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.
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.
the Creative Commons Attribution 4.0 License.
Original research article
| 
18 Aug 2020
Original research article |
| 18 Aug 2020
| 18 Aug 2020
Game theory interpretation of digital soil mapping convolutional neural networks
José Padarian
CORRESPONDING AUTHOR
Sydney Institute of Agriculture, School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia
Alex B. McBratney
Sydney Institute of Agriculture, School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia
Budiman Minasny
Sydney Institute of Agriculture, School of Life and Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia
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Latest update: 20 Nov 2024
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.
In this paper we introduce the use of game theory to interpret a digital soil mapping (DSM)...
