About this Research Topic
Soil erosion is a critical environmental concern that affects land productivity, water quality, and ecosystem health across the globe. With increasing land degradation driven by both natural processes and anthropogenic activities, understanding and predicting soil erosion susceptibility has become a high priority for land management, conservation, and sustainable development initiatives. Recent advances in geospatial technologies and the growing availability of open-source geospatial data present new avenues for enhancing soil erosion research. Such advances enable researchers to analyze complex interactions of erosion factors, monitor erosion dynamics, and develop predictive models with improved accuracy.
This collection aims to bring together cutting-edge research that leverages open-source geospatial data to improve soil erosion mapping, modelling, and prediction. Specifically, we welcome contributions that address the challenge of accurately identifying actual and potential soil erosion hotspots, as well as assessing susceptibility to erosion under varying anthropogenic and natural forces. Additionally, the collection explores the integration of multi-source, free geospatial data (e.g., satellite imagery, topographic data, land use/cover, and climate datasets) with advanced analytical techniques such as machine learning, Multi-Criteria Decision Making (MCDM), Geographic Information System (GIS), and the Revised Universal Soil Loss Equation (RUSLE) to create more robust and scalable erosion susceptibility maps.
We welcome original research articles that cover a range of geospatial and computational techniques to improve the spatial resolution, accuracy, and reliability of erosion models using open-source platforms and tools to enhance the accessibility and reproducibility of soil erosion research. Comprehensive review articles summarizing the state of the art in leveraging geospatial data for soil erosion research and highlight future directions are also welcome. Manuscripts addressing the following themes may be submitted:
• Innovations in erosion mapping techniques using open-source geospatial data;
• Applications of machine learning models for improving the accuracy of soil erosion susceptibility assessments and identification of influencing factors;
• Enhanced parameterization of the RUSLE model using open-source geospatial datasets;
• Identification, mapping, and managing soil erosion hotspots at different spatial scales;
• Integration of multi-source data (e.g., remote sensing, DEMs, land cover, and climate data) to improve erosion prediction models;
• Methods for assessing the accuracy and uncertainty of erosion susceptibility models.
This collection aims to bring together cutting-edge research that leverages open-source geospatial data to improve soil erosion mapping, modelling, and prediction. Specifically, we welcome contributions that address the challenge of accurately identifying actual and potential soil erosion hotspots, as well as assessing susceptibility to erosion under varying anthropogenic and natural forces. Additionally, the collection explores the integration of multi-source, free geospatial data (e.g., satellite imagery, topographic data, land use/cover, and climate datasets) with advanced analytical techniques such as machine learning, Multi-Criteria Decision Making (MCDM), Geographic Information System (GIS), and the Revised Universal Soil Loss Equation (RUSLE) to create more robust and scalable erosion susceptibility maps.
We welcome original research articles that cover a range of geospatial and computational techniques to improve the spatial resolution, accuracy, and reliability of erosion models using open-source platforms and tools to enhance the accessibility and reproducibility of soil erosion research. Comprehensive review articles summarizing the state of the art in leveraging geospatial data for soil erosion research and highlight future directions are also welcome. Manuscripts addressing the following themes may be submitted:
• Innovations in erosion mapping techniques using open-source geospatial data;
• Applications of machine learning models for improving the accuracy of soil erosion susceptibility assessments and identification of influencing factors;
• Enhanced parameterization of the RUSLE model using open-source geospatial datasets;
• Identification, mapping, and managing soil erosion hotspots at different spatial scales;
• Integration of multi-source data (e.g., remote sensing, DEMs, land cover, and climate data) to improve erosion prediction models;
• Methods for assessing the accuracy and uncertainty of erosion susceptibility models.
Keywords: erosion susceptibility, prediction, spatial resolution, erosion factors, multi-source data, accuracy
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
