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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Cited articles

Adhikari, K., Hartemink, A. E., Minasny, B., Kheir, R. B., Greve, M. B., and Greve, M. H.: Digital mapping of soil organic carbon contents and stocks in Denmark, PloS one, 9, e105519, https://doi.org/10.1371/journal.pone.0105519, 2014. a, b
Akpa, S. I., Odeh, I. O., Bishop, T. F., Hartemink, A. E., and Amapu, I. Y.: Total soil organic carbon and carbon sequestration potential in Nigeria, Geoderma, 271, 202–215, 2016. a, b
Angelini, M. E. and Heuvelink, G. B.: Including spatial correlation in structural equation modelling of soil properties, Spat. Stat.-Nath., 25, 35–51, 2018. a
Angelini, M., Heuvelink, G., and Kempen, B.: Multivariate mapping of soil with structural equation modelling, Eur. J. Soil Sci., 68, 575–591, 2017. a
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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.