International audienceRemote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 × 100 km². In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability

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Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

[1] In order to prepare the sea surface salinity (SSS) retrieval in the frame of the Soil Moisture and Ocean Salinity (SMOS) mission we conduct sensitivity studies to quantify uncertainties on simulated brightness temperatures (T b ) related to uncertainties on sea surface and scattering modeling. Using a two-scale sea surface emissivity model to simulate T b at L band (1.4 GHz), we explore the influence on estimated SSS of the parameterization of the seawater permittivity, of the sea wave spectrum, of the choice of the two-scale cutoff wavelength, and of adding swell to the wind sea. Differences between T b estimated with various existing permittivity models are up to 1.5 K. Therefore a better knowledge of the seawater permittivity at L band is required. The influence of wind speed on T b simulated with various parameterizations of the sea wave spectrum differs by up to a factor of two; for a wind speed of 7 m s À1 the differences on estimated SSS is several psu depending on the sea wave spectral model taken, so that sea spectrum is a major source of uncertainty in models. We find no noticeable effect on simulated T b when changing the two-scale cutoff wavelength and when adding swell to the wind sea for low to moderate incidence angles. The dependence of the wind-induced T b on SST and SSS being weak, we assess the error in SSS estimated assuming that the wind speed influence is independent of SST and SSS. We find errors on estimated SSS up to 0.5 psu for 20°C variation in SST. Therefore this assumption would induce regional biases when applied to global measurements.INDEX TERMS: 4275 Oceanography: General: Remote sensing and electromagnetic processes (0689); 4271 Oceanography: General: Physical and chemical properties of seawater; 0659 Electromagnetics: Random media and rough surfaces; KEYWORDS: microwave, radiometry, remote sensing, salinity, roughness Citation: Dinnat, E. P., J. Boutin, G. Caudal, and J. Etcheto, Issues concerning the sea emissivity modeling at L band for retrieving surface salinity,

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Global surface ocean salinity measurements have been available since the launch of SMOS in 2009 and coverage was further enhanced with the launch of Aquarius in 2011. In the polar regions where spatial and temporal changes in sea surface salinity (SSS) are deemed important, the data have not been as robustly validated because of the paucity of in situ measurements. This study presents a comparison of four SSS products in the ice‐free Arctic region, three using Aquarius data and one using SMOS data. The accuracy of each product is assessed through comparative analysis with ship and other in situ measurements. Results indicate RMS errors ranging between 0.33 and 0.89 psu. Overall, the four products show generally good consistency in spatial distribution with the Atlantic side being more saline than the Pacific side. A good agreement between the ship and satellite measurements was also observed in the low salinity regions in the Arctic Ocean, where SSS in situ measurements are usually sparse, at the end of summer melt seasons. Some discrepancies including biases of about 1 psu between the products in spatial and temporal distribution are observed. These are due in part to differences in retrieval techniques, geophysical filtering, and sea ice and land masks. The monthly SSS retrievals in the Arctic from 2011 to 2015 showed variations (within ∼1 psu) consistent with effects of sea ice seasonal cycles. This study indicates that spaceborne observations capture the seasonality and interannual variability of SSS in the Arctic with reasonably good accuracy.

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