Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices

Satellite remote sensing technologies have a high potential in applications for evaluating land conditions and can facilitate optimized planning agricultural sectors. However, misinformed selection decisions limit crop yields increase production-related costs to farmers. Therefore, the purpose of this research was develop suitability assessment system using satellite sensing-derived soil-vegetation indicators. A multicriteria decision analysis conducted by integrating weighted linear combinations fuzzy analyses GIS platform following eight criteria: elevation, slope, LST vegetation indices (SAVI, ARVI, SARVI, MSAVI, OSAVI). The relative priorities indicators were identified expert system. Furthermore, results evaluated ground truthed yield data. In addition, estimation method developed representing influential factors. utilizing equal weights showed that 43% (1832 km2) highly suitable, 41% (1747 moderately 10% (426 marginally suitable improved productions. Alternatively, knowledge also considered, along with references, when membership function; as result, 48% (2045 being suitable; 39% 7% (298 suitable. Additionally, 6% (256 described not both methods. Moreover, SAVI (R2 = 77.3%), ARVI 68.9%), SARVI 71.1%), MSAVI 74.5%) OSAVI 81.2%) good predictive ability. combined model these five reported highest accuracy 0.839); then applied prediction maps corresponding years (2017–2020). This suggests methods platforms are an effective convenient way land-use planners policy makers select cultivable areas increased production.