Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model

• Published in: Land (Basel) 2024-02, Vol.13 (2), p.174
• Main Authors: Samarinas, Nikiforos, Tsakiridis, Nikolaos, Kalopesa, Eleni, Zalidis, George
• Format: Article
• Language: English
• Subjects: Agricultural land; Agricultural policy; Agriculture; Artificial intelligence; Classification; Climatic data; Copernicus; Datasets; Decision making; Digital Elevation Models; Digital mapping; earth observation; Environmental aspects; Environmental policy; Ground truth; Land use planning; Lowlands; machine learning; Mountain regions; Mountainous areas; Mountains; Organic carbon; Organic soils; Regional planning; Remote sensing; Soil erosion; Soil layers; Soil mapping; Soil maps; Soil properties; Soil texture; Soil water; spaceborne data; Spatial discrimination; Spatial resolution; Sustainable development; Texture; Topography; Vegetation; Water erosion
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land13020174

The existing digital soil maps are mainly characterized by coarse spatial resolution and are not up to date; thus, they are unable to support the physical process-based models for improved predictions. The overarching objective of this work is oriented toward a data-driven approach and datacube-based tools (Soil Data Cube), leveraging Sentinel-2 imagery data, open access databases, ground truth soil data and Artificial Intelligence (AI) architectures to provide enhanced geospatial layers into the Revised Universal Soil Loss Equation (RUSLE) model, improving both the reliability and the spatial resolution of the final map. The proposed methodology was implemented in the agricultural area of the Imathia Regional Unit (northern Greece), which consists of both mountainous areas and lowlands. Enhanced soil maps of Soil Organic Carbon (SOC) and soil texture were generated at 10 m resolution through a time-series analysis of satellite data and an XGBoost (eXtrene Gradinent Boosting) model. The model was trained by 84 ground truth soil samples (collected from agricultural fields) taking into account also additional environmental covariates (including the digital elevation model and climatic data) and following a Digital Soil Mapping (DSM) approach. The enhanced layers were introduced into the RUSLE’s soil erodibility factor (K-factor), producing a soil erosion layer with high spatial resolution. Notable prediction accuracy was achieved by the AI model with R2 0.61 for SOC and 0.73, 0.67 and 0.63 for clay, sand, and silt, respectively. The average annual soil loss of the unit was found to be 1.76 ton/ha/yr with 6% of the total agricultural area suffering from severe erosion (>11 ton/ha/yr), which was mainly found in the mountainous border regions, showing the strong influence of the mountains in the agricultural fields. The overall methodology could strongly support regional decision making and planning and environmental policies such as the European Common Agricultural Policy (CAP) and the Sustainable Development Goals (SDGs).