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.

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 monsoon freshwater and wind forcing drive high Sea Surface Salinity (SSS) contrasts and variability (up to 10 pss range) in the Bay of Bengal (BoB), with important consequences for upper ocean mixing and air-sea interactions. Synoptic SSS maps did only become available with the advent of L-band radiometers in 2010, due to insufficient prior in situ data coverage. Here, we build tools aiming at reconstructing the monthly BoB SSS at ¼° resolution since 2002 from AMSR-E radiometer data. The C-band low sensitivity to SSS requires a very careful processing. Taking the X- minus C- bands signals reduce the impact of Sea Surface Temperature (SST) and wind on brightness temperatures. It was however further necessary to train the algorithm with SSS data from L-Band radiometers to remove residual surface winds, SST, and atmospheric water contents signals. We also found that a separate treatment of the ascending and descending passes was necessary, as well as a proper data screening to minimize contamination by land signals. The resulting SSS product reproduces the broad BoB climato-logical SSS, and has a 0.66 correlation, 1.08 pss rms-difference to co-located in situ surface salinity from Array for Real-time Geostrophic Oceanography (ARGO) floats. Comparisons with ocean re-analyses in two SSS interannual variability hotspots indicate poor performance in the Northern BoB, but some skill along the East coast of India. Our results provide a proof of concept for reconstructing the BoB SSS from AMSR-E data, and we discuss possible future improvements of the data processing to further reduce the impact of spurious signals.

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 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.

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.

The Local Oscillators (LO) of the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) onboard the Soil Moisture and Ocean Salinity (SMOS) satellite are used to maintain the operating frequency of the 69 receivers. The phase of the LO drifts over time, in turn blurring the MIRAS brightness temperature (TB) measurements. After a pre-launch assessment, it was decided to calibrate the LO every 10 minutes to reduce the phase drifts. During short periods of the first 2.5 years of SMOS mission, the LO calibration has been performed every 2 minutes to assess the impact of a higher calibration frequency on the quality of the data. In this study, relative differences (10-min TBs versus 2-min TBs) of about 0.3 K are shown, which lead to non-negligible relative differences of about 0.2-0.3 practical salinity units (psu) in the retrieved sea surface salinity (SSS). However, when performing independent validation against Argo float SSS data at Level 3 (spatio-temporally averaged SSS products), no significant differences are found between 10-min and 2-min data. This is due to the fact that current SMOS SSS accuracy (relative to Argo) is about 0.6-0.8 psu, thus masking the relatively smaller LO calibration frequency effect.

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.

A prerequisite for the successful retrieval of geophysical parameters from remote sensing measurements is the development of an accurate forward model. The European Space Agency Soil Moisture and Ocean Salinity (SMOS), carrying onboard an L-band interferometric radiometer (Microwave Interferometric Radiometer using Aperture Synthesis), was launched on November 2009. Due to the lack of L-band passive ocean measurements from space, several prelaunch forward models were developed and initially used in the SMOS ocean salinity operational processor. In this paper, an update of the prelaunch semi-empirical forward model is presented, using for the first time, real SMOS data. In particular, the ocean surface emissivity modulation at L-band due to rough sea surface is reviewed and reanalyzed. A new model definition is provided with the help of a simple neural network. The improvement is quantified in terms of retrieved salinity accuracy compared with the climatology and concerns essentially the range of wind speeds higher than 12 m·s-1.

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.

Airborne radar observations of the sea surface at C- Band and small incidence angles are used to investigate some properties of the surface slope probability density function (pdf). The method is based on the analysis of the variation of the radar cross-section with incidence angle, assuming that the backscatter can be described by the Geometrical Optics theory. First, we show that roughness properties with scales larger than 12 cm can be analyzed in our configuration (C-Band, incidence 7 to 16deg). The radar data are then analyzed in terms of filtered mean square slope under the assumption of a Gaussian slope pdf. Dependence with wind speed of the upwind and total mss are analyzed and compared to various published studies. Finally an analysis of the radar data under a non-Gaussian assumption for the slope pdf is proposed, by applying the compound model suggested by [1].