Geospatial Approaches to Assessing Soil Degradation and Crop Productivity

Abstract

Soil degradation poses a major threat to agricultural productivity and global food security. Geospatial technologies, including remote sensing, Geographic Information Systems (GIS), geostatistical modeling, and Unmanned Aerial Vehicles (UAVs), provide powerful tools to monitor, assess, and manage soil and crop dynamics at multiple spatial and temporal scales. This paper reviews the role of geospatial approaches in evaluating soil degradation specifically soil erosion, salinization, and nutrient loss and their impacts on crop productivity. Techniques such as the Revised Universal Soil Loss Equation (RUSLE), digital soil mapping (DSM), and vegetation indices (NDVI and EVI) enable precise assessment of soil quality, crop health, and yield prediction. Furthermore, integration of artificial intelligence (AI), Internet of Things (IoT), and machine learning with geospatial data enhances precision agriculture by enabling real-time monitoring, resource optimization, and evidence-based decision-making. While these technologies hold strong potential for promoting sustainable agriculture, challenges such as high initial costs, large data management, and technical expertise requirements remain. Future directions emphasize the integration of multi-source geospatial data with AI-driven analytics to develop sustainable land management strategies, improve soil health, and optimize crop productivity under changing climatic conditions. This review highlights that geospatial technologies are indispensable for achieving sustainable agricultural practices and ensuring long-term food security.