International audience

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.

Our study focuses on the variability of Sea Surface Salinity (SSS) near south Greenland. This is based on extensive in situ data gathered from a variety of sources, including Argo floats, CTD casts, thermosalinographs, and drifters, as well as the satellite-derived SSS product from the Climate Change Initiative (CCI). The CCI SSS effectively captures a significant portion of the salinity’s seasonal and interannual variability beyond 50km from the coast, outperforming SSS from individual satellite missions. The examination of a well-sampled fresh blob in fall 2021 suggests that satellite SSS is a valuable tool for studying freshwater transfer from the shelves to the deeper ocean, particularly during ice-free periods. However, we found positive biases in the CCI SSS on the shelves, highlighting the need for improved absolute calibration in these areas. For a more detailed study of SSS within 50km of the coast, a satellite SSS with a higher spatial resolution would be required.

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.

Sea surface salinity is retrieved for more than 11 years from the Soil Moisture and Ocean Salinity (SMOS) satellite mission. This data set provides a unique monitoring of the Sea Surface Salinity (SSS) spatio-temporal variability at global scale. It is particularly useful to follow the surface ocean pathway of fresh river plumes water as illustrated here in the Bay of Bengal. A revised adjustment of the whole SMOS SSS time series (CATDS Expertise Center version 5, 2010–2020) leads to clear reduction of local biases in very variable regions and in very noisy regions. The robust std difference between SMOS CEC v5 (18-day, ~70km SSS) and Argo in situ SSS is 0.17 in regions warmer than 5°C. We will discuss how future CATDS products will be improved in view of two ongoing reprocessings, the CATDS L1/L2 v7 reprocessing and the ESA CCI+SSS L2 SMOS reprocessing.

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) 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.

Earlier studies have pointed out systematic differences between sea surface salinity retrieved from L-band radiometric measurements and measured in situ, which depend on sea surface temperature (SST). We investigate how to cope with these differences given existing physically based radiative transfer models. In order to study differences coming from seawater dielectric constant parametrization, we consider the model of Somaraju and Trumpf (2006) (ST) which is built on sound physical bases and close to a single relaxation term Debye equation. While ST model uses fewer empirically adjusted parameters than other dielectric constant models currently used in salinity retrievals, ST dielectric constants are found close to those obtained using the Meissner and Wentz (2012) (MW) model. The ST parametrization is then slightly modified in order to achieve a better fit with seawater dielectric constant inferred from SMOS data. Upgraded dielectric constant model is intermediate between KS and MW models. Systematic differences between SMOS and in situ salinity are reduced to less than +/-0.2 above 0 °C and within +/-0.05 between 7 °C and 28 °C. Aquarius salinity becomes closer to in situ salinity, and within +/-0.1. The order of magnitude of remaining differences is very similar to the one achieved with the Aquarius version 5 empirical adjustment of wind model SST dependence. The upgraded parametrization is recommended for use in processing the SMOS data. Further assessment or improvement using new laboratory measurements should consider keeping the physics-based formulation by ST that has been shown here to be very efficient.

An important contribution of satellite Sea Surface Salinity (SSS) is the spatio-temporal monitoring of rivers fresh water plumes at mesoscale. In this paper, we detail a new correction for systematic errors in the Soil Moisture and Ocean Salinity (SMOS) measurements that is implemented in the Centre Aval de Traitement des Donnees SMOS (CATDS). With this new mitigation, the SMOS and Soil Moisture Active Passive (SMAP) SSS monitor very consistent features in most areas close to continents. The rms-difference between bi-weekly SMOS and SMAP SSS over 20 months and in selected coastal regions is about 0.3pss (once outliers are filtered out), rather consistent with the rms-difference between satellite and in situ SSS (on the order of 0.2pss). The coefficient of determination (r2) between SMOS and SMAP SSS is above than 0.8 in very fresh areas (river plumes). Over the open ocean, the rms difference between SMOS and ship SSS is 0.2pss.

The variability observed on SMOS (Soil Moisture and Ocean Salinity) SSS (sea surface salinity) recorded in 2010 and 2011 is partly attributable to geophysical variations but also to imperfections in various corrections (e.g. sun aliases, sea surface scattering of the galactic signal). We perform a retrieval of bistatic coefficients from SMOS Tbs, suggesting more peaked coefficients than the ones currently used for simulating the galactic contribution.

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.

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.

This contribution summarizes prominent features of the Level 2 algorithm aimed at processing land surface geophysical quantities from the ESA-led SMOS mission. It emphasizes the soil moisture retrieval and describes the decision tree built in order to select appropriate retrieval configurations. The expected performance is illustrated by preliminary results of the algorithm validation.

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.

This paper aims at studying the quality of the sea surface salinity (SSS) retrieved from soil moisture and ocean salinity (SMOS) data in coastal areas. These areas are characterized by strong and variable SSS gradients [several practical salinity units (psu)] on relatively small scales: the extent of river plumes is highly variable, typically at kilometric and daily scales. Monitoring this variability from SMOS measurements is particularly challenging because of their resolution (typically 30-100 km) and because of the contamination by the nearby land. A set of academic tests was conducted with a linear coastline and constant geophysical parameters, and more realistic tests were conducted over the Bay of Biscay. The bias of the retrieved SSS has been analyzed, as well as the root mean square (rms) of the bias, and the retrieved SSS compared to a numerical hydrodynamic model in the semirealistic case. The academic study showed that the Blackman apodization window provides the best compromise in terms of magnitude and fluctuations of the bias of the retrieved SSS. Whatever the type of vegetation cover, a strong negative bias, greater than 1 psu, was found when nearer than 36 km from the coast. Between 44 and 80 km, the type of vegetation cover has an impact of less than a factor 2 on the bias, and no influence further than 80 km from the coast. The semirealistic study conducted in the Bay of Biscay showed a bias over ten days lower than 0.2 psu for distances greater than 47 km, due to an averaging over various geometries (coastline orientation, swath orientation, etc.). The bias showed a weak dependence on the location of the grid point within the swath. Despite the noise on the retrieved SSS, contrasts due to the plume of the Loire River and the Gironde estuary remained detectable on ten-day averaged maps with an rms of 0.57 psu. Finally, imposing thresholds on the major axis of the measurements brought little improvement to the bias, whereas it increased the rms and could lead to strong swath restriction: a 49-km threshold on the major axis resulted in an effective swath of 800-900 km instead of 1200 km.

The European Space Agency SMOS (Soil Moisture and Ocean Salinity) mission aims at obtaining global maps of soil moisture and sea surface salinity from space for large scale and climatic studies. It uses an L-band (1400-1427 MHz) microwave interferometric radiometer by aperture synthesis (MIRAS) to measure brightness temperature at the Earth surface at horizontal and vertical polarizations (Th and Tv). These two parameters will be used together to retrieve the geophysical variables. The retrieval of salinity is a complex process that requires the knowledge of other environmental information and an accurate processing of the radiometer measurements, due to the narrow range of ocean brightness temperatures and the strong impact in the measured values of different geophysical parameters (as sea state) other than salinity. Here we present the baseline approach chosen by ESA to retrieve sea surface salinity from MIRAS data, as it has been developed and implemented by the joint team of scientists and engineers responsible for the SMOS ocean salinity level 2 prototype processor.

This article is concerned with the apodization windows to be applied to brightness temperature maps reconstructed from complex visibilities provided by the MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) instrument on board the SMOS (Soil Moisture and Ocean Salinity space mission) spacecraft in order to achieve a close to uniform pixel at the Earth's surface level

L-band spaceborne microwave radiometers are becoming able to provide estimates of surface soil moisture, on both spatial and temporal scales compatible with applications to meteorology and hydrology. The basic rationale for retrieving soil moisture from radiometric measurements is the assumption that the surface layer can be modeled as a dielectric medium. Its dielectric constant then depends on several physical parameters, including soil moisture; emissivities for various incidence angles are computed using Fresnel's formulas. Many controlled field experiments have demonstrated the validity of this approach. Scenes exist still (e.g., ice-covered or frozen surfaces, barren areas) where surface soil moisture is not a relevant concept. For such scenes, information should, however, be available on the complex dielectric constant itself. This paper shows that the dielectric constant cannot be fully retrieved from single-frequency multiangular data; it describes, however, a methodology that aims at retrieving in an optimized way the available information.

L-band passive microwave remote sensing sensors are able to provide estimates of the surface soil moisture on both spatial and temporal scales compatible with applications in the fields of meteorology and hydrology. This paper presents the design work for the soil moisture retrieval algorithm developed in the frame of the SMOS mission.

L-band passive microwave remote sensing sensors are able to provide estimates of surface soil moisture, on both spatial and temporal scales compatible with applications in the fields of meteorology and hydrology. A radiometric system using a 2-D interferometric design with multi-angular viewing capabilities will be borne by the Soil Moisture and Ocean Salinity (SMOS) space mission. The basic rationale for retrieving soil moisture from radiometric measurements is the assumption that the surface layer can be modeled as a dielectric medium. Its dielectric constant then depends on several physical parameters, including soil moisture; emissivities for various incidence angles are computed using Fresnel's formulas. Many controlled field experiments have demonstrated the validity of this approach. Scenes exist however (e.g. ice covered or frozen surfaces, complete desert areas) where surface soil moisture is not a relevant concept. For such scenes, information should however be available on the complex dielectric constant itself. This communication describes a methodology which aims at retrieving in an optimized way the dielectric constant information available from multiangular radiometric data.