variability. The potential for combining these two methods will be further discussed at the conference.

In the framework of the ESA regional initiative Arctic+ Salinity, a new Sea Surface Salinity (SSS) product (v4) has been developed from the Soil Moisture and Ocean Salinity (SMOS) measurements. The data processing starts from the SMOS Level 0 (L0) data and applies specific algorithms for reducing the contamination close to the ice edge at Level 1 (L1) (in the Brightness Temperature reconstruction) and at Level 2 (L2) (in the correction of biases in the salinity retrieval). In this work we explain these algorithms and present the new 13-year temporal series of SMOS SSS maps as well as the main results of the quality assessment.

The Soil Moisture and Ocean Salinity (SMOS) satellite mission, operational since 2010, relies on an L-Band microwave interferometric radiometer to generate brightness temperature images along the swath, with global coverage every 3 days. These images are then used to derive sea surface salinity (SSS) with an effective resolution of less than 50 km. However, signal acquisition in some ocean regions is intermittently and significantly disrupted by radio-frequency interferences (RFI) from various terrestrial military or civilian sources worldwide. We develop a new methodology based on principal component and regression analyses to extract the RFI signatures in time and space, thereby enabling the construction of a corrected SSS estimate along the swath. This method successfully filters out many disruptive features characterized by long and wide branches occurring around the RFI sources, hence recovering SSS variability as demonstrated in comparison to in situ reference data. This correction methodology is an alternative to separate filtering procedures that were applied on brightness temperature at Level 1. Independent information indicating the probability of RFI occurrence on land areas or nearby is used to verify the timing of oceanic RFI contamination inferred by the correction process. The methodology performs particularly well in areas where the probability is close to 1 for a significant and contiguous portion of the entire period. Already applied with significant improvement in three selected regions, this correction method is a starting point for expanding and systematizing the methodology to treat as many RFI-polluted regions as possible and to recover SMOS SSS variability.

The accuracy of the Sea Surface Salinity (SSS) retrieved from L-Band radiometer measurements is strongly dependent on the reliability of the dielectric constant model. Two new parametrizations were recently developed based on one hand on the Soil Moisture and Ocean Salinity (SMOS) satellite multi-angular brightness temperature measurements by Boutin et al. (2021) (BV), and on the other hand on new George Washington University laboratory measurements by Zhou et al. (2021) (GW2020). These two approaches are fully independent. For most SSS and Sea Surface Temperature (SST) conditions commonly observed over the open ocean, the relative variations of brightness temperatures Tb simulated through the BV and GW2020 parametrizations agree particularly well, and better than with earlier parametrizations previously used in the SMOS, Soil Moisture Active Passive (SMAP) and Aquarius SSS retrievals. Nevertheless, uncertainty remains, especially below 10°C where a ~0.1K relative difference between the two models is observed. This motivates the development of a revised parameterization, BVZ, based on a methodology similar to that used to derive BV but using GW2020 instead of SMOS measurements. Compared to the GW2020 parameterization, BVZ is derived with a reduced number of degrees of freedom, it relies on TEOS10 PSS78 conductivity-salinity relationship and on previously derived static permittivity of fresh water. One month per season of SMOS data have been reprocessed in 2018 using BV, GW2020 and BVZ. We find the best overall agreement between SMOS SSS and Argo SSS with BVZ parametrization, with noticeable improvement in the 5°C-15°C SST range.

The accuracy of the Sea Surface Salinity (SSS) retrieved from L-Band radiometer measurements is strongly dependent on the accuracy of the modelling of the dielectric constant (ε). Two new ε parametrizations have recently been developed based on one hand on the Soil Moisture and Ocean Salinity (SMOS) satellite multi-angular brightness temperature measurements and on the other hand on new laboratory measurements. These two approaches are fully independent. These new ε parametrizations are compared with each other and with the ε models previously in use in the SMOS, Soil Moisture Active Passive (SMAP) and Aquarius SSS retrievals. The two new ε parametrizations are found to be in closer agreement than with earlier parametrizations for most common ocean conditions. We will further study to which extent the recent SMOS CCI+SSS v3 reprocessing confirms the above results and could help resolve remaining inconsistencies.

The European Space Agency (ESA) leads on observing the Earth's changing climate from space. Its flagship programme, the Climate Change Initiative (CCI), draws together over 40 years of data from ESA's own satellite missions and those from other space agencies - from past as well as currently active in -orbit ins trumentation. The CCI science teams focus on R&D activities to generate long -term, global climate data records that describe the evolution of key components of the Earth's climate system, as defined by the Global Climate Observing System (GCOS) (see https://public.wmo.int/en/programmes/global-climate-observing-system) in support of the United Nations Framework Convention on Climate Change (see https://unfccc.int/). Currently more than 20 Essential Climate Variables (ECV) of the 54 GCOS defined ECVs have been addressed by science teams involved in the CCI. All ECV datasets are fully validated and have high levels of traceability and consistency, including quantitative estimates of uncertainty required by both climate science and modelling communities. In its contribution to climate and Earth system science, this programme has published over 700 peer-reviewed articles, and supported the Intergovernmental Panel on Climate Change's (IPCC) headline statements on climate in both its fifth Assessment Report and subsequent reports, such as the ‘Special Report on Oceans and Cryosphere in a Changing Climate’, with ongoing involvement in the IPCC's sixth assessment cycle. Besides providing an overview on CCI, this presentation will make the link between CCI and ESA's Soil Moisture and Ocean Salinity (SMOS) mission, demonstrating the contribution that SMOS data can make in the creation of climate data records (CDR). Several CDRs are already including SMOS data on a regular basis, such as the sea surface salinity and soil moisture long-term data sets. There is also potential for using SMOS data for sea ice, biomass and vegetation climate data records.

The European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (SSS) aims at generating global SSS fields from all available satellite L-band radiometer measurements over the longest possible period with a great stability (including Soil Moisture and Ocean Salinity, SMOS). Version 1 and 2 of CCI+SSS level 4 fields combine SSS form the three satellite L-Band radiometer missions and have been found to be in a very good agreement with in situ measurements (global rms difference of 0,16 pss). Nevertheless, some systematic differences still remain between CCI+SSS and in situ SSS. We study here to which extent some errors coming from the SMOS SSS processing are reduced, when making some key changes in the SMOS level 2 OS processing. Then, we discuss the contribution of each change in the preliminary results we obtain.

Arctic freshwater fluxes make this region key to regulate ocean currents and global climate. Hence, the Arctic Ocean sea surface salinity (SSS) knowledge is crucial to describe some of the processes that govern climate change.Recently, Barcelona Expert Center (BEC) deployed their version 2 of SSS Arctic data retrieved from Soil Moisture and Ocean Salinity mission (SMOS) mission. Nevertheless, in the context of the ESA Arctic+ initiative, BEC has planned to introduce improvements in all the processing levels. Some of the planned improvements include: (i) optimizing the projection grid of level 1, (ii) studying the performance of different dielectric models in the Artic region and (iii) producing an additional level 4 product. The SSS Arctic products from Soil Moisture Active Passive (SMAP) mission could be used to produce a level 4 by merging them with this new version of SSS level 3 produced from SMOS.Big data techniques are applied to produce debiased SSS maps. These techniques will be refined by introducing a new grouping method for the statistical study of the data.The aim of this work is to obtain a more accurate version of the Arctic salinity maps starting at 2011. The new SMOS SSS maps are expected to better capture the Arctic river plumes and thus they will help to better understand the freshwater inflow/outflow in the Arctic Ocean.

The quality of the Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) maps has been noticeably improved in the last two years, in particular those produced at the Barcelona Expert Center (BEC). However, the BEC SSS maps are still affected by a latitudinal and seasonal bias. In this work, we comprehensively characterize the residual latitudinal and seasonal biases, which are used to correct de retrieved SSS, leading to a new generation of higher-quality SSS maps. The shape and regularity of this bias suggests that the effect, which produces this error, is not a poor characterization of the galaxy, some residual Total Electron Content (TEC) effect, or a poor characterization of the systematic Sea Surface Temperature (SST) effects on the SSS retrieval. It appears to be related to a geometrical effect associated to the relative position between the SMOS antenna, the Sun and the Earth.

The European Space Agency's (ESA) SMOS mission, in orbit since more than 8 years, carries a passive microwave interferometric radiometer measuring in L-Band and provides accurate global observations of emitted radiation originating from the Earth's surfaces since the atmosphere is almost transparent in this spectral range. In addition, over land the effect of vegetation on the measurements is smaller than for shorter wavelengths. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide information on the characterisation of ice and snow covered surfaces and the sea ice effect on ocean-atmosphere heat fluxes and dynamics, which affects large-scale processes of the Earth's climate system.

After more than eight years of Soil Moisture and Ocean Salinity (SMOS) aquisitions, an empirical characterization of the biases and the computation of an effective brightness temperature uncertainty is possible. In this work we show that both parameters strongly depend on the geographical location of the acquisition. Metrics based on the differences between expected and theoretical values of the bias and uncertainty are developed and used for a quantitative assessment of the locations where SMOS errors are currently being worse characterized. This characterization can be used for the definition of an empirical bias correction and a more accurate cost function which are expected to provide a better SMOS SSS product.

Novel SMOS satellite estimates of Sea Surface salinity in the Mediterranean Sea will be used to infer the spatial and temporal distribution of Alkalinity in this basin, exploiting the direct relationship between salinity and alkalinity. A proper validation of the derived variable will be performed against in-situ data, climatologies and model outputs. The resulting estimates of alkalinity in the Mediterranean Sea will be linked to the overall carbonate system in the broader context of ocean acidification assessment.

ESA's Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit for over 7 years, with its Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) functioning well. This 7 year period has provided a wealth of information which has enabled us to understand and consolidate the performance of the payload in great detail. More importantly, we know now the things that work well, those that need improvement, and how the instrument could be enhanced if we were to build it again. This paper presents the lessons learnt from SMOS after 7 years in orbit.

The overall process commonly referred to as Ocean Acidification (OA) is nowadays gathering increasing attention for its profound impact at scientific and socio-economic level. To date, the majority of the scientific studies into the potential impacts of OA have focused on models and in situ datasets. Satellite remote sensing technology have yet to be fully exploited and could play a significant role by providing synoptic and frequent measurements for investigating OA processes on global scales. Within this context, the purpose of the ESA “Pathfinders-OA” project is to quantitatively and routinely estimate surface ocean pH by means of satellite observations in several ocean regions. Satellite Ocean Colour, Sea Surface Temperature and Sea Surface Salinity data (with an emphasis on the latter) will be exploited. A proper merging of these different datasets will allow to compute at least two independent proxies among the seawater carbonate system parameters and therefore obtain the best educated guess of the surface ocean pH. Preliminary results of the anomaly and variability of the ocean pH maps are presented.

The performances of a novel salinity retrieval strategy by means of Support Vector Regression (SVR) have been studied. The SMOS satellite brightness temperatures measurements and additional auxiliary parameters have been co-located with salinity data collected by Argo buoys, which represented the ground-truth to be matched by the algorithm. Salinity fields estimated by the SVR with a combination of features and at two polarizations are in good agreement with the ground-truth data. A critical additional feature that was studied was the TB/emissivity incidence angle dependency, analyzing the possible performances improvements due to the multi-angular information content. The robustness and versatility of this approach are under further assessment over wider areas and time lags, and in various combinations of SMOS features.

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.

A sounding of the capabilities of a novel salinity retrieval strategy by means of Support Vector Regression (SVR) has been performed. SMOS brightness temperatures measurements and additional auxiliary parameters have been co-located with salinity data collected by ARGO buoys, which represented the ground-truth to be matched by the algorithm. Salinity fields estimated by the SVR are in good agreement with the ground-truth, suggesting that the chosen approach can be promising, despite its robustness and versatility are under further assessment over wider areas and time lags, and in various combinations of SMOS features.

After 2.5 years of the Soil Moisture and Ocean Salinity (SMOS) mission, the characterization of residual instrumental systematic errors in the measured brightness temperatures (TB) is still rather poor. This, in turn, negatively impacts the sea surface salinity retrievals and, as such, notably limits the mission's success. The error mitigation methodology currently used operationally, the so-called Ocean Target Transformation (OTT), mixes both instrumental and model-induced errors. In this paper, it is proposed to distinguish errors by their type of impact on the TB images: mean brightness level, incidence angle dependence, and azimuth angle dependence. A new approach to characterize the azimuth-dependent errors is proposed. First, a careful data selection strategy is applied. Then, an empirically fitted model, which only accounts for the TB incidence angle dependence, is subtracted from the mean TB images of the selected data sets to estimate the systematic antenna-frame errors. The robustness of this methodology is assessed through the estimated anomaly pattern stability when computed for different geophysical conditions, periods of time, and latitudinal bands. The residual variability ranges from 0.03 K to 0.14 K, whereas the OTT variability is about 0.5 K. The new method is forward model independent and generic. It can therefore be applied to estimate the antenna-frame systematic errors over land and ice. Moreover, it proves to be very effective in separating different sources of error and can therefore be used to further characterize other error components and improve the various SMOS forward model terms.

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.

A prospective sounding of the capabilities of a novel salinity retrieval by means of Support Vector Regression has been performed. Co-located SMOS measurements and additional auxiliary parameters have been considered, whilst salinity data collected by ARGO buoys represented the ground-truth to be matched by the algorithm. Salinity fields estimated by the SVR are in good agreement with the ground-truth, suggesting that the chosen approach can be promising, despite its robustness and versatility needs to be assessed over wider areas and time lags, and in various combinations of SMOS features.

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

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.

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.

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

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

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

It is accepted that the best way to monitor sea surface salinity (SSS) on a global basis is by means of L-band radiometry. However, the measured sea surface brightness temperature (TB) depends not only on the SSS but also on the sea surface temperature (SST) and, more importantly, on the sea state, which is usually parameterized in terms of the 10-m-height wind speed (U10) or the significant wave height. It has been recently proposed that the mean-square slope (mss) derived from global navigation satellite system (GNSS) signals reflected by the sea surface could be a potentially appropriate sea-state descriptor and could be used to make the necessary sea state TBcorrections to improve the SSS estimates. This paper presents a preliminary error analysis of the use of reflected GNSS signals for the sea roughness correction and was performed to support the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission; the orbit and parameters for the SMOS instrument were assumed. The accuracy requirement for the retrieved SSS is 0.1 practical salinity units after monthly averaging over 2deg times 2degboxes. In this paper, potential improvements in salinity estimation are hampered mainly by the coarse sampling and by the requirements of the retrieval algorithm, particularly the need for a semiempirical model that relates TBand mss.

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.

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

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.

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.

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).

We present a study on the electromagnetic scattering of sea surface based on the Integral Equation Method (IEM), in which the sea roughness spectrum is approximated by a power-law analytical expression. This allows extending the usual first-order IEM approach.

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.