Assessing Soil Erosion Susceptibility in Bayelsa StateNigeria, ‎Using Sentinel-1 in SAR Data

Abstract

Soil erosion is a critical environmental challenge in the Niger Delta region of Nigeria, driven by a combination of fluvial processes, coastal ‎dynamics, and anthropogenic activities. Traditional monitoring methods are often labor-intensive, spatially limited, and struggle to capture ‎the dynamic nature of land surface changes. This study presents a novel framework for assessing soil erosion susceptibility in Bayelsa State, ‎a predominantly low-lying, riverine state within the Niger Delta, by integrating Interferometric Synthetic Aperture Radar (InSAR) data with ‎key environmental parameters within the Google Earth Engine (GEE) cloud-computing platform. Using the Sentinel-1 SAR data from 2019 ‎to 2022, we employed the Small Baseline Subset (SBAS) technique to generate time-series ground deformation maps. Areas of significant ‎subsidence were identified as potential zones of ground instability, which are intrinsically linked to soil erosion susceptibility, particularly in ‎waterlogged and deltaic environments. These deformation signals were then integrated into a Modified Universal Soil Loss Equation ‎‎(MUSLE) framework, combined with ancillary datasets such as Digital Elevation Models (DEM), rainfall data (CHIRPS), and land cover ‎information. The results reveal that deformation rates of 0.2cm/year correspond to uplift. High Susceptibility zones account for roughly ‎‎79.3% of soil erosion, while Moderate Susceptibility zones cover 17.8%, and Low Susceptibility zones are present in 2.9% of the area. This ‎research demonstrates the efficacy of GEE as a powerful tool for large-scale, multi-temporal geospatial analysis and highlights the significant potential of Sentinel-1 InSAR to provide actionable intelligence for land management, infrastructure planning, and mitigation strategies ‎in vulnerable deltaic ecosystems.