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Articles | Volume 11, issue 2
Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-833-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-11-833-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 2
Original research article
 | 
15 Oct 2025
Original research article |  | 15 Oct 2025

Quantifying spatial uncertainty to improve soil predictions in data-sparse regions

Kerstin Rau, Katharina Eggensperger, Frank Schneider, Michael Blaschek, Philipp Hennig, and Thomas Scholten

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Short summary
We developed an uncertainty method to show where machine learning (ML) models predicting soil units are most reliable, especially for transfer tasks. The model was able to correctly predict soil patterns, especially along rivers, in a new but similar region without retraining. It was too confident about common soil types, showing the need for balanced data. This helps improve soil maps and guides better planning for future data collection, saving time and resources while showing uncertainty.
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