Application of Artificial Intelligence and Geospatial Satellite Data in Assessing Soil Erosion Vulnerability and Sediment Yield Driving Factors.

Abstract

Soil erosion represents one of the most critical geo-environmental challenges influencing ecological equilibrium, hydrological sustainability, agricultural productivity, and watershed stability across diverse physiographic landscapes. Accelerated erosion processes induced by anthropogenic interventions, climatic perturbations, deforestation, unscientific agricultural practices, and geomorphological instability have substantially intensified sediment transportation dynamics and environmental degradation globally. Conventional methodologies employed for erosion assessment often exhibit substantial limitations associated with spatial heterogeneity, temporal inconsistency, computational inefficiency, and restricted predictive capability. Consequently, the integration of Artificial Intelligence (AI), machine learning paradigms, and geospatial satellite remote sensing technologies has emerged as an advanced scientific framework for environmental vulnerability modeling and predictive spatial analytics.

The present investigation examines the application of AI-enabled geospatial satellite datasets for evaluating soil erosion vulnerability and identifying dominant sediment yield driving factors through advanced computational modeling approaches. Multi-spectral satellite imagery derived from Landsat-8, Sentinel-2, MODIS, and Digital Elevation Models (DEM) was integrated with Geographic Information Systems (GIS), machine learning algorithms, and deep neural computational architectures for spatial classification and predictive erosion analysis. Environmental variables including rainfall erosivity, topographic slope, vegetation indices, drainage density, land use-land cover transformation, and anthropogenic disturbances were systematically analyzed to determine their contribution toward sediment yield generation.

Advanced computational algorithms such as Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) were employed for erosion susceptibility zonation and predictive modeling. The findings indicate that AI-based geospatial systems significantly improve predictive precision, automate environmental intelligence generation, and enhance real-time monitoring capabilities. The study further demonstrates that steep topographical gradients, vegetation depletion, intensive rainfall variability, and land use modifications constitute the dominant drivers accelerating sediment transport and geomorphic instability.

The integration of AI, remote sensing, geospatial analytics, and environmental informatics offers substantial opportunities for sustainable watershed management, ecological restoration, climate adaptation planning, and precision environmental governance. The proposed framework contributes toward the development of intelligent environmental monitoring systems capable of supporting data-driven policy formulation and sustainable resource conservation strategies.