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.
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
Related authors
David G. Rossiter and Laura Poggio
EGUsphere, https://doi.org/10.5194/egusphere-2025-1896, https://doi.org/10.5194/egusphere-2025-1896, 2025
Short summary
Short summary
Soil maps are useful for many applications, e.g., hydrology, agriculture, ecology, and civil engineering. The dominant mapping method is Digital Soil Mapping (DSM), which uses training observations and machine-learning to predict per-pixel. Accuracy is assessed by statistical evaluation at known points, but soils occur in spatial patterns. We present methods for letting the map "speak for itself" to reveal the pattern of the soil landscape, which can be evaluated by expert judgement.
Laura Poggio, Luis M. de Sousa, Niels H. Batjes, Gerard B. M. Heuvelink, Bas Kempen, Eloi Ribeiro, and David Rossiter
SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, https://doi.org/10.5194/soil-7-217-2021, 2021
Short summary
Short summary
This paper focuses on the production of global maps of soil properties with quantified spatial uncertainty, as implemented in the SoilGrids version 2.0 product using DSM practices and adapting them for global digital soil mapping with legacy data. The quantitative evaluation showed metrics in line with previous studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations.
David G. Rossiter and Laura Poggio
EGUsphere, https://doi.org/10.5194/egusphere-2025-1896, https://doi.org/10.5194/egusphere-2025-1896, 2025
Short summary
Short summary
Soil maps are useful for many applications, e.g., hydrology, agriculture, ecology, and civil engineering. The dominant mapping method is Digital Soil Mapping (DSM), which uses training observations and machine-learning to predict per-pixel. Accuracy is assessed by statistical evaluation at known points, but soils occur in spatial patterns. We present methods for letting the map "speak for itself" to reveal the pattern of the soil landscape, which can be evaluated by expert judgement.
Laura Poggio, Luis M. de Sousa, Niels H. Batjes, Gerard B. M. Heuvelink, Bas Kempen, Eloi Ribeiro, and David Rossiter
SOIL, 7, 217–240, https://doi.org/10.5194/soil-7-217-2021, https://doi.org/10.5194/soil-7-217-2021, 2021
Short summary
Short summary
This paper focuses on the production of global maps of soil properties with quantified spatial uncertainty, as implemented in the SoilGrids version 2.0 product using DSM practices and adapting them for global digital soil mapping with legacy data. The quantitative evaluation showed metrics in line with previous studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations.
Cited articles
Araujo-Carrillo, G. A., Varón-Ramírez, V. M., Jaramillo-Barrios, C. I.,
Estupiñan-Casallas, J. M., Silva-Arero, E. A., Gómez-Latorre, D. A.,
and Martínez-Maldonado, F. E.: IRAKA: The First Colombian Soil
Information System with Digital Soil Mapping Products, CATENA, 196, 104940,
https://doi.org/10.1016/j.catena.2020.104940, 2021. a
Arrouays, D., Grundy, M. G., Hartemink, A. E., Hempel, J. W., Heuvelink, G. B.,
Hong, S. Y., Lagacherie, P., Lelyk, G., McBratney, A. B., McKenzie, N. J.,
d. L. Mendonca-Santos, M., Minasny, B., Montanarella, L., Odeh, I. O.,
Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: GlobalSoilMap: Towards
a Fine-Resolution Global Grid of Soil Properties, Adv. Agron., 125,
93–134, 2014. a, b, c
Arrouays, D., McBratney, A., Bouma, J., Libohova, Z., Richer-de-Forges,
A. C., Morgan, C. L., Roudier, P., Poggio, L., and Mulder, V. L.: Impressions
of Digital Soil Maps: The Good, the Not so Good, and Making Them Ever
Better, Geoderma Reg., 20, e00255, https://doi.org/10.1016/j.geodrs.2020.e00255,
2020. a, b, c
Batjes, N. H., Ribeiro, E., and van Oostrum, A.: Standardised Soil Profile
Data to Support Global Mapping and Modelling (WoSIS Snapshot 2019), Earth
Syst. Sci. Data, 12, 299–320, https://doi.org/10.5194/essd-12-299-2020, 2020. a
Beaudette, D.: ncss-tech/compare-psm: PSM Comparison Code v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.5512626, 2021. a
Bie, S. W. and Beckett, P. H. T.: Comparison of Four Independent Soil Surveys
by Air-Photo Interpretation, Paphos Area (Cyprus), Photogrammetria,
29, 189–202, 1973. a
Bloom, A. L.: Gorges History: Landscapes and Geology of the Finger Lakes
Region, Paleontological Research Institution, Ithaca, New York, ISBN 978-0-87710-524-4, 2018. a
Brus, D., Kempen, B., and Heuvelink, G.: Sampling for Validation of Digital
Soil Maps, Europ. J. Soil Sci., 62, 394–407,
https://doi.org/10.1111/j.1365-2389.2011.01364.x, 2011. a
Chaney, N., Minasny, B., Herman, J., Nauman, T., Brungard, C., Morgan, C.,
McBratney, A., Wood, E., and Yimam, Y.: POLARIS Soil Properties: 30-m
Probabilistic Maps of Soil Properties over the Contiguous United States,
Water Resour. Res., 55, 2916–2938, https://doi.org/10.1029/2018WR022797, 2019. a, b
Cornell University Geospatial Information Repository (CUGIR): Soil
Survey, Tompkins County NY, 1965 (FGDC Metadata),
https://cugir-data.s3.amazonaws.com/00/74/98/fgdc.html, last access: 18 August 2022. a
D'Avelo, T. P. and McLeese, R. L.: Why Are Those Lines Placed Where They Are?:
An Investigation of Soil Map Recompilation Methods, Soil Survey Horizons,
39, 119–126, https://doi.org/10.2136/sh1998.4.0119, 1998. a, b
Fridland, V. M.: Structure of the Soil Mantle, Geoderma, 12, 35–42,
https://doi.org/10.1016/0016-7061(74)90036-6, 1974. a
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G.
B. M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh,
M. G., and Gonzalez, M. R.: SoilGrids1km – Global Soil Information
Based on Automated Mapping, PLOS ONE, 9, e105992,
https://doi.org/10.1371/journal.pone.0105992, 2014. a
Hengl, T., de Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda,
M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global Gridded Soil Information Based on
Machine Learning, PLOS ONE, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017. a, b
Hesselbarth, M. H.: R-Spatialecology/Landscapemetrics, r-spatialecology,
https://github.com/r-spatialecology/landscapemetrics (last access: 18 August 2022), 2021. a
Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K., and Nowosad, J.:
Landscapemetrics: An Open-Source R Tool to Calculate Landscape Metrics,
Ecography, 42, 1648–1657, https://doi.org/10.1111/ecog.04617, 2019. a
Hole, F. and Campbell, J.: Soil Landscape Analysis, Rowman & Allanheld,
Totowa, NJ, ISBN 978-0-7102-0492-9, 1985. a
Hudson, B. D.: The Soil Survey as Paradigm-Based Science, Soil Sci. Soc. Am. J., 56, 836–841,
https://doi.org/10.2136/sssaj1992.03615995005600030027x, 1992. a, b, c
ISRIC – World Soil Information: SoilGrids – Global Gridded Soil
Information, https://www.isric.org/explore/soilgrids (last access: 18 August 2022), 2020. a
Kupfer, J. A.: Landscape Ecology and Biogeography: Rethinking Landscape
Metrics in a Post-FRAGSTATS Landscape, Prog. Phys.
Geogr.-Earth Environ., 36, 400–420,
https://doi.org/10.1177/0309133312439594, 2012. a
Lagacherie, P., Andrieux, P., and Bouzigues, R.: Fuzziness and Uncertainty of
Soil Boundaries: From Reality to Coding in GIS, in: Geographic Objects
with Indeterminate Boundaries, edited by: Burrough, P. A., Frank, A. U., and
Salgé, F., GISDATA 2, 275–286, Taylor & Francis, London, ISBN 978-0-7484-0387-5, 1996. a
Libohova, Z., Wills, S., and Odgers, N. P.: Legacy data quality and uncertainty
estimation for United States GlobalSoilMap products, in:
GlobalSoilMap: Basis of the Global Spatial Soil Information
System, edited by: Arrouays, D., McKenzie, N., Hempel, J., DeForges, A.
C. R., and McBratney, A., 63–68, Crc Press-Taylor & Francis Group, Boca
Raton,
2014. a
Liu, F., Rossiter, D. G., Zhang, G.-L., and Li, D.-C.: A Soil Colour Map of
China, Geoderma, 379, 114556, https://doi.org/10.1016/j.geoderma.2020.114556,
2020. a
Mallavan, B., Minasny, B., and McBratney, A.: Homosoil, a Methodology for
Quantitative Extrapolation of Soil Information Across the Globe, in: Digital
Soil Mapping, edited by: Boettinger, J. L., Howell, D. W., Moore, A. C.,
Hartemink, A. E., and Kienast-Brown, S., 137–150, Springer
Netherlands, Dordrecht, ISBN 978-90-481-8862-8, 2010. a, b
McBratney, A. B., Mendonça Santos, M. L., and Minasny, B.:
On Digital Soil Mapping, Geoderma, 117, 3–52,
https://doi.org/10.1016/S0016-7061(03)00223-4, 2003. a
McGarigal, K., Cushman, S. A., and Ene, E.: FRAGSTATS v4: Spatial Pattern
Analysis Program for Categorical and Continuous Maps, Tech. Rep., University
of Massachusetts, Amherst, MA,
2012. a
Meinshausen, N.: Quantile Regression Forests, J. Mach. Learn.
Res., 7, 983–999, 2006. a
Meyer, H. and Pebesma, E.: Machine Learning-Based Global Maps of Ecological
Variables and the Challenge of Assessing Them, Nat. Commun., 13,
2208, https://doi.org/10.1038/s41467-022-29838-9, 2022. a
Minasny, B. and McBratney, A. B.: Digital Soil Mapping: A Brief History and
Some Lessons, Geoderma, 264, 301–311,
https://doi.org/10.1016/j.geoderma.2015.07.017, 2016. a
Moreira de Sousa, L., Poggio, L., and Kempen, B.: Comparison of FOSS4G
Supported Equal-Area Projections Using Discrete Distortion Indicatrices,
ISPRS Int. Geo-Inf., 8, 351,
https://doi.org/10.3390/ijgi8080351, 2019. a
Natural Resources Conservation Service: Web Soil Survey,
https://websoilsurvey.nrcs.usda.gov/ (last access: 18 August 2022), 2019. a
Natural Resources Conservation Service: National Soil Information System
(NASIS),
https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/tools/?cid=nrcs142p2_053552, last access: 18 August 2022. a
Nowosad, J.: sabre: Spatial Association Between Regionalizations,
https://nowosad.github.io/sabre/ (last access: 18 August 2022), 2020. a
Nowosad, J.: Motif: An Open-Source R Tool for Pattern-Based Spatial
Analysis, Landscape Ecol., 36, 29–43, https://doi.org/10.1007/s10980-020-01135-0,
2021. a
Nowosad, J. and Stepinski, T. F.: Spatial Association between Regionalizations
Using the Information-Theoretical V-Measure, Int. J.
Geogr. Inf. Sci., 32, 2386–2401,
https://doi.org/10.1080/13658816.2018.1511794, 2018. a
NRCS Soils: Soils, https://nrcs.app.box.com/v/soils (last access: 18 August 2022),
2020a. a
NRCS Soils: Description of Gridded Soil Survey Geographic (gSSURGO)
Database,
https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/home/?cid=nrcs142p2_053628 (last access: 18 August 2022),
2022a. a
NRCS Soils: Gridded National Soil Survey Geographic Database
(gNATSGO),
https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625 (last access: 18 August 2022),
2022b. a
Odgers, N. P., McBratney, A. B., Minasny, B., Sun, W., and Clifford, D.:
DSMART: An Algorithm to Spatially Disaggregate Soil Map Units, in:
GlobalSoilMap: Basis of the Global Spatial Soil Information System,
edited by: Arrouays, D., McKenzie, N., Hempel, J., DeForges, A. C. R., and
McBratney, A., 261–266, CRC Press-Taylor & Francis Group, Boca
Raton, CRC Press, ISBN 978-1-138-00119-0, 2014. a, b
Pebesma, E. J.: Multivariable Geostatistics in S: The Gstat Package,
Comput. Geosci., 30, 683–691, https://doi.org/10.1016/j.cageo.2004.03.012,
2004. a
Pindral, S., Kot, R., Hulisz, P., and Charzyński, P.: Landscape Metrics as a
Tool for Analysis of Urban Pedodiversity, Land Degrad. Dev.,
31, 2281–2294, https://doi.org/10.1002/ldr.3601, 2020. a
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B.,
Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: Producing Soil Information
for the Globe with Quantified Spatial Uncertainty, SOIL, 7, 217–240,
https://doi.org/10.5194/soil-7-217-2021, 2021. a, b, c
R Studio: R Markdown, https://rmarkdown.rstudio.com/ (last access: 18 August 2022),
2020. a
Ramcharan, A., Hengl, T., Nauman, T., Brungard, C., Waltman, S., Wills, S., and
Thompson, J.: Soil Property and Class Maps of the Conterminous United
States at 100-Meter Spatial Resolution, Soil Sci. Soc. Am.
J., 82, 186–201, https://doi.org/10.2136/sssaj2017.04.0122, 2018. a, b
Reddy, N. N., Chakraborty, P., Roy, S., Singh, K., Minasny, B., McBratney,
A. B., Biswas, A., and Das, B. S.: Legacy Data-Based National-Scale Digital
Mapping of Key Soil Properties in India, Geoderma, 381, 114684,
https://doi.org/10.1016/j.geoderma.2020.114684, 2021. a
Rossiter, D. G., Poggio, L., Beaudette, D., and Libohova, Z.: How Well Does
Predictive Soil Mapping Represent Soil Geography? An Investigation
from the USA, Case Studies, ISRIC Report 2016-004, ISRIC-World
Soil Information, ISRIC-World Soil Information, ISRIC-World Soil Information,
https://doi.org/10.17027/isric-wdcsoils.20160004, 2021. a, b, c
Schoeneberger, P. J., Wysocki, D. A., Benham, E. C., and Soil Survey Staff:
Field Book for Describing and Sampling Soils, USDA Natural Resources
Conservation Service, Lincoln, NE, 3.0 Edn.,
https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/research/guide/?cid=nrcs142p2_054184 (last access: 22 August 2022), 2012. a, b
Science Committee: Specifications: Tiered GlobalSoilMap.Net Products;
Release 2.3, Tech. Rep., GlobalSoilMap.net,
http://www.ozdsm.com.au/resources/GlobalSoilMap%20specs%20version%202point3.pdf (last access: 18 August 2022),
2012. a
Scull, P., Franklin, J., Chadwick, O., and McArthur, D.: Predictive Soil
Mapping: A Review, Prog. Phys. Geogr., 27, 171–197,
https://doi.org/10.1191/0309133303pp366ra, 2003. a
Soil Survey Division Staff: Keys to Soil Taxonomy, US Government
Printing Office, Washington, DC, 12th Edn.,
https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/class/ (last access: 18 August 2022),
2014. a
Szatmári, G. and Pásztor, L.: Comparison of Various Uncertainty Modelling
Approaches Based on Geostatistics and Machine Learning Algorithms, Geoderma, 337, 1329–1340,
https://doi.org/10.1016/j.geoderma.2018.09.008, 2018.
a
Taghizadeh-Mehrjardi, R., Mahdianpari, M., Mohammadimanesh, F., Behrens, T.,
Toomanian, N., Scholten, T., and Schmidt, K.: Multi-Task Convolutional Neural
Networks Outperformed Random Forest for Mapping Soil Particle Size Fractions
in Central Iran, Geoderma, 376, 114552,
https://doi.org/10.1016/j.geoderma.2020.114552, 2020. a
Thompson, J. A., Kienast-Brown, S., D'Avello, T., Philippe, J., and Brungard,
C.: Soils2026 and Digital Soil Mapping – A Foundation for the Future of
Soils Information in the United States, Geoderma Reg., 22, e00294,
https://doi.org/10.1016/j.geodrs.2020.e00294, 2020. a
United States Department of Agriculture, Natural Resources Conservation
Service: National Soil Survey Handbook, United States Department of
Agriculture, Natural Resources Conservation Service, Washington, DC,
https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/home/?cid=nrcs142p2_054242, last access: 22 August
2022. a
Uuemaa, E., Mander, U., and Marja, R.: Trends in the Use of Landscape Spatial
Metrics as Landscape Indicators: A Review, Ecol. Indic., 28,
100–106, https://doi.org/10.1016/j.ecolind.2012.07.018, 2013. a
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...