SMAP Sea Surface Salinity Improvement in the Arctic Region Using Machine Learning Approaches

Abstract

Sea surface salinity (SSS) is a key physicochemical characteristic of the ocean that plays a significant role in describing the climate. Routine SSS retrieval algorithms exploiting remote sensing data have been developed and validated with high precision for typical regions of the World Ocean. Their effectiveness is worse in the Arctic though. To address this limitation, in this study, we employ machine learning (ML) techniques to enhance the quality of standard algorithms. We evaluate a few ML models, ranging from classical methods that process vector features, provided by standard Soil Moisture Active Passive (SMAP) satellite salinity algorithms, to deep artificial neural networks that combine vector features with two-dimensional fields extracted from the ERA5 reanalysis. We validate these models using in situ the data collected by the Shirshov Institute of Oceanology RAS during the expeditions to the Barents, Kara, Laptev, and East Siberian seas from 2015 to 2021. The results of the study indicate that the SMAP sea surface salinity standard product is improved in these regions. The ML models developed in this study make it possible to further study the Arctic region using enhanced sea surface salinity maps.