Revolutionising agricultural land suitability and water accessibility assessment using remote sensing: a case study of Jeypore Block, Koraput, Odisha

Abstract

This study aims to delineate and evaluate zones suitable for sustainable agricultural development in the Jeypore Block of Koraput District, Odisha, India. A systematic approach to land use planning is essential for minimizing the impact of human activities on natural resources and ensuring sustainable land utilization. The adaptability of cropland is critical for meeting the growing food demands driven by population growth, climate change and environmental degradation. This research focuses on identifying suitable land, assessing the influence of water on soil characteristics and evaluating the potential impacts of climate change on existing land conditions. The study employs Geographic Information System (GIS) techniques and various interpolation models to integrate environmental data for analysis. Eighteen diverse environmental and climatic criteria were considered, including two terrain attributes, seven soil properties, two infrastructure-related factors, three hydrological parameters, two vegetation characteristics and two climatic variables, to identify potential agricultural zones within an area of approximately 456 km². A digital elevation model (DEM) with a 10-m pixel resolution was utilised to assess elevation and slope. Additional data sources included digital geology and soil maps, land-use/land-cover data, long-term meteorological records and 45 soil samples collected from the study area. A distinguishing feature of this study is its emphasis on land suitability assessment through enhanced soil moisture analysis, in conjunction with surface water dynamics, groundwater availability and climate change factors. The results were validated using a multi-model analysis, yielding a kappa coefficient of 0.7069, which indicates strong agreement and model robustness. The assessment revealed that approximately 78.07% of the study area (356 km²) is classified as either highly suitable (S1) or suitable (S2) for agricultural activities. Additionally, 20.94% of the area is considered moderately suitable (S3), while only 0.99% falls into the marginally suitable or unsuitable category.

Keywords: DEM (digital elevation model); GIS-based analysis; NDVI (normalized difference vegetation index); Resource optimization; Sustainable agriculture; Watershed management.