High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment

Siewert, Matthias B.

doi:https://doi.org/10.5194/bg-15-1663-2018

Articles | Volume 15, issue 6

https://doi.org/10.5194/bg-15-1663-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Changing Permafrost in the Arctic and its Global Effects in...

https://doi.org/10.5194/bg-15-1663-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 6

Research article

|

21 Mar 2018

Research article |

| 21 Mar 2018

High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment

Matthias B. Siewert

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Final revised paper (published on 21 Mar 2018)
Supplement to the final revised paper
Preprint (discussion started on 04 Aug 2017)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of Siewert', Anonymous Referee #1, 21 Sep 2017
- AC1: 'RC1: 'Review of Siewert', Anonymous Referee #1, 21 Sep 2017', Matthias Siewert, 08 Nov 2017
RC2: 'Siewert review', Anonymous Referee #2, 26 Sep 2017
- AC2: 'reply: RC2: 'Siewert review', Anonymous Referee #2, 26 Sep 2017', Matthias Siewert, 08 Nov 2017
RC3: 'Review of Siewert (2017), High-resolution digital mapping of soil organic carbon in permafrost terrain using machine-learning: A case study in a sub-Arctic peatland environment', Anonymous Referee #3, 03 Oct 2017
- AC3: 'Reply: RC3: 'Review of Siewert (2017), High-resolution', Matthias Siewert, 08 Nov 2017
RC4: 'A very high-resolution mapping of sub-Arctic peatland soil organic carbon', Anonymous Referee #4, 03 Oct 2017
- AC4: 'reply: RC4: High-resolution...', Matthias Siewert, 08 Nov 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (30 Nov 2017) by Susan Natali

AR by Matthias Siewert on behalf of the Authors (11 Jan 2018) Author's response Manuscript

ED: Referee Nomination & Report Request started (20 Jan 2018) by Susan Natali

RR by Anonymous Referee #1 (31 Jan 2018)

ED: Publish as is (13 Feb 2018) by Susan Natali

AR by Matthias Siewert on behalf of the Authors (15 Feb 2018)

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Short summary

Large amounts of soil organic carbon are stored in the circumpolar permafrost region. This article aims to improve how we map this carbon. Typically the amount of soil organic carbon is estimated using soil or land cover maps. Here the amount of carbon is modeled using machine learning. This is done at a very fine spatial resolution of 1 × 1 m. This reveals a lot of small-scale landscape variability and underlines the importance of permafrost-related landforms vulnerable to a warming climate.