AUTHOR=Savin Alexander , Krinitskiy Mikhail , Osadchiev Alexander TITLE=Improved sea surface salinity data for the Arctic Ocean derived from SMAP satellite data using machine learning approaches JOURNAL=Frontiers in Marine Science VOLUME=11 YEAR=2024 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2024.1358882 DOI=10.3389/fmars.2024.1358882 ISSN=2296-7745 ABSTRACT=
Salinity is among the key climate characteristics of the World Ocean. During the last 15 years, sea surface salinity (SSS) is measured using satellite passive microwave sensors. Standard retrieving SSS algorithms from remote sensing data were developed and verified for the most typical temperature and salinity values of the World Ocean. However, they have far lower accuracy for the Arctic Ocean, especially its shelf areas, which are influenced by large river runoff and have low typical temperature and salinity values. In this study, an improved algorithm has been developed to retrieve SSS in the Arctic Ocean during ice-free season, based on Soil Moisture Active Passive (SMAP) mission data, and using machine learning approaches. Extensive database of