Articles | Volume 8, issue 2
https://doi.org/10.5194/soil-8-559-2022
© Author(s) 2022. 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-8-559-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
05 Sep 2022
Original research article |
| 05 Sep 2022
| 05 Sep 2022
How well does digital soil mapping represent soil geography? An investigation from the USA
David G. Rossiter
CORRESPONDING AUTHOR
ISRIC – World Soil Information, Postbus 353, Wageningen 6700 AJ, the Netherlands
Section of Soil & Crop Sciences, New York State College of Agriculture and Life Sciences, 233 Emerson Hall, Cornell University, Ithaca, NY 14853, USA
Laura Poggio
ISRIC – World Soil Information, Postbus 353, Wageningen 6700 AJ, the Netherlands
Dylan Beaudette
USDA – NRCS, Soil and Plant Science Division, 19777 Greenley Rd, Sonora, CA 95370, USA
Zamir Libohova
USDA – ARS, Dale Bumpers Small Farms Research Center, 6883 South State Highway 23, Booneville, AR 72927, USA
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Total article views: 5,691 (including HTML, PDF, and XML)
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Total article views: 3,403 (including HTML, PDF, and XML)
Thereof 3,240 with geography defined
and 163 with unknown origin.
Total article views: 2,288 (including HTML, PDF, and XML)
Thereof 2,177 with geography defined
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Cited
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- Soil organic carbon stock prediction using multi-spatial resolutions of environmental variables: How well does the prediction match local references? M. Zeraatpisheh et al. 10.1016/j.catena.2023.107197
- National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France A. Suleymanov et al. 10.1016/j.geodrs.2024.e00801
- Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al. 10.1111/ejss.13463
- Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0 A. Ali et al. 10.5194/soil-10-189-2024
- Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping P. Owens et al. 10.1016/j.geoderma.2024.116911
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model Z. Libohova et al. 10.1016/j.geodrs.2024.e00863
- Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning S. Sahaar & J. Niemann 10.3390/rs16193699
- Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales T. Tan et al. 10.1021/acs.est.4c01172
- Estimating natural soil drainage classes in the Wisconsin till plain of the Midwestern U.S.A. based on lidar derived terrain indices: Evaluating prediction accuracy of multinomial logistic regression and machine learning algorithms S. Rahmani et al. 10.1016/j.geodrs.2023.e00728
- Digital soil mapping in the Russian Federation: A review A. Suleymanov et al. 10.1016/j.geodrs.2024.e00763
- Uncovering the effects of Urmia Lake desiccation on soil chemical ripening using advanced mapping techniques F. Shahbazi et al. 10.1016/j.catena.2023.107440
- Multiple soil map comparison highlights challenges for predicting topsoil organic carbon concentration at national scale C. Feeney et al. 10.1038/s41598-022-05476-5
- Multiscale evaluations of global, national and regional digital soil mapping products in France B. Lemercier et al. 10.1016/j.geoderma.2022.116052
- Reducing location error of legacy soil profiles leads to improvement in digital soil mapping G. Shi et al. 10.1016/j.geoderma.2024.116912
- How well does digital soil mapping represent soil geography? An investigation from the USA D. Rossiter et al. 10.5194/soil-8-559-2022
- Locally enhanced digital soil mapping in support of a bottom-up approach is more accurate than conventional soil mapping and top-down digital soil mapping M. Bohn & B. Miller 10.1016/j.geoderma.2024.116781
- High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic D. Žížala et al. 10.1016/j.catena.2022.106024
- Influence of Land Use and Topographic Factors on Soil Organic Carbon Stocks and Their Spatial and Vertical Distribution K. Blackburn et al. 10.3390/rs14122846
13 citations as recorded by crossref.
- Variation in fine-scale water table depth drives abundance of a unique semi-terrestrial crayfish species M. Carlson et al. 10.7717/peerj.17330
- Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity F. Kaya et al. 10.3390/land12040819
- Soil organic carbon stock prediction using multi-spatial resolutions of environmental variables: How well does the prediction match local references? M. Zeraatpisheh et al. 10.1016/j.catena.2023.107197
- National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France A. Suleymanov et al. 10.1016/j.geodrs.2024.e00801
- Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al. 10.1111/ejss.13463
- Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0 A. Ali et al. 10.5194/soil-10-189-2024
- Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping P. Owens et al. 10.1016/j.geoderma.2024.116911
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model Z. Libohova et al. 10.1016/j.geodrs.2024.e00863
- Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning S. Sahaar & J. Niemann 10.3390/rs16193699
- Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales T. Tan et al. 10.1021/acs.est.4c01172
- Estimating natural soil drainage classes in the Wisconsin till plain of the Midwestern U.S.A. based on lidar derived terrain indices: Evaluating prediction accuracy of multinomial logistic regression and machine learning algorithms S. Rahmani et al. 10.1016/j.geodrs.2023.e00728
- Digital soil mapping in the Russian Federation: A review A. Suleymanov et al. 10.1016/j.geodrs.2024.e00763
- Uncovering the effects of Urmia Lake desiccation on soil chemical ripening using advanced mapping techniques F. Shahbazi et al. 10.1016/j.catena.2023.107440
7 citations as recorded by crossref.
- Multiple soil map comparison highlights challenges for predicting topsoil organic carbon concentration at national scale C. Feeney et al. 10.1038/s41598-022-05476-5
- Multiscale evaluations of global, national and regional digital soil mapping products in France B. Lemercier et al. 10.1016/j.geoderma.2022.116052
- Reducing location error of legacy soil profiles leads to improvement in digital soil mapping G. Shi et al. 10.1016/j.geoderma.2024.116912
- How well does digital soil mapping represent soil geography? An investigation from the USA D. Rossiter et al. 10.5194/soil-8-559-2022
- Locally enhanced digital soil mapping in support of a bottom-up approach is more accurate than conventional soil mapping and top-down digital soil mapping M. Bohn & B. Miller 10.1016/j.geoderma.2024.116781
- High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic D. Žížala et al. 10.1016/j.catena.2022.106024
- Influence of Land Use and Topographic Factors on Soil Organic Carbon Stocks and Their Spatial and Vertical Distribution K. Blackburn et al. 10.3390/rs14122846
Latest update: 23 Nov 2024
Short summary
Maps of soil properties made by machine learning techniques are increasingly applied in Earth surface process modelling and agronomy. Maps of the same area made by different methods appear quite different and also differ from field-based polygon soil survey maps. We explore these differences both visually and numerically, using methods that quantify the spatial patterns. Readers can apply the methods to their areas of interest in the USA with the supplied R Markdown scripts.
Maps of soil properties made by machine learning techniques are increasingly applied in Earth...
