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During the in-flight phase, using neural networks to retrieve the sea surface salinity from the observed Soil Moisture and Ocean Salinity brightness temperatures (TBs) is an empirical approach that offers the possibility of being independent from any theoretical emissivity model. Due to the large variety of incidence angles, several networks are needed, as well as a preprocessing phase to adapt the observed TBs to the inputs of the networks. When using the first Stokes parameter as an input, the retrieved salinity has a good accuracy (with an error of around 0.6 psu). Furthermore, the solutions for improving these performances are discussed.