Integrating soil and vegetation indices into a spatial decision support system for agricultural suitability evaluation in arid landscapes

Sabab Ali Shah, Aamir Shakoor, Muhammad Nouman Sattar, Akinwale T. Ogunrinde, Xian Xue, Waqar Ahmed, Hareef Ahmed Keerio, Shuban Ali, Sanjrani Manzoor Ahmed, Hafiz Umer Farid

Agriculture is a vital component of Pakistan’s economic development, and the land suitability analysis is very important to assess the land situation to help the decision-maker in planning in the agricultural sector. This research was conducted to analyze land suitability using a GIS-based multicriteria decision method for Punjab, Pakistan. Various parameters such as slope, elevation, land surface temperature, soil-adjusted vegetation index, modified soil-adjusted vegetation index, optimized soil-adjusted vegetation index, the atmospherically resistant vegetation index, and soil-adjusted and atmospherically resistant vegetation index were used. The weighted overlay and fuzzy logic overlay methods were used for analyzing land suitability. The weighted overlay and fuzzy logic overlay revealed, respectively, that 37% and 41% of the land was highly suitable, 29% and 24% was moderately suitable, 19% and 17% was marginally suitable and only 15% and 18% of the area was not suitable. The yield prediction models that used each vegetation index separately displayed an accuracy of up to 80%. By applying all indices at once, the prediction model demonstrated a higher accuracy of 89% and was therefore adopted for the estimation of the yield. The study offers a useful tool for optimizing wheat cultivation and can be extended to other crops and regions; however, further validation and scaling across multiple crops and seasons is recommended to support data-driven and climate-resilient agricultural planning.