Articles | Volume 6, issue 2
SOIL, 6, 389–397, 2020
https://doi.org/10.5194/soil-6-389-2020
SOIL, 6, 389–397, 2020
https://doi.org/10.5194/soil-6-389-2020

Original research article 18 Aug 2020

Original research article | 18 Aug 2020

Game theory interpretation of digital soil mapping convolutional neural networks

José Padarian et al.

Download

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

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)
Download
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.