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Spatial and temporal assessment of soil degradation risk in Europe

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Abstract

Soil degradation threatens agricultural productivity and ecosystem resilience across Europe, yet spatially consistent assessments of its intensity and drivers remain limited. In this study, we used Soil Degradation Proxy (SDP), that integrates four key indicators of soil degradation, including erosion rate, soil pH, electrical conductivity, and organic carbon content, to quantify soil degradation risk. Using over 38,000 LUCAS topsoil observations and a machine learning model trained on climate, land cover, topographic, soil parent material properties, and spectral variables, we map annual SDP values between years 2000 to 2022 across Europe. Results show soil degradation risk is highest in southern Europe, especially in intensively managed and sparsely vegetated landscapes. Over the past two decades, approximately 7.1% of land area across the EU and the UK has experienced increasing degradation risk (most notably across Eastern Europe), with rainfed croplands emerging as the most affected land cover type. Land cover is the most influential driver, modulating effects of climatic variables such as precipitation and temperature on SDP. This data-driven framework provides a consistent and scalable approach for monitoring soil degradation risk and offers actionable insights to support targeted conservation and EU-wide policy implementation.

Data availability

The required data of the study explained in Sect. Results were obtained from the following sources: The LUCAS observations are available at https://esdac.jrc.ec.europa.eu/content/lucas2015-topsoil-data, elevation data are obtained from https:/doi.org/10.5270/ESA-c5d3d65, MODIS observations are retrieved from https://doi.org/10.5067/MODIS/MOD11A2.061; https://doi.org/10.5067/MODIS/MOD13A2.061; and https://doi.org/10.5067/MODIS/MOD09GA.061, soil parent material properties were downloaded from SoilGrids https://doi.org/10.17027/isric-soilgrids.713396fa-1687-11ea-a7c0-a0481ca9e724, land cover datasets are taken from Copernicus Global Land Service https://doi.org/10.24381/cds.006f2c9a, and lithology maps are obtained from https://doi.org/10.5281/zenodo.12607973.

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Acknowledgements

The authors thank the European Commission’s Joint Research Centre for access to LUCAS datasets, the Copernicus programme for providing land cover data, the European Space Agency for DEM data, the NASA MODIS team for vegetation and tillage indices, ISRIC – SoilGrids for global soil property data, the ECMWF for ERA5-Land climate data, and OpenGeoHub for providing the lithology dataset, derived from the EGDI / OneGeology 1:1 M scale map.

Funding

Open Access funding enabled and organized by Projekt DEAL. This research is part of the project AI4SoilHealth (Accelerating collection and use of soil health information using AI technology to support the Soil Deal for Europe and EU Soil Observatory) to support the Soil Deal for Europe and EU Soil funded Horizon Europe (Grant No. 101086179). P.B. was funded by the Swiss State Secretariat for Education, Research and Innovation (SERI), grant agreement no. 101086179 (Horizon Europe name AI4SoilHealth).

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Authors and Affiliations

  1. Institute of Geo-Hydroinformatics, Hamburg University of Technology, Hamburg, Germany

    Mehdi H. Afshar, Milad Aminzadeh & Nima Shokri

  2. United Nations University Hub on Engineering to Face Climate Change at the Hamburg University of Technology, United Nations University Institute for Water, Environment and Health (UNU-INWEH), Hamburg, Germany

    Mehdi H. Afshar, Milad Aminzadeh & Nima Shokri

  3. The Climate and Environmental Research Institute NILU, P.O. Box 100, Kjeller, 2027, Norway

    Amirhossein Hassani

  4. Department of Environmental Sciences, University of Basel, Basel, 4056, Switzerland

    Pasquale Borrelli

  5. European Commission, Joint Research Centre (JRC), Ispra, Italy

    Panos Panagos

  6. Centre for Ecology & Hydrology, Bangor, UK

    David A. Robinson

  7. Department of Civil & Environmental Engineering, University of Nevada, Reno, NV, USA

    Dani Or

  8. Environmental Systems Science, ETH Zurich, Zurich, Switzerland

    Dani Or

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  1. Mehdi H. Afshar
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  2. Amirhossein Hassani
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  3. Milad Aminzadeh
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  4. Pasquale Borrelli
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Contributions

Conceptualization: All authors, Methodology: MHA, AH, Validation: All authors, Formal analysis: MHA, Investigation: All authors, Data Curation: MHA, AH, Writing – Original Draft: MHA, Writing – Review & Editing: All authors, Visualization: MHA, Supervision: NS, Funding acquisition: NS.

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Mehdi H. Afshar or Nima Shokri.

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Afshar, M.H., Hassani, A., Aminzadeh, M. et al. Spatial and temporal assessment of soil degradation risk in Europe.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33318-7

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  • DOI: https://doi.org/10.1038/s41598-025-33318-7

Keywords

  • Soil monitoring
  • Machine learning
  • Land cover
  • Climate variability
  • Soil health indicator
  • Environmental policy

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