Articles | Volume 5, issue 1
SOIL, 5, 79–89, 2019
https://doi.org/10.5194/soil-5-79-2019
SOIL, 5, 79–89, 2019
https://doi.org/10.5194/soil-5-79-2019

Original research article 26 Feb 2019

Original research article | 26 Feb 2019

Using deep learning for digital soil mapping

José Padarian et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (10 Jan 2019) by Bas van Wesemael
AR by José Padarian on behalf of the Authors (18 Jan 2019)  Author's response    Manuscript
ED: Publish as is (05 Feb 2019) by Bas van Wesemael
ED: Publish subject to technical corrections (07 Feb 2019) by Kristof Van Oost(Executive Editor)
AR by José Padarian on behalf of the Authors (08 Feb 2019)  Author's response    Manuscript
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Short summary
Digital soil mapping has been widely used as a cost-effective method for generating soil maps. DSM models are usually calibrated using point observations and rarely incorporate contextual information of the landscape. Here, we use convolutional neural networks to incorporate spatial context. We used as input a 3-D stack of covariate images to simultaneously predict organic carbon content at multiple depths. In this study, our model reduced the error by 30 % compared with conventional techniques.