Land Suitability Assessment Based on Feature-Level Fusion of Sentinel-1 and Sentinel-2 Imagery: A Case Study of the Honam Region of Iran

Optimal use of agricultural land leads to increased productivity and paves the way for sustainable agriculture. The land suitability assessment (LSA) is known as the basic scientific solution in this regard and has been widely used. This study takes a new approach to LSA based on the cultivated crops and their land production potential (LPP). The Honam region of Lorestan Province in western Iran was selected as a case study, where various plants including orchards and crop types (e.g., rainfed wheat, irrigated wheat, barley, chickpea, alfalfa, clover, and garlic) were cultivated. A map of crop types was first produced using the time series of Sentinel-1 synthetic aperture radar and Sentinel-2 optical imagery in the context of a feature-level fusion classification approach based on the support vector machine (SVM) algorithm. The LSA was then performed using the Food and Agriculture Organization method, and the LPP for plants was estimated using climate, soil, and landscape requirements. The SVM-derived crop type map gave acceptable performance with an overall accuracy of 92% and 86% kappa coefficient. Meanwhile, the clover and alfalfa had the highest (96%) and lowest (66%) accuracy, respectively. The LSA showed that slope, temperature, and soil physical properties such as texture, structure, and coarse fragments resulted in a significant reduction in LPP compared to potential yield. These limitations lowered the land suitability for chickpea, wheat, and garlic and made the land unsuitable for chickpea. Despite the limitations, over 60% of the studied lands for alfalfa, clover, barley, and wheat were in the suitable and moderately suitable classes.