Authors: Dr. Ananya Sharma, Rahul Verma
Abstract: Soil erosion poses significant challenges to sustainable land management and agricultural productivity. Remote sensing technology offers a robust, cost effective, and scalable approach for monitoring and assessing soil erosion risk across diverse landscapes. This paper examines the principles, methodologies, and applications of remote sensing in soil erosion risk assessment, emphasizing multi-spectral, hyperspectral, and radar data integration. The research explores the advantages of satellite imagery in temporal monitoring, highlights case studies of erosion mapping in various climatic regions, and evaluates the role of GIS in integrating remote sensing data for predictive modeling. The paper also presents a comparative analysis of commonly used erosion models such as RUSLE, USPED, and WEPP when combined with remote sensing datasets. Results indicate that multi-temporal analysis, particularly using Sentinel-2 and Landsat-8 data, significantly improves the accuracy of soil erosion risk predictions. Finally, the study underscores the need for continuous monitoring frameworks and advanced machine learning approaches to enhance erosion risk management at both local and regional scales.
Keywords: Remote Sensing, Soil Erosion, RUSLE, GIS, Hyperspectral Imaging, Risk Assessment, Sentinel-2, Landsat-8
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