Agricultural Land Suitability Mapping in Akwa Ibom State, Nigeria using Cloud-Based Remote Sensing and GIS - obot, U.; Eyoh, A. (Journal of Spatial Information Sciences, 2026)
Rapid population growth and concurrent urban expansion in Nigeria have placed unprecedented pressure on arable land, necessitating precise, data-driven geospatial evaluations to secure regional food supplies. This study integrates cloud-based remote sensing and Geographic Information System (GIS) technologies to map agricultural suitability zones in Akwa Ibom State, Nigeria. Google Earth Engine (GEE) and ArcGIS 10.8 were employed for land use/land cover classification using Sentinel-2 imagery, factor weight determination via the Analytical Hierarchy Process (AHP), and weighted overlay analysis. Five biophysical factors were assessed: soil properties, climate, topography, land use/land cover, and Normalized Difference Vegetation Index (NDVI) using data spanning 2014-2024 from ISRIC SoilGrids, ALOS PALSAR DEM, CHIRPS, and ERA5. AHP assigned weights of soil properties (51.8%), climate (26.5%), topography (10.4%), land use/land cover (7.9%), and NDVI (3.4%). Results indicate 14.89% (1,002.60 km²) is very highly suitable and 28.19% (1,898.14 km²) is highly suitable for agriculture, with Nsit Ubium and Uruan Local Government Areas showing the highest suitability. Conversely, 8.96% (603.31 km²) and 17.03% (1,146.66 km²) were classified as very low and low suitability. Ground-truthing via soil sampling validated the map, confirming superior soil characteristics in high-suitability zones. The study demonstrates the efficacy of cloud-based geospatial technologies for data-driven agricultural planning and provides a replicable framework for sustainable land-use management.
Keywords: Agricultural Suitability Mapping; Cloud-based Remote Sensing; Google Earth Engine; Geographic Information System; Analytical Hierarchy Process