This study aimed to determine the spatial variability of soil properties using arable lands at the Kokzhon deposit in the Zhambyl Region, Kazakhstan. The research used Sentinel-2 and Landsat-8 images from 2016 to 2022, covering the spring-to-autumn periods. The indices NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), NDMI (Normalized Difference Moisture Index), and NDRE (Normalized Difference Red Edge Index) reached their calculations using the raster calculator in the ArcGIS software environment. Spatial data processing took place in QGIS. Likewise, the conduct of channel resolution normalization and index calculation used the ‘nearest neighbor’ method. The highest vegetation index (0.7–0.8) resulted in favorable years, 2018 and 2020, indicating the optimum soil fertility in specific areas. However, the minimum vegetation index (0.1–0.3) pointed to the need for reclamation measures. The resulting maps allow for effective identification and zoning of fertile areas. The GIS technologies and satellite data application demonstrated the maximum efficiency in assessing soil fertility. The methodology considered spatial heterogeneity and monitored the dynamic variations to make decisions for the management of sustainable agriculture. The obtained results can be beneficial to develop strategies for restoring low-fertility lands and increasing crop yields.
Soil fertility, GIS, remote sensing, vegetation indices, precision agriculture
The study highlighted the use of GIS and remote sensing (Sentinel-2 and Landsat-8) to assess soil fertility in Southern Kazakhstan. Vegetation indices identified zones of high and low soil fertility, enabling targeted land management. The approach provides a scalable tool for improving agricultural planning in semi-arid regions.