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Articles | Volume 11, issue 2
Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-763-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-11-763-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 2
Original research article
 | 
06 Oct 2025
Original research article |  | 06 Oct 2025

Terrain is a stronger predictor of peat depth than airborne radiometrics in Norwegian landscapes

Julien Vollering, Naomi Gatis, Mette Kusk Gillespie, Karl-Kristian Muggerud, Sigurd Daniel Nerhus, Knut Rydgren, and Mikko Sparf

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Short summary
Peat depth is crucial to peatland management but often unknown. We used machine learning to map peat depth in two Norwegian landscapes, based on terrain and remotely sensed radiation. We found that terrain, especially elevation and valley bottom flatness, predicted peat depth better than radiation. Our approach improved existing maps but struggled to identify very deep peat, demonstrating that it can support regional planning but not replace field measurements for local carbon stock assessments.
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