This article is about monitoring soil salinity using machine learning and the polarimetric scattering features of PALSAR-2 data. The use of machine learning is a promising approach for gaining insight on changes in land-use, particularly in salinity, which can have damaging effects on biodiversity and crop yields. Therefore, a reliable and accurate way of detecting changing soil salinity is critical for successful land management and conservation. The primary aim of this study was to develop and assess the use of PALSAR-2 data with a machine learning technique for monitoring soil salinity.
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Dr. Yerlan Baidashov is a professor at the Institute of Engineering Cyberspace Center of Nazarbayev University and an expert in geoinformatics and remote sensing. He is the lead researcher of this study and his expertise and hands-on experience in machine learning, remote sensing and polarimetric scattering guarantee high data accuracy. He is also a member of the International Society for Photogrammetry and Remote Sensing and the Asian Association for Remote Sensing and was highly recommended for this very research.
This study outlines the important factors for effective monitoring and management of soil salinity using machine learning and PALSAR-2 data. The research team studied the polarization of backscattering from the soil surface, as well as the effectiveness of a Random Forest algorithm for extracting useful information. After the successful implementation of the study, it was concluded that satellite polarimetry and machine learning are powerful tools for monitoring and managing soil salinity and other land-use changes.
