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This article deals with an important aspect of the neural network retrievalof sea surface salinity (SSS) from SMOS brightness temperatures (TBs). Theneural network retrieval method is an empirical approach that offers thepossibility of being independent from any theoretical emissivity model, duringthe in-flight phase. A Previous study [1] has proven that this approach isapplicable to all pixels on ocean, by designing a set of neural networks withdifferent inputs. The present study focuses on the choice of the learningdatabase and demonstrates that a judicious distribution of the geophysicalparameters allows to markedly reduce the systematic regional biases of theretrieved SSS, which are due to the high noise on the TBs. An equalization ofthe distribution of the geophysical parameters, followed by a new technique forboosting the learning process, makes the regional biases almost disappear forlatitudes between 40{\deg}S and 40{\deg}N, while the global standard deviationremains between 0.6 psu (at the center of the of the swath) and 1 psu (at theedges).

Building a Learning Database for the Neural Network Retrieval of Sea Surface Salinity from SMOS Brightness Temperatures