Multicriteria evaluation of cropland suitability using multisource datasets of satellite remote sensing and ground observation

Abstract

The potential of agriculture land monitoring serves as the lifeblood of communities, nourishing populations and fostering economic growth on a global scale. Towards the advancement of the computational approach, this study employed an analytical hierarchal process modeling for agriculture land suitability assessment by integrating a comprehensive array of 4 major criteria with a decision matrix, including 19 influencing parameters. This research incorporates the field data, including soil chemical and physical properties, irrigation water accessibility and irrigation water quality parameters, which are analyzed with cutting-edge remote sensing data layers and climatic variables using an integrated modelling approach. Thorough field observations and integrated methodology improved the conventional practices by considering the close interactions between soil, irrigation water, cropland and topography. The most innovative aspect of this research is based upon the seamless fusion of data layers of different datasets, which improves agriculture's suitability. Pairwise comparisons are systematically conducted to assign weights to each parameter, ensuring a robust decision-support framework with weighted overlay. The finding showed that the 72209.03 acres (9.13 %) cropland is highly suitable, whereas the 717738.48 acres (90.77 %) area is suitable, and the 788.49 acres (0.1 %) area is less suitable for crop cultivation. The study emphasizes the significance of each parameter in influencing suitability, contributing valuable insights into sustainable land management practices. The findings provide meaningful information for policymakers, land use planners and agriculture stakeholders interested in optimizing land management strategies to ensure sustainable agriculture development.