Articles | Volume 6, issue 2
SOIL, 6, 389–397, 2020
SOIL, 6, 389–397, 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.

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Cited articles

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
Anwar, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., and Khan, M. K.: Medical image analysis using convolutional neural networks: a review, J. Med. Syst., 42, 226,, 2018. a
Behrens, T., MacMillan, R. A., Rossel, R. A. V., Schmidt, K., and Lee, J.: Teleconnections in spatial modelling, Geoderma, 354, 113854,, 2019. a
Bui, E. N., Henderson, B. L., and Viergever, K.: Knowledge discovery from models of soil properties developed through data mining, Ecol. Model., 191, 431–446, 2006. a
Casanova, M., Salazar, O., Seguel, O., and Luzio, W.: The soils of Chile, Springer, London,, 2013. a
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.