ALGORITHMS FOR ASSESSING SOIL SALINITY LEVELS BASED ON REMOTE SENSING IMAGERY

This article investigates methods for assessing soil salinity levels based on satellite (remote sensing) imagery and their calculation algorithms. Determining the degree of salinity plays a crucial role in the rational use of land resources and increasing agricultural efficiency. The study analyzes indices for determining soil salt content using remote sensing technologies, particularly multispectral images obtained from satellite systems such as Landsat and Sentinel (for example, SI - Salinity Index, NDVI - Normalized Difference Vegetation Index, and others). Furthermore, algorithms are developed based on these indices that enable automatic determination of salinity assessments. Artificial intelligence, machine learning, and geographic information systems (GIS) technologies are extensively applied in creating models. As a result, accurate, rapid, and cost-effective solutions are proposed for determining various levels of soil salinity (non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline). This approach plays a crucial role in preventing soil degradation and planning ameliorative measures.