Land cover and management factor in soil erosion assessments: where do we stand and where are we going?

EGU25-15855, updated on 07 Oct 2025 https://doi.org/10.5194/egusphere-egu25-15855 EGU General Assembly 2025 © Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License. Land cover and management factor in soil erosion assessments: where do we stand and where are we going? Melissa Latella, Gabriel Cerveira, Alessandro D'Anca, Monia Santini, and Manuela Balzarolo CMCC Foundation – Euro-Mediterranean Center on Climate Change, Italy ([email protected]) Rainfall-induced soil erosion is recognized as a significant threat to both human and ecosystem health, leading to habitat degradation, food insecurity, disruptions to socio-economic activities, and damage to infrastructure. Addressing and mitigating soil erosion has become a priority in global and national strategies, therefore requiring multitemporal large-scale assessments to understand how precipitation patterns, soil properties, and surface conditions interact and contribute to erosion in specific areas and over time. In these assessments, the land cover and management, and natural vegetation dynamics components play a critical role in reducing soil susceptibility to erosion. These components are represented by a specific parameter (C-factor) in the Universal Soil Loss Equation (USLE) and its updated versions. While numerous methods exist for determining the C-factor (e.g., in situ survey, remote sensing observation, data-driven models), these approaches are diverse and often have limitations, from neglecting phenological sub-annual dynamics to long timeliness, low update frequency, and coarse spatial resolution, among others. As a result, making their selection for specific purposes is challenging. In this talk, we will present a comprehensive review of existing methodologies to assess C-factor by examining their development, strengths, and drawbacks. We will provide practical examples from selected case studies across Europe to show methods’ applicability, allow cross-comparison, and guide their choice. Finally, we will explore emerging methodologies leveraging Earth Observation and Artificial Intelligence and the advances in neural networks trained on ESA Sentinel-2 data within the framework of two European-funded projects (i.e., SDGs-EYES and EO4EU). Powered by TCPDF (www.tcpdf.org)