Soil salinization and their annual increase in volume is not only one of the main problems of arid and subarid regions, but it is becoming global. Studying the problem of salinization and its spatial distribution using operational remote sensing methods is very important for Kazakhstan, where almost half of the agricultural land is exposed to salinization, but it is at the initial stage of development in the use of space technologies of research. The main goal of this study is to conduct a field study of soil salinity in corn fields, one of the most common crops in the arid region of the country, located in the Shaulder irrigated massif, using space-based methods, and to create algorithms for compiling a salinity map based on remote sensing data. For this purpose, firstly, using Sentinel-2 images, the method of separating corn from other dominant crops in the region by creating NDVI dynamics covering all phases of growth of agricultural crops was shown. Then, a regression analysis was performed on soil and vegetation indices calculated using satellite images and data on soil salinity obtained through field studies. As a result of the analysis, the main predictor of deciphering salinized soils was determined. By dividing the predictive image into quartiles, contours of salinized soils were determined and a soil salinity map was created. With the help of the soil salinity map, it was found that, non-saline soils – 2912.2 ha; slightly saline soils – 3288.4 ha, moderately saline soils – 2615.2 ha, and strongly saline soils – 1284.3 ha in the study area.