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

Nikiforos Samarinas (), Nikolaos L. Tsakiridis, Eleni Kalopesa and George C. Zalidis
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Nikiforos Samarinas: Spectra Lab Group, Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Nikolaos L. Tsakiridis: Spectra Lab Group, Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Eleni Kalopesa: Spectra Lab Group, Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
George C. Zalidis: Spectra Lab Group, Laboratory of Remote Sensing, Spectroscopy, and GIS, Department of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Land, 2024, vol. 13, issue 2, 1-21

Abstract: 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 R 2 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).

Keywords: spaceborne data; remote sensing; Copernicus; machine learning; earth observation; soil properties; agriculture; big data (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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