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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Cited
31 citations as recorded by crossref.
- Drivers of soil organic carbon stocks at village scale in a sub-humid region of Zimbabwe R. Nyawasha et al.
- National baseline high-resolution mapping of soil organic carbon in Moroccan cropland areas A. Bouasria et al.
- Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity F. Kaya et al.
- Representing soil landscapes from digital soil mapping products – helping the map to speak for itself D. Rossiter & L. Poggio
- Synergetic Integration of Multitemporal Remote Sensing Mosaic and Conventional Soil Map for Mapping Organic Carbon Content in Chernozems A. Suleymanov et al.
- Practical guidance for deciding whether to account for soil variability when managing for land health, agricultural production, and climate resilience J. Herrick et al.
- National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France A. Suleymanov et al.
- Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al.
- Building irrigation-tailored soil datasets for land suitability assessment in collective irrigation systems A. Ferreira et al.
- Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping P. Owens et al.
- Digital Mapping of Soil pH and Driving Factor Analysis Based on Environmental Variable Screening H. Huang et al.
- Mapping soil property classes over a large territory with multiple soilscapes by digital extrapolations of legacy detailed soil maps: A case study in Karnataka -South India P. Lagacherie & S. Dharumarajan
- Accounting for soil class variability in Mediterranean Europe using legacy soil maps and the topsoil LUCAS survey G. Belvisi et al.
- 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.
- Digital soil mapping in the Russian Federation: A review A. Suleymanov et al.
- Soil health in Latin America and the Caribbean R. Poppiel et al.
- Distribution and Habitat Characteristics of the Ozark Pocket Gopher, Geomys bursarius ozarkensis M. Reusche et al.
- Using the farmers’ knowledge on soils improves local digital soil mapping products: A case study in South India P. Lagacherie et al.
- Variation in fine-scale water table depth drives abundance of a unique semi-terrestrial crayfish species M. Carlson et al.
- Soil organic carbon stock prediction using multi-spatial resolutions of environmental variables: How well does the prediction match local references? M. Zeraatpisheh et al.
- Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0 A. Ali et al.
- Pruned hierarchical Random Forest framework for digital soil mapping: Evaluation using NEON soil properties C. Xu et al.
- Predicting and Mapping the Soil Organic Carbon Using Random Forest Model in Natural Forest Ecosystem of Central India K. Karthikeyan et al.
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model Z. Libohova et al.
- Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning S. Sahaar & J. Niemann
- Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales T. Tan et al.
- Operational Digital Soil Mapping: Achievements, Challenges and Future Strategies to Go Beyond P. Lagacherie
- Ranking Territorial Units Using Entropy-Based Pedodiversity C. Secu & R. Pîrnău
- Uncovering the effects of Urmia Lake desiccation on soil chemical ripening using advanced mapping techniques F. Shahbazi et al.
- Mapping soil parent materials in a previously glaciated landscape: Potential for a machine learning approach for detailed nationwide mapping Y. Lin et al.
- Uncertainty quantification for digital soil mapping: An overview A. ZHU et al.
31 citations as recorded by crossref.
- Drivers of soil organic carbon stocks at village scale in a sub-humid region of Zimbabwe R. Nyawasha et al.
- National baseline high-resolution mapping of soil organic carbon in Moroccan cropland areas A. Bouasria et al.
- Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity F. Kaya et al.
- Representing soil landscapes from digital soil mapping products – helping the map to speak for itself D. Rossiter & L. Poggio
- Synergetic Integration of Multitemporal Remote Sensing Mosaic and Conventional Soil Map for Mapping Organic Carbon Content in Chernozems A. Suleymanov et al.
- Practical guidance for deciding whether to account for soil variability when managing for land health, agricultural production, and climate resilience J. Herrick et al.
- National-scale digital soil mapping performances are related to covariates and sampling density: Lessons from France A. Suleymanov et al.
- Evaluating the quality of soil legacy data used as input of digital soil mapping models P. Lagacherie et al.
- Building irrigation-tailored soil datasets for land suitability assessment in collective irrigation systems A. Ferreira et al.
- Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping P. Owens et al.
- Digital Mapping of Soil pH and Driving Factor Analysis Based on Environmental Variable Screening H. Huang et al.
- Mapping soil property classes over a large territory with multiple soilscapes by digital extrapolations of legacy detailed soil maps: A case study in Karnataka -South India P. Lagacherie & S. Dharumarajan
- Accounting for soil class variability in Mediterranean Europe using legacy soil maps and the topsoil LUCAS survey G. Belvisi et al.
- 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.
- Digital soil mapping in the Russian Federation: A review A. Suleymanov et al.
- Soil health in Latin America and the Caribbean R. Poppiel et al.
- Distribution and Habitat Characteristics of the Ozark Pocket Gopher, Geomys bursarius ozarkensis M. Reusche et al.
- Using the farmers’ knowledge on soils improves local digital soil mapping products: A case study in South India P. Lagacherie et al.
- Variation in fine-scale water table depth drives abundance of a unique semi-terrestrial crayfish species M. Carlson et al.
- Soil organic carbon stock prediction using multi-spatial resolutions of environmental variables: How well does the prediction match local references? M. Zeraatpisheh et al.
- Reference soil groups map of Ethiopia based on legacy data and machine learning-technique: EthioSoilGrids 1.0 A. Ali et al.
- Pruned hierarchical Random Forest framework for digital soil mapping: Evaluation using NEON soil properties C. Xu et al.
- Predicting and Mapping the Soil Organic Carbon Using Random Forest Model in Natural Forest Ecosystem of Central India K. Karthikeyan et al.
- Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model Z. Libohova et al.
- Estimating Rootzone Soil Moisture by Fusing Multiple Remote Sensing Products with Machine Learning S. Sahaar & J. Niemann
- Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales T. Tan et al.
- Operational Digital Soil Mapping: Achievements, Challenges and Future Strategies to Go Beyond P. Lagacherie
- Ranking Territorial Units Using Entropy-Based Pedodiversity C. Secu & R. Pîrnău
- Uncovering the effects of Urmia Lake desiccation on soil chemical ripening using advanced mapping techniques F. Shahbazi et al.
- Mapping soil parent materials in a previously glaciated landscape: Potential for a machine learning approach for detailed nationwide mapping Y. Lin et al.
- Uncertainty quantification for digital soil mapping: An overview A. ZHU et al.
Saved (final revised paper)
Latest update: 11 May 2026
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...
