Articles | Volume 12, issue 1
https://doi.org/10.5194/soil-12-321-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-12-321-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing the potential of complex artificial neural networks for modelling small-scale soil erosion by water
Nils Barthel
CORRESPONDING AUTHOR
Physical Geography and Landscape Ecology Section, Institute of Earth System Sciences, Leibniz University Hannover, Schneiderberg 50, 30167, Hannover, Germany
Simone Ott
Physical Geography and Landscape Ecology Section, Institute of Earth System Sciences, Leibniz University Hannover, Schneiderberg 50, 30167, Hannover, Germany
Benjamin Burkhard
Physical Geography and Landscape Ecology Section, Institute of Earth System Sciences, Leibniz University Hannover, Schneiderberg 50, 30167, Hannover, Germany
Bastian Steinhoff-Knopp
Coordination Unit Climate Soil Biodiversity, Thünen-Institute, Bundesallee 49, 38116, Braunschweig, Germany
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The Cryosphere, 19, 3949–3970, https://doi.org/10.5194/tc-19-3949-2025, https://doi.org/10.5194/tc-19-3949-2025, 2025
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Snow has a major impact on palsa dynamics, yet our understanding of its distribution at the small scale remains limited. We used unoccupied aerial system (UAS) light detection and ranging (lidar) and ground truth data in combination with machine learning to model snow distribution at three palsa sites. We identified extremes in snow depth corresponding to palsa topography, providing insights into the influence of the distribution on their dynamics. The results demonstrate the usability of machine learning and UAS lidar for small-scale snow distribution mapping.
Mariana Verdonen, Alexander Störmer, Eliisa Lotsari, Pasi Korpelainen, Benjamin Burkhard, Alfred Colpaert, and Timo Kumpula
The Cryosphere, 17, 1803–1819, https://doi.org/10.5194/tc-17-1803-2023, https://doi.org/10.5194/tc-17-1803-2023, 2023
Short summary
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The study revealed a stable and even decreasing thickness of thaw depth in peat mounds with perennially frozen cores, despite overall rapid permafrost degradation within 14 years. This means that measuring the thickness of the thawed layer – a commonly used method – is alone insufficient to assess the permafrost conditions in subarctic peatlands. The study showed that climate change is the main driver of these permafrost features’ decay, but its effect depends on the peatland’s local conditions.
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Short summary
This study compares neural networks and a random forest model for predicting soil erosion in agricultural cropland using long-term data from northern Germany. All models captured general erosion patterns, while more complex neural networks slightly improved the distinction between soil loss classes. A permutation importance analysis identified slope and machine direction vs. aspect as the most influential predictors across all models.
This study compares neural networks and a random forest model for predicting soil erosion in...