Artificial Intelligence for Soil Erosion Prediction and Modeling

Dear Colleagues,

Soil erosion is one of the most pressing environmental challenges of the 21st century, contributing to the degradation of arable land, loss of biodiversity, and increased sedimentation in water bodies. Driven mostly by natural forces, such as rainfall and wind, as well as by anthropogenic activities including deforestation, agriculture, and urbanization, soil erosion threatens the global Water–Energy–Food–Ecosystem Nexus and sustainable development. Traditional methods for predicting, monitoring, and controlling soil erosion often struggle to capture the complexity and variability of erosion processes across different scales and regions. At the same time, artificial intelligence (AI) has emerged as a powerful tool that can enhance our ability to understand and manage soil erosion, providing new insights into the factors driving erosion.

This Special Issue focuses on the application of AI in soil erosion research and management, covering all research where AI is integrated into soil erosion modeling. We aim to showcase innovative methodologies and workflows, integrations of AI with remote sensing technologies, GIS, and big data analytics, with the end goal of improving the overall soil erosion mapping/prediction in both space and time. However, the reliance on AI should not overshadow the importance of traditional knowledge and practices in soil conservation, highlighting the need for interdisciplinary approaches and ethical guidelines in AI applications.

This Special Issue will welcome manuscripts (original research articles and review papers) that link the following themes:

• Soil erosion mapping by using Earth Observation technologies (e.g., satellite imagery, unmanned aerial vehicles, or other airborne data) coupled with AI techniques
• Development of innovative AI-driven soil erosion frameworks.
• Assessing the impact of different soil erosion processes (e.g., water, wind, and tillage erosion) in soil conservation and erosion control through AI.
• Hybrid AI models combining physical and data-driven approaches to soil erosion.
• Case studies demonstrating AI applications in soil erosion mitigation under climate change scenarios.

We look forward to receiving your original research articles and reviews.

Dr. Nikiforos Samarinas
Dr. Nikolaos L. Tsakiridis
Dr. Nikolaos Tziolas
Dr. Ioannis N. Daliakopoulos
Guest Editors

Manuscript Submission Information

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• soil erosion
• artificial intelligence
• earth observation
• big data
• sediment transport
• water resources
• environmental impact
• climate change