Spatial Assessment of Soil Erosion Using the RUSLE Model and Remote Sensing Data Processed in Google Earth Engine: A Case Study of the Birjand Plain

Document Type : Original Research Article

Authors

1 Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran

2 Department of Environmental Engineering, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran

Abstract

Soil erosion is a major environmental challenge in arid and semi-arid regions, threatening agricultural productivity, water resources, and ecological stability. This study quantified and spatially analyzed soil erosion in the Birjand Plain, eastern Iran, by integrating the Revised Universal Soil Loss Equation (RUSLE) model with satellite-derived data using the Google Earth Engine (GEE) cloud-computing platform. Key erosion factors—including rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practices (P)—were extracted from multi-source remote sensing datasets. The results revealed considerable spatial variability in erosion intensity across the plain. Approximately 67.3% of the area experiences very low soil loss rates (0–10 tons/ha/year), while 22.9% is categorized as low erosion (10–20 tons/ha/year). Moderate (20–40 t/ha/year), high (40–60 t/ha/year), and very high (>60 t/ha/year) erosion zones constitute 7.5%, 2.0%, and 0.2% of the area, respectively. These high-risk zones are often associated with steep terrain, sparse vegetation, and poor land management. The use of GEE facilitated fast, large-scale erosion modeling with high spatial resolution and minimal reliance on ground data. This approach proves to be scalable, cost-efficient, and reproducible, particularly for data-scarce regions. The findings provide valuable insights for land managers and policymakers aiming to prioritize soil conservation efforts and develop sustainable land-use strategies. This research underscores the potential of integrating remote sensing and cloud-based tools with empirical models to enhance environmental monitoring and resilience in erosion-prone landscapes.

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Main Subjects

References

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