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Articles | Volume 5, issue 1
Articles | Volume 5, issue 1
https://doi.org/10.5194/soil-5-79-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-5-79-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 1
Original research article
 | 
26 Feb 2019
Original research article |  | 26 Feb 2019

Using deep learning for digital soil mapping

José Padarian, Budiman Minasny, and Alex B. McBratney

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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.
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