Game theory interpretation of digital soil mapping convolutional neural networks

Padarian, José; McBratney, Alex B.; Minasny, Budiman

doi:https://doi.org/10.5194/soil-6-389-2020

Articles | Volume 6, issue 2

https://doi.org/10.5194/soil-6-389-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/soil-6-389-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 6, issue 2

Original research article

|

18 Aug 2020

Original research article |

| 18 Aug 2020

Game theory interpretation of digital soil mapping convolutional neural networks

José Padarian, Alex B. McBratney, and Budiman Minasny

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Final revised paper (published on 18 Aug 2020)
Preprint (discussion started on 30 Mar 2020)

Interactive discussion

Status: closed

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

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- Supplement

RC1: 'Comments on the Discussion paper soil-2020-17', Anonymous Referee #1, 24 Apr 2020
- AC1: 'Response to reviewers', José Padarian, 29 Apr 2020
RC2: 'Game theory interpretation of DSM using convolutional neural networks', Matt Aitkenhead, 28 Apr 2020
- AC1: 'Response to reviewers', José Padarian, 29 Apr 2020
AC1: 'Response to reviewers', José Padarian, 29 Apr 2020

Peer-review completion

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

ED: Publish subject to minor revisions (review by editor) (04 Jun 2020) by Olivier Evrard

AR by José Padarian on behalf of the Authors (24 Jun 2020) Author's response Manuscript

ED: Publish as is (01 Jul 2020) by Olivier Evrard

ED: Publish as is (01 Jul 2020) by John Quinton (Executive editor)

AR by José Padarian on behalf of the Authors (02 Jul 2020) Manuscript

Short summary

In this paper we introduce the use of game theory to interpret a digital soil mapping (DSM) model to understand the contribution of environmental factors to the prediction of soil organic carbon (SOC) in Chile. The analysis corroborated that the SOC model is capturing sensible relationships between SOC and climatic and topographical factors. We were able to represent them spatially (map) addressing the limitations of the current interpretation of models in DSM.