Remote sensing and machine learning algorithms to predict soil salinity in southern Kazakhstan

Abstract

Abstract Salinization and land degradation are significant challenges in the southern regions of Kazakhstan. These issues arise due to climate change, unequal water resource distribution, and human impact. The primary concern revolves around water resources, which are influenced by the area’s trans boundary flow of major rivers. The low level of water and food security has pushed the development of new approaches based on remote sensing monitoring and geographic information systems (GIS) to provide solutions for soil salinity. The research aims to focus on utilizing high-resolution radar images. This data type is effective for cloudy weather and can be useful for continued monitoring of some areas. Machine learning methods can solve the problem of automatic mapping of agricultural land salinity in Kazakhstan’s southern regions. The precise mapping of the salinity area helps prevent or decrease salinity’s impact on agriculture. The experiment realized that complex models such as LightGBM do not have significant accuracy performance over simple models on a small dataset compared with Ridge regression. The results allow us to recommend an approach for further improvement with ground-based measurement data and other deep-learning methods for mapping the salinity of agricultural lands.

Keywords

• Soil salinity
• Machine learning
• Decision tree
• LightGBM
• Ridge linear regression
• Synthetic aperture radar