Mapping potential water repellency of Danish topsoil

Gomes, Lucas Carvalho; Weber, Peter Lystbæk; Hermansen, Cecilie; Danielsen, Anne-Cathrine Storgaard; Gutierrez, Sebastian; Mikstas, Deividas; Pesch, Charles; Greve, Mogens Humlekrog; Moldrup, Per; Robinson, David A. ORCID: https://orcid.org/0000-0001-7290-4867; de Jonge, Lis Wollesen. 2025 Mapping potential water repellency of Danish topsoil. Geoderma, 457, 117280. 10, pp. 10.1016/j.geoderma.2025.117280

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Official URL: https://doi.org/10.1016/j.geoderma.2025.117280

Abstract/Summary

Soil water repellency (SWR) is a natural process and affects water dynamics from nano to ecosystem scales. However, the spatial distribution of SWR at the ecosystem scale, as well as the underlying drivers across diverse habitats, land uses and soil textures, remain underexplored. This study presents a comprehensive survey of SWR in Denmark and its predicted spatial distribution, using approximately 7,500 samples. We used digital soil mapping methods (Quantile Random Forest model) to map and identify the relationship between SWR and various environmental variables, including vegetation (via satellite imagery), soil properties (texture and soil organic carbon), and landforms (slope and wetness index). The predicted maps at 10 m resolution revealed that SWR varies across different land uses and vegetation types, with higher values in areas of natural vegetation (e.g., heathlands and coniferous forests) compared to grasslands and croplands (mostly hydrophilic). The analysis also identified soil organic carbon, Sentinel band 3 (Green band − Chlorophyll absorption) and soil texture as key drivers of spatial variation in SWR at the national extent. We found that soil texture influences SWR intensity, which generally decreases as clay content increases across most land use types, except for heathlands. While the predicted maps provided valuable insights into SWR distribution and its environmental drivers, further research is needed to explore the spatio-temporal dynamics of SWR within each habitat, particularly in relation to soil moisture changes. This study highlights the potential of combining machine learning and remote sensing to provide crucial spatial information for managing water resources and enhancing ecosystem resilience in the face of climate change.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.geoderma.2025.117280
UKCEH and CEH Sections/Science Areas:	Environmental Pressures and Responses (2025-)
ISSN:	0016-7061
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
NORA Subject Terms:	Agriculture and Soil Science
Date made live:	28 Apr 2025 09:29 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/539329

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.geoderma.2025.117280

UKCEH and CEH Sections/Science Areas:

Environmental Pressures and Responses (2025-)

ISSN:

0016-7061

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

NORA Subject Terms:

Agriculture and Soil Science