Articles | Volume 6, issue 2
SOIL, 6, 389–397, 2020
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.
SOIL, 6, 389–397, 2020
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
Game theory interpretation of digital soil mapping convolutional neural networks
José Padarian et al.
Viewed
Total article views: 2,609 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Mar 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,841 | 703 | 65 | 2,609 | 63 | 65 |
- HTML: 1,841
- PDF: 703
- XML: 65
- Total: 2,609
- BibTeX: 63
- EndNote: 65
Total article views: 1,945 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Aug 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,497 | 419 | 29 | 1,945 | 25 | 28 |
- HTML: 1,497
- PDF: 419
- XML: 29
- Total: 1,945
- BibTeX: 25
- EndNote: 28
Total article views: 664 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Mar 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 344 | 284 | 36 | 664 | 38 | 37 |
- HTML: 344
- PDF: 284
- XML: 36
- Total: 664
- BibTeX: 38
- EndNote: 37
Viewed (geographical distribution)
Total article views: 2,296 (including HTML, PDF, and XML)
Thereof 2,244 with geography defined
and 52 with unknown origin.
Total article views: 1,782 (including HTML, PDF, and XML)
Thereof 1,734 with geography defined
and 48 with unknown origin.
Total article views: 514 (including HTML, PDF, and XML)
Thereof 510 with geography defined
and 4 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
18 citations as recorded by crossref.
- Evaluating the Splintex model for estimating the soil water retention curve for a wide range of soils A. da Silva et al. 10.1016/j.still.2021.104974
- Explainable artificial intelligence: a comprehensive review D. Minh et al. 10.1007/s10462-021-10088-y
- Towards to intelligent routing for DTN protocols using machine learning techniques E. Alaoui et al. 10.1016/j.simpat.2021.102475
- Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory A. Mohammadifar et al. 10.1016/j.catena.2021.105178
- Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis M. Lebrasse et al. 10.1007/s12237-022-01050-4
- Integrated modelling for mapping spatial sources of dust in central Asia - An important dust source in the global atmospheric system H. Gholami et al. 10.1016/j.apr.2021.101173
- A novel approach to identify the spectral bands that predict moisture content in canola and wheat J. Torres-Tello & S. Ko 10.1016/j.biosystemseng.2021.08.004
- Gap-free global annual soil moisture: 15 km grids for 1991–2018 M. Guevara et al. 10.5194/essd-13-1711-2021
- Prediction of various soil properties for a national spatial dataset of Scottish soils based on four different chemometric approaches: A comparison of near infrared and mid-infrared spectroscopy R. Haghi et al. 10.1016/j.geoderma.2021.115071
- Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction E. Khalili et al. 10.3389/fpls.2020.590529
- Revealing drivers and risks for power grid frequency stability with explainable AI J. Kruse et al. 10.1016/j.patter.2021.100365
- Uncovering the Past and Future Climate Drivers of Wheat Yield Shocks in Europe With Machine Learning P. Zhu et al. 10.1029/2020EF001815
- Identifying causes of crop yield variability with interpretive machine learning E. Jones et al. 10.1016/j.compag.2021.106632
- Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data O. Odebiri et al. 10.1016/j.geoderma.2022.115695
- Towards near real-time national-scale soil water content monitoring using data fusion as a downscaling alternative I. Fuentes et al. 10.1016/j.jhydrol.2022.127705
- Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China X. Li et al. 10.3390/rs13153011
- Soil properties: Their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks L. Zhong et al. 10.1016/j.geoderma.2021.115366
- Interpretation of Convolutional Neural Networks for Acid Sulfate Soil Classification A. Beucher et al. 10.3389/fenvs.2021.809995
17 citations as recorded by crossref.
- Evaluating the Splintex model for estimating the soil water retention curve for a wide range of soils A. da Silva et al. 10.1016/j.still.2021.104974
- Explainable artificial intelligence: a comprehensive review D. Minh et al. 10.1007/s10462-021-10088-y
- Towards to intelligent routing for DTN protocols using machine learning techniques E. Alaoui et al. 10.1016/j.simpat.2021.102475
- Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory A. Mohammadifar et al. 10.1016/j.catena.2021.105178
- Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: a Semi-automated Remote Sensing Analysis M. Lebrasse et al. 10.1007/s12237-022-01050-4
- Integrated modelling for mapping spatial sources of dust in central Asia - An important dust source in the global atmospheric system H. Gholami et al. 10.1016/j.apr.2021.101173
- A novel approach to identify the spectral bands that predict moisture content in canola and wheat J. Torres-Tello & S. Ko 10.1016/j.biosystemseng.2021.08.004
- Gap-free global annual soil moisture: 15 km grids for 1991–2018 M. Guevara et al. 10.5194/essd-13-1711-2021
- Prediction of various soil properties for a national spatial dataset of Scottish soils based on four different chemometric approaches: A comparison of near infrared and mid-infrared spectroscopy R. Haghi et al. 10.1016/j.geoderma.2021.115071
- Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction E. Khalili et al. 10.3389/fpls.2020.590529
- Revealing drivers and risks for power grid frequency stability with explainable AI J. Kruse et al. 10.1016/j.patter.2021.100365
- Uncovering the Past and Future Climate Drivers of Wheat Yield Shocks in Europe With Machine Learning P. Zhu et al. 10.1029/2020EF001815
- Identifying causes of crop yield variability with interpretive machine learning E. Jones et al. 10.1016/j.compag.2021.106632
- Deep learning-based national scale soil organic carbon mapping with Sentinel-3 data O. Odebiri et al. 10.1016/j.geoderma.2022.115695
- Towards near real-time national-scale soil water content monitoring using data fusion as a downscaling alternative I. Fuentes et al. 10.1016/j.jhydrol.2022.127705
- Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China X. Li et al. 10.3390/rs13153011
- Soil properties: Their prediction and feature extraction from the LUCAS spectral library using deep convolutional neural networks L. Zhong et al. 10.1016/j.geoderma.2021.115366
1 citations as recorded by crossref.
Latest update: 05 Jul 2022
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)...
