Articles | Volume 11, issue 2
https://doi.org/10.5194/soil-11-763-2025
https://doi.org/10.5194/soil-11-763-2025
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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-1046', Anonymous Referee #1, 11 Apr 2025
    • AC2: 'Reply on RC1', Julien Vollering, 10 Jun 2025
  • RC2: 'Comment on egusphere-2025-1046', Anonymous Referee #2, 22 Apr 2025
    • AC1: 'Reply on RC2', Julien Vollering, 10 Jun 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (07 Jul 2025) by Jonathan Maynard
AR by Julien Vollering on behalf of the Authors (05 Aug 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (22 Aug 2025) by Jonathan Maynard
ED: Publish as is (26 Aug 2025) by Raphael Viscarra Rossel (Executive editor)
AR by Julien Vollering on behalf of the Authors (29 Aug 2025)  Author's response 
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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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