Building a Learning Database for the Neural Network Retrieval of Sea Surface Salinity from SMOS Brightness Temperatures
This work addresses regional accuracy issues in satellite-based ocean salinity measurements, which is incremental as it builds on prior neural network methods.
The study tackled the problem of systematic regional biases in sea surface salinity retrieval from SMOS brightness temperatures using neural networks, by optimizing the learning database distribution and boosting the learning process, resulting in nearly eliminated biases between 40°S and 40°N latitudes and maintaining a global standard deviation of 0.6 to 1 psu.
This article deals with an important aspect of the neural network retrieval of sea surface salinity (SSS) from SMOS brightness temperatures (TBs). The neural network retrieval method is an empirical approach that offers the possibility of being independent from any theoretical emissivity model, during the in-flight phase. A Previous study [1] has proven that this approach is applicable to all pixels on ocean, by designing a set of neural networks with different inputs. The present study focuses on the choice of the learning database and demonstrates that a judicious distribution of the geophysical parameters allows to markedly reduce the systematic regional biases of the retrieved SSS, which are due to the high noise on the TBs. An equalization of the distribution of the geophysical parameters, followed by a new technique for boosting the learning process, makes the regional biases almost disappear for latitudes between 40°S and 40°N, while the global standard deviation remains between 0.6 psu (at the center of the of the swath) and 1 psu (at the edges).