Optimized Land Suitability Mapping for Wheat Cultivation by Integrating Fuzzy Hierarchical Analysis and Satellite Images

This study focuses on identifying optimal areas for wheat cultivation in the northwestern part of the Khuzestan province- southwest Iran. This research adopts a multi-method approach that integrates Fuzzy logic, the Analytical Hierarchy Process (AHP), and satellite images to facilitate result-driven analysis. The study employs 86 bands and band ratios from Landsat-9, Sentinel-3, Sentinel-2, and fused Landsat-9_Sentinel-2 satellite images to establish correlations (CR) with key agro-ecological parameters relevant to wheat cultivation. The analysis encompasses climatic data (average, minimum, and maximum temperature, and annual rainfall), topographic data (slope percentage), and soil physical–chemical properties (organic carbon percentage, electrical conductivity, exchangeable sodium percentage, pH, gypsum, and soil texture). The choice of Sentinel-2 bands and band ratios is based on their superior CR values, which enable the development of robust regression models. This approach facilitates the generation of precise maps for all pertinent agro-ecological parameters. By overlaying weighted fuzzy maps, the research unveils distinct suitability degrees for wheat cultivation across various regions. The center and northern parts of the study area exhibit highly and moderately suitable classes, while the southeastern and southwestern areas display marginally and permanently not suitable classes. Out of the total land area, 2.29% is highly suitable, 6.51% is moderately suitable, 48.45% is marginally suitable, 36.75 is marginally not suitable, and 6% is permanently not suitable. To validate the findings, the researchers collect field data, which confirms the accuracy of the generated output map. Furthermore, a comparison between the present research method and traditional approaches highlights the superiority of the proposed methodology.