INTEGRATED MULTI-CRITERIA AGRICULTURAL LAND SUITABILITY ASSESSMENT IN TASHKENT REGION USING SENTINEL-2, RUSLE, AND AHP-BASED MODELING

Land suitability assessment is a critical component of sustainable agricultural planning, particularly in semi-arid regions exposed to climatic variability and soil degradation processes. This study presents an integrated multi-criteria evaluation framework for agricultural land suitability in the Tashkent region (Uzbekistan) using Sentinel-2 imagery, topographic, climatic, and erosion-related factors within a Google Earth Engine (GEE) environment. A total of 257 Sentinel-2 images (2023–2024 vegetation period) were processed to derive vegetation indices (NDVI, NDWI, EVI). Terrain characteristics were extracted from SRTM DEM, climatic variables from CHIRPS precipitation and MODIS LST datasets, and soil erosion risk was estimated using the RUSLE model. All criteria were normalized and integrated through a Weighted Linear Combination (WLC) approach based on Analytical Hierarchy Process (AHP) weights. The results indicate that 8,065.20 ha are highly suitable (S1), 595,143.66 ha moderately suitable (S2), 485,628.54 ha marginally suitable (S3), 101,540.48 ha conditionally unsuitable (N1), and no areas were classified as permanently unsuitable (N2). The proposed framework provides a reproducible and scalable spatial decision-support tool for sustainable agricultural land management.