Articles | Volume 7, issue 1
https://doi.org/10.5194/soil-7-217-2021
© Author(s) 2021. 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-7-217-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty
ISRIC – World Soil Information, Wageningen, the Netherlands
Luis M. de Sousa
ISRIC – World Soil Information, Wageningen, the Netherlands
Niels H. Batjes
ISRIC – World Soil Information, Wageningen, the Netherlands
Gerard B. M. Heuvelink
ISRIC – World Soil Information, Wageningen, the Netherlands
Bas Kempen
ISRIC – World Soil Information, Wageningen, the Netherlands
Eloi Ribeiro
ISRIC – World Soil Information, Wageningen, the Netherlands
David Rossiter
ISRIC – World Soil Information, Wageningen, the Netherlands
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.
David G. Rossiter, Laura Poggio, Dylan Beaudette, and Zamir Libohova
SOIL, 8, 559–586, https://doi.org/10.5194/soil-8-559-2022, https://doi.org/10.5194/soil-8-559-2022, 2022
Short summary
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.
Lei Zhang, Lin Yang, Thomas W. Crowther, Constantin M. Zohner, Sebastian Doetterl, Gerard B. M. Heuvelink, Alexandre M. J.-C. Wadoux, A.-Xing Zhu, Yue Pu, Feixue Shen, Haozhi Ma, Yibiao Zou, and Chenghu Zhou
Earth Syst. Sci. Data, 17, 2605–2623, https://doi.org/10.5194/essd-17-2605-2025, https://doi.org/10.5194/essd-17-2605-2025, 2025
Short summary
Short summary
Current understandings of depth-dependent variations and controls of soil organic carbon turnover time (τ) at global, biome, and local scales remain incomplete. We used the state-of-the-art soil and root profile databases and satellite observations to generate new spatially explicit global maps of topsoil and subsoil τ, with quantified uncertainties for better user applications. The new insights from the resulting maps will facilitate efforts to model the carbon cycle and will support effective carbon management.
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.
Niels H. Batjes, Luis Calisto, and Luis M. de Sousa
Earth Syst. Sci. Data, 16, 4735–4765, https://doi.org/10.5194/essd-16-4735-2024, https://doi.org/10.5194/essd-16-4735-2024, 2024
Short summary
Short summary
Soils are an important provider of ecosystem services. This dataset provides quality-assessed and standardised soil data to support digital soil mapping and environmental applications at a broad scale. The underpinning soil profiles were shared by a wide range of data providers. Special attention was paid to the standardisation of soil property definitions, analytical method descriptions and property values. We present three measures to assess "fitness for intended use" of the standardised data.
Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink
Earth Syst. Sci. Data, 16, 2941–2970, https://doi.org/10.5194/essd-16-2941-2024, https://doi.org/10.5194/essd-16-2941-2024, 2024
Short summary
Short summary
Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here we introduce BIS-4D, a statistical modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
Luís Moreira de Sousa, Rául Palma, Bogusz Janiak, and Paul van Genuchten
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 29–34, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-29-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-29-2024, 2024
David G. Rossiter, Laura Poggio, Dylan Beaudette, and Zamir Libohova
SOIL, 8, 559–586, https://doi.org/10.5194/soil-8-559-2022, https://doi.org/10.5194/soil-8-559-2022, 2022
Short summary
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.
Jairo Arturo Torres-Matallana, Ulrich Leopold, and Gerard B. M. Heuvelink
Hydrol. Earth Syst. Sci., 25, 193–216, https://doi.org/10.5194/hess-25-193-2021, https://doi.org/10.5194/hess-25-193-2021, 2021
Short summary
Short summary
This study aimed to select and characterise the main sources of input uncertainty in urban sewer systems, while accounting for temporal correlations of uncertain model inputs, by propagating input uncertainty through the model. We discuss the water quality impact of the model outputs to the environment, specifically in combined sewer systems, in relation to the uncertainty analysis, which constitutes valuable information for the environmental authorities and decision-makers.
Cited articles
Aitchison, J.: The statistical analysis of compositional data, Chapman &
Hall, London, UK, 1986. a
Akpa, S. I. C., Odeh, I. O. A., Bishop, T. F. A., and Hartemink, A. E.:
Digital Mapping of Soil Particle-Size Fractions for Nigeria, Soil Sci.,
78, 1953–1966, https://doi.org/10.2136/sssaj2014.05.0202, 2014. 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., de Lourdes Mendonca-Santos, M., Minasny, B., Montanarella, L., Odeh, I. O., Sanchez, P. A., Thompson, J. A., and Zhang, G.-L.: GlobalSoilMap: Toward a Fine-Resolution Global Grid of Soil Properties, in: Advances in Agronomy, Academic Press, 93–134, https://doi.org/10.1016/B978-0-12-800137-0.00003-0, 2014. a, b, c
Arrouays, D., Leenaars, J. G. B., Richer-de Forges, A. C., Adhikari, K.,
Ballabio, C., Greve, M., Grundy, M., Guerrero, E., Hempel, J., Hengl, T.,
Heuvelink, G., Batjes, N., Carvalho, E., Hartemink, A., Hewitt, A., Hong,
S.-Y., Krasilnikov, P., Lagacherie, P., Lelyk, G., Libohova, Z., Lilly, A.,
McBratney, A., McKenzie, N., Vasquez, G. M., Mulder, V. L., Minasny, B.,
Montanarella, L., Odeh, I., Padarian, J., Poggio, L., Roudier, P., Saby, N.,
Savin, I., Searle, R., Solbovoy, V., Thompson, J., Smith, S., Sulaeman, Y.,
Vintila, R., Rossel, R. V., Wilson, P., Zhang, G.-L., Swerts, M., Oorts, K.,
Karklins, A., Feng, L., Ibelles Navarro, A. R., Levin, A., Laktionova, T.,
Dell'Acqua, M., Suvannang, N., Ruam, W., Prasad, J., Patil, N., Husnjak, S.,
Pásztor, L., Okx, J., Hallet, S., Keay, C., Farewell, T., Lilja, H.,
Juilleret, J., Marx, S., Takata, Y., Kazuyuki, Y., Mansuy, N., Panagos, P.,
Van Liedekerke, M., Skalsky, R., Sobocka, J., Kobza, J., Eftekhari, K.,
Alavipanah, S. K., Moussadek, R., Badraoui, M., Da Silva, M., Paterson, G.,
Gonçalves, M. D. C., Theocharopoulos, S., Yemefack, M., Tedou, S., Vrscaj,
B., Grob, U., Kozák, J., Boruvka, L., Dobos, E., Taboada, M., Moretti, L.,
and Rodriguez, D.: Soil legacy data rescue via GlobalSoilMap and other
international and national initiatives, GeoResJ, 14, 1–19,
https://doi.org/10.1016/j.grj.2017.06.001, 2017. a
Arrouays, D., McBratney, A., Bouma, J., Libohova, Z., de Forges, A. C. R.,
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 Regional, 20, e00255, https://doi.org/10.1016/j.geodrs.2020.e00255, 2020. a
Ballabio, C., Panagos, P., and Monatanarella, L.: Mapping topsoil physical
properties at European scale using the LUCAS database, Geoderma, 261,
110–123, 2016. a
Banwart, S., Black, H., Cai, Z., Gicheru, P., Joosten, H., Victoria, R., Milne, E., Noellemeyer, E., Pascual, U., Nziguheba, G., Vargas, R., Bationo, A., Buschiazzo, D., de Brogniez, D., Melillo, J., Richter, D., Termansen, M., van Noordwijk, M., Goverse, T., Ballabio, C., Bhattacharyya, T., Goldhaber, M., Nikolaidis, N., Zhao, Y., Funk, R., Duffy, C., Pan, G., la Scala, N.,
Gottschalk, P., Batjes, N., Six, J., van Wesemael, B., Stocking, M., Bampa,
F., Bernoux, M., Feller, C., Lemanceau, P., and Montanarella, L.: Benefits of
soil carbon: report on the outcomes of an international scientific committee
on problems of the environment rapid assessment workshop, Carbon Manag.,
5, 185–192, https://doi.org/10.1080/17583004.2014.913380, 2014. a
Barnes, M.: Aichi targets: Protect biodiversity, not just area, Nature, 526,
195–195, https://doi.org/10.1038/526195e, 2015. a
Barnes, R., Sahr, K., Evenden, G., Johnson, A., and Warmerdam, F.: dggridR: Discrete Global Grids for R, R package version 2.0.4, available at: https://github.com/r-barnes/dggridR/ (last access: 21 May 2021), 2016. a
Batjes, N.: ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global
grid (ver. 1.2), Report 2012/01, ISRIC – World Soil Information, available at:
http://www.isric.org/sites/default/files/isric_report_2012_01.pdf (last access: 21 May 2021), 2012. a
Batjes, N.: Harmonised soil property values for broad-scale modelling
(WISE30sec) with estimates of global soil carbon stocks, Geoderma, 269,
61–68, https://doi.org/10.1016/j.geoderma.2016.01.034 2016. a
Batjes, N., Al-Adamat, R., Bhattacharyya, T., Bernoux, M., Cerri, C., Gicheru, P., Kamoni, P., Milne, E., Pal, D., and Rawajfih, Z.: Preparation of
consistent soil data sets for SOC modelling purposes: secondary SOTER data
sets for four case study areas, Agr. Ecosyst. Environ., 122,
26–34, https://doi.org/10.1016/j.agee.2007.01.005, 2007. a
Batjes, N. H.: Technologically achievable soil organic carbon sequestration in world croplands and grasslands, Land Degrad. Dev., 30, 25–32, https://doi.org/10.1002/ldr.3209, 2019. a
Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., Kirches, G., Zulkhe, M., Kalogirou, V., Seifert, F. M., and Arino, O.: Consistent global land cover maps for climate modelling communities: Current
achievements of the ESA's land cover CCI, in: Proceedings of the ESA Living
Planet Symposium, Edinburgh, Scotland, 9–13, 2013. a, b
Borrelli, P., Robinson, D. A., Fleischer, L. R., Lugato, E., Ballabio, C.,
Alewell, C., Meusburger, K., Modugno, S., Schütt, B., Ferro, V., Bagarello,
V., Oost, K. V., Montanarella, L., and Panagos, P.: An assessment of the
global impact of 21st century land use change on soil erosion, Nat. Commun., 8, 2013, https://doi.org/10.1038/s41467-017-02142-7, 2017. a
Bouma, J.: Engaging Soil Science in Transdisciplinary Research Facing
“Wicked” Problems in the Information Society, Soil Sci. Soc. Am. J., 79,
454–458, https://doi.org/10.2136/sssaj2014.11.0470, 2015. a
Brus, D. J.: Statistical sampling approaches for soil monitoring, Eur. J. Soil Sci., 65, 779–791, https://doi.org/10.1111/ejss.12176, 2014. a, b
Brus, D. J., Kempen, B., and Heuvelink, G.: Sampling for validation of digital soil maps, Eur. J. Soil Sci., 62, 394–407,
https://doi.org/10.1111/j.1365-2389.2011.01364.x, 2011. a, b
Buchhorn, M., Lesiv, M., Tsendbazar, N. E., Herold, M., Bertels, L., and Smets, B.: Copernicus Global Land Cover Layers – Collection 2,
Remote Sens.-Basel, 12, 1044, https://doi.org/10.3390/rs12061044, 2020. a
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G.,
Peng, S., Lu, M., Zhang, W., Tong, X, and Mills, J.: Global land cover mapping at 30 m resolution: A
POK-based operational approach, ISPRS J. Photogramm., 103, 7–27, 2015. a
Chen, S., Mulder, V. L., Heuvelink, G. B. M., Poggio, L., Caubet, M.,
Román Dobarco, M., Walter, C., and Arrouays, D.: Model averaging for mapping
topsoil organic carbon in France, Geoderma, 366, 114237,
https://doi.org/10.1016/j.geoderma.2020.114237, 2020. a
Cowie, A. L., Orr, B. J., Castillo Sanchez, V. M., Chasek, P., Crossman, N. D., Erlewein, A., Louwagie, G., Maron, M., Metternicht, G. I., Minelli, S., Tengberg, A. E., Walter, S., and Welton, S.: Land in balance: The scientific conceptual framework for Land Degradation Neutrality, Environ. Sci. Policy, 79, 25–35, https://doi.org/10.1016/j.envsci.2017.10.011, 2018. a
Dai, Y., Shangguan, W., Wei, N., Xin, Q., Yuan, H., Zhang, S., Liu, S., Lu, X., Wang, D., and Yan, F.: A review of the global soil property maps for Earth system models, SOIL, 5, 137–158, https://doi.org/10.5194/soil-5-137-2019, 2019. a, b, c
de Sousa, L. M., Poggio, L., and Kempen, B.: Comparison of FOSS4G Supported
Equal-Area Projections Using Discrete Distortion Indicatrices,
ISPRS Int. J. Geo-Inf., 8, 351, https://doi.org/10.3390/ijgi8080351, 2019. a
de Sousa, L. M., Poggio, L., Dawes, G., Kempen, B., and Van Den Bosch, R.:
Computational Infrastructure of SoilGrids 2.0, in: International Symposium on
Environmental Software Systems, 24–31, 2020. a
Deutsch, C. and Journel, A.: GSLIB: Geostatistical Software Library and
User's Guide, edn. 2, Oxford University Press, New York, USA, 1998. a
Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., Hahn, N., Palminteri, S., Hedao, P., Noss, R., Hansen, M., Locke, H., Ellis, E. C., Jones, B., Barber, C. V., Hayes, R., Kormos, C., Martin, V., Crist, E., Sechrest, W., Price, L., Baillie, J. E. M., Weeden, D., Suckling, K., Davis, C., Sizer, N., Moore, R., Thau, D., Birch, T., Potapov, P., Turubanova, S., Tyukavina, A., de Souza, N., Pintea, L., Brito, J. C., Llewellyn, O. A., Miller, A. G., Patzelt, A., Ghazanfar, S. A., Timberlake, J., Klöser, H., Shennan-Farpón, Y., Kindt, R., Lillesø, J.-P. B., van Breugel, P., Graudal, L., Voge, M., Al-Shammari, K. F., and Saleem, M.: An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm,
BioScience, 67, 534–545, https://doi.org/10.1093/biosci/bix014, 2017. a
Dorji, T., Odeh, I. O. A., Field, D. J., and Baillie, I. C.: Digital soil
mapping of soil organic carbon stocks under different land use and land cover
types in montane ecosystems, Eastern Himalayas,
Forest Ecol. Manag., 318, 91–102, https://doi.org/10.1016/j.foreco.2014.01.003, 2014. a
Ellili, Y., Walter, C., Michot, D., Pichelin, P., and Lemercier, B.: Mapping
soil organic carbon stock change by soil monitoring and digital soil mapping
at the landscape scale, Geoderma, 351, 1–8, https://doi.org/10.1016/j.geoderma.2019.03.005, 2019. a
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table
Depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881, 2013. a
FAO: Digital Soil Map of the World and derived properties (ver. 3.5), Report
FAO Land and Water Digital Media Series 1, Food and Agriculture Organization of the United Nations (FAO), available at:
http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116 (last access: 21 May 2021),
1995. a
FAO: Guidelines for soil description, edn. 4, Report, Food and Agriculture Organization of the United Nations (FAO), available at:
http://www.fao.org/docrep/019/a0541e/a0541e.pdf (last access: 21 May 2021), 2006. a
FAO and ITPS: Status of the world's soil resources (SWSR) – Main report,
Report, Food and Agriculture Organization of the United Nations (FAO) and
Intergovernmental Technical Panel on Soils (ITPS), available at:
http://www.fao.org/3/a-i5199e.pdf (last access: 21 May 2021), 2015. a
FAO, IIASA, ISRIC, ISSCAS, and JRC: Harmonized World Soil Database (version
1.2), Report, edited by: Nachtergaele, F. O., van Velthuizen, H., Verelst,
L., Wiberg, D., Batjes, N. H., Dijkshoorn, J. A., van Engelen, V. W. P.,
Fischer, G., Jones, A., Montanarella, L., Petri, M., Prieler, S.,
Teixeira, E., and Xuezheng, S., Food and Agriculture Organization of the United Nations (FAO), International Institute for Applied Systems Analysis (IIASA), ISRIC – World Soil Information, Institute of Soil Science – Chinese Academy of Sciences (ISSCAS), Joint Research Centre of the European Commission (JRC), available at:
http://webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HWSD_Documentation.pdf (last access: 21 May 2021), 2012. a
FAO, IFAD, UNICEF, WFP, and WHO: The State of Food Security and Nutrition in
the World 2018, Building climate resilience for food security and nutrition,
Report, Food and Agriculture Organization of the United Nations (FAO), available at: http://www.fao.org/3/I9553EN/i9553en.pdf (last access: 21 May 2021), 2018. a
FAO-ISRIC: Guidelines for soil description, edn. 3, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 1986. a
FAO-Unesco: FAO-Unesco Soil Map of the World, 1:5 000 000 (Vol. 1 to 10),
United Nations Educational, Scientific, and Cultural Organization, Paris,
France, 1971–1981. a
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1 km spatial resolution
climate surfaces for global land areas, Int. J. Climatol.,
37, 4302–4315, 2017. a
Fluet-Chouinard, E., Lehner, B., Rebelo, L.-M., Papa, F., and Hamilton, S. K.: Development of a global inundation map at high spatial resolution from
topographic downscaling of coarse-scale remote sensing data,
Remote Sens. Environ., 158, 348–361, 2015. a
Goovaerts, P.: Geostatistics for Natural Resources Evaluation, Oxford
University Press, Oxford, UK, 500 pp., 1997. a
GRASS Development Team: Geographic Resources Analysis Support System (GRASS GIS) Software, version 7.8.0, available at: http://www.grass.osgeo.org (last access: 21 May 2021), 2020. a
Grunwald, S., Thompson, J. A., and Boettinger, J. L.: Digital soil mapping and modeling at continental scales: Finding solutions for global issues,
Soil Sci. Soc. Am. J., 75, 1201–1213, https://doi.org/10.2136/sssaj2011.0025, 2011. a
GSP and FAO: Pillar 4 Implementation Plan – Towards a Global Soil Information System, Report, Global Soil Partnership, available at:
http://www.fao.org/3/a-bl102e.pdf (last access: 21 May 2021), 2016. a
GSP and ITPS: Global soil organic carbon map (GSOCmap), Report Technical
Report, Global Soil Partnership (GSP) and International Panel on Soils
(ITPS), available at: http://www.fao.org/3/i8891en/I8891EN.pdf (last access: 21 May 2021), 2018. a
Guevara, M., Olmedo, G. F., Stell, E., Yigini, Y., Aguilar Duarte, Y., Arellano Hernández, C., Arévalo, G. E., Arroyo-Cruz, C. E., Bolivar, A., Bunning, S., Bustamante Cañas, N., Cruz-Gaistardo, C. O., Davila, F., Dell Acqua, M., Encina, A., Figueredo Tacona, H., Fontes, F., Hernández Herrera, J. A., Ibelles Navarro, A. R., Loayza, V., Manueles, A. M., Mendoza Jara, F., Olivera, C., Osorio Hermosilla, R., Pereira, G., Prieto, P., Ramos, I. A., Rey Brina, J. C., Rivera, R., Rodríguez-Rodríguez, J., Roopnarine, R., Rosales Ibarra, A., Rosales Riveiro, K. A., Schulz, G. A., Spence, A., Vasques, G. M., Vargas, R. R., and Vargas, R.: No silver bullet for digital soil mapping: country-specific soil organic carbon estimates across Latin America, SOIL, 4, 173–193, https://doi.org/10.5194/soil-4-173-2018, 2018. a
Han, E., Ines, A. V. M., and Koo, J.: Development of a 10 km resolution global soil profile dataset for crop modeling applications,
Environ. Modell. Softw., 119, 70–83, https://doi.org/10.1016/j.envsoft.2019.05.012, 2019. a
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S.,
Tyukavina, A., Thau, D., Stehman, S., Goetz, S., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R.,G.:
High-resolution global maps of 21st-century forest cover change, Science,
342, 850–853, 2013. a
Harden, J. W., Hugelius, G., Ahlström, A., Blankinship, J. C., Bond-Lamberty,
B., Lawrence, C. R., Loisel, J., Malhotra, A., Jackson, R. B., Ogle, S.,
Phillips, C., Ryals, R., Todd-Brown, K., Vargas, R., Vergara, S. E., Cotrufo,
M. F., Keiluweit, M., Heckman, K. A., Crow, S. E., Silver, W. L., DeLonge,
M., and Nave, L. E.: Networking our science to characterize the state,
vulnerabilities, and management opportunities of soil organic matter,
Global Change Biol., 24, 705–718, https://doi.org/10.1111/gcb.13896, 2017. a
Hartmann, J. and Moosdorf, N.: The new global lithological map database GLiM: A representation of rock properties at the Earth surface,
Geochem. Geophy. Geosy., 13, https://doi.org/10.1029/2012GC004370, 2012. a
Hengl, T., Mendes de Jesus, J., MacMillan, R. A., Batjes, N. H., Heuvelink,
G. B., Ribeiro, E. C., Samuel-Rosa, A., Kempen, B., Leenaars, J. G., Walsh,
M. G., and Gonzalez, M. R.: SoilGrids1km – global soil information based on automated mapping, PLoS ONE, 9, e114788, https://doi.org/10.1371/journal.pone.0105992, 2014. a, b
Hengl, T., Leenaars, J. G. B., Shepherd, K. D., Walsh, M. G., Heuvelink, G. B. M., Mamo, T., Tilahun, H., Berkhout, E., Cooper, M., Fegraus, E., Wheeler,
I., and Kwabena, N. A.: Soil nutrient maps of Sub-Saharan Africa: assessment
of soil nutrient content at 250 m spatial resolution using machine learning,
Nutr. Cycl. Agroecosys., 109, 77–102, https://doi.org/10.1007/s10705-017-9870-x, 2017a. a
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
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,
2017b. a, b, c, d, e
Heuvelink, G. B. M., Angelini, M. E., Poggio, L., Bai, Z., Batjes, N. H.,
van den Bosch, R., Bossio, D., Estella, S., Lehmann, J., Olmedo, G. F., and
Sanderman, J.: Machine learning in space and time for modelling soil organic
carbon change, Eur. J. Soil Sci., 1–17, https://doi.org/10.1111/ejss.12998, 2020. a
Hounkpatin, O. K., de Hipt, F. O., Bossa, A. Y., Welp, G., and Amelung, W.:
Soil organic carbon stocks and their determining factors in the Dano
catchment (Southwest Burkina Faso), Catena, 166, 298–309, 2018. a
IPBES: Global assessment report on biodiversity and ecosystem services of the
Intergovernmental Science – Policy Platform on Biodiversity and Ecosystem
Services, edited by: Brondizio, E. S., Settele, J., Díaz, S., and Ngo, H. T.,
Report, IPBES Secretariat, Bonn, Germany, available at: https://www.ipbes.net/global-assessment-report-biodiversity-ecosystem-services (last access: 21 May 2021),
2019. a
IUSS Working Group WRB: World Reference Base for Soil Resources 2014, in: Update 2015 – International Soil Classification System for Naming Soils and Creating Legends for Soil Maps, no. 106 in World Soil Resources Reports, Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, available at: http://www.fao.org/3/i3794en/I3794en.pdf (last access: 21 May 2021), 2015. a
Ivushkin, K., Bartholomeus, H., Bregt, A. K., Pulatov, A., Kempen, B., and
de Sousa, L.: Global mapping of soil salinity change, Remote Sens. Environ., 231, 11260, https://doi.org/10.1016/j.rse.2019.111260, 2019. a
Janssen, P. H. M. and Heuberger, P. S. C.: Calibration of process-oriented
models, Ecol. Model., 83, 55–66, 1995. a
Karger, D., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R., Zimmermann, N., Linder, H., and Kessler, M.: CHELSA climatologies at high
resolution for the earth's land surface areas (Version 1.1), World Data
Center for Climate, 2016. a
Kempen, B., Brus, D. J., and de Vries, F.: Operationalizing digital soil
mapping for nationwide updating of the 1:50 000 soil map of the Netherlands, Geoderma, 241–242, 313–329, https://doi.org/10.1016/j.geoderma.2014.11.030, 2015. a
Kempen, B., Dalsgaard, S., Kaaya, A. K., Chamuya, N., Ruipérez-González, M.,
Pekkarinen, A., and Walsh, M. G.: Mapping topsoil organic carbon
concentrations and stocks for Tanzania, Geoderma, 337, 164–180,
https://doi.org/10.1016/j.geoderma.2018.09.011, 2019. a
Keskin, H. and Grunwald, S.: Regression kriging as a workhorse in the digital
soil mapper's toolbox, Geoderma, 326, 22–41,
https://doi.org/10.1016/j.geoderma.2018.04.004, 2018. a, b, c
Khaledian, Y. and Miller, B. A.: Selecting appropriate machine learning methods for digital soil mapping, Appl. Math. Model., 81, 401–418, 2020. a
Kuhn, M.: A Short Introduction to the caret Package, R Found Stat Comput, https://cran.microsoft.com/snapshot/2015-08-17/web/packages/caret/vignettes/caret.pdf (last access: 21 May 2021), 10 pp., 2015. a
Liu, F., Zhang, G.-L., Song, X., Li, D., Zhao, Y., Yang, J., Wu, H., and Yang, F.: High-Resolution and Three-Dimensional Mapping of Soil Texture of
China, Geoderma, 361, 114061, https://doi.org/10.1016/j.geoderma.2019.114061,
2020. a
Luo, Y., Ahlström, A., Allison, S., Batjes, N., Brovkin, V., Carvalhais, N.,
Chappell, A., Ciais, P., Davidson, E., Finzi, A., Georgiou, K., Guenet, B.,
Hararuk, O., Harden, J., He, Y., Hopkins, F. M., Jiang, L., Koven, C.,
Jackson, R., Jones, C., Lara, M., Liang, J., McGuire, A., Parton, W. J.,
Peng, C., Randerson, J., Salazr, A., Sierra, C., Smoth, M., Tian, H.,
Todd-Brown, K., Torn, M., van Groeningen, K., Wang, Y. P., Westm, O., Wei,
Y., Wieder, W., Xia, J., Xia, X., Xu, X., and Zhu, T.: Towards more realistic
projections of soil carbon dynamics by Earth System Models,
Global Biogeochem. Cy., 30, 40–56, https://doi.org/10.1002/2015GB005239, 2016. a, b
Ma, Y., Minasny, B., McBratney, A., Poggio, L., and Fajardo, M.: Predicting
soil properties in 3D: Should depth be a covariate?, Geoderma, 383, 114794,
https://doi.org/10.1016/j.geoderma.2020.114794, 2021. a
Malhotra, A., Todd-Brown, K., Nave, L. E., Batjes, N. H., Holmquist, J. R.,
Hoyt, A. M., Iversen, C. M., Jackson, R. B., Lajtha, K., Lawrence, C.,
Vindušková, O., Wieder, W., Williams, M., Hugelius, G., and Harden, J.: The
landscape of soil carbon data: emerging questions, synergies and databases,
Prog. Phys. Geog., 43, 707–719, https://doi.org/10.1177/0309133319873309, 2019. a
Mallavan, B., Minasny, B., and Mcbratney, A.: Homosoil, a Methodology for Quantitative Extrapolation of Soil Information Across the Globe, in: Digital Soil Mapping, Progress in Soil Science, edited by: Boettinger, J. L., Howell, D. W., Moore, A. C., Hartemink, A. E., and Kienast-Brown, S., Springer, Dordrecht, The Netherlands, 137–150, https://doi.org/10.1007/978-90-481-8863-5_12, 2010. a
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
Meyer, H. and Pebesma, E.: Predicting into unknown space? Estimating the area
of applicability of spatial prediction models, ArXiv, abs/2005.07939, 2020. a
Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., and Nauss, T.: Improving
performance of spatio-temporal machine learning models using forward feature
selection and target-oriented validation, Environ. Modell. Softw., 101, 1–9, https://doi.org/10.1016/j.envsoft.2017.12.001, 2018. 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, b
Mora-Vallejo, A., Claessens, L., Stoorvogel, J., and Heuvelink, G. B. M.: Small scale digital soil mapping in Southeastern Kenya, Catena, 76, 44–53,
https://doi.org/10.1016/j.catena.2008.09.008, 2008. a
Moulatlet, G. M., Zuquim, G., Figueiredo, F. O. G., Lehtonen, S., Emilio, T.,
Ruokolainen, K., and Tuomisto, H.: Using digital soil maps to infer edaphic
affinities of plant species in Amazonia: Problems and prospects,
Ecol. Evol., 7, 8463–8477, https://doi.org/10.1002/ece3.3242, 2017. a
Nauman, T. W. and Duniway, M. C.: Relative prediction intervals reveal larger
uncertainty in 3D approaches to predictive digital soil mapping of soil
properties with legacy data, Geoderma, 347, 170–184,
https://doi.org/10.1016/j.geoderma.2019.03.037, 2019. a
Nijbroek, R., Piikki, K., Söderström, M., Kempen, B., Turner, K., Hengari,
S., and Mutua, J.: Soil Organic Carbon Baselines for Land Degradation
Neutrality: Map Accuracy and Cost Tradeoffs with Respect to Complexity in
Otjozondjupa, Namibia, Sustainability, 10, 1610, https://doi.org/10.3390/su10051610, 2018. a
NRCS National Soil Survey Center: Gridded Soil Survey Geographic
(gSSURGO) Database User Guide, Version 2.2, Tech. Rep., National Soil Survey Center, available at:
http://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=nrcs142p2_051847&ext=pdf (last access: 21 May 2021), 2016. a
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., and Papritz, A.: Evaluation of digital soil mapping approaches with large sets of environmental covariates, SOIL, 4, 1–22, https://doi.org/10.5194/soil-4-1-2018, 2018. a, b, c
Omuto, C., Nachtergaele, F., and Vargas Rojas, R.: State of the Art Report on
Global and Regional Soil Information: Where are we? Where to go?, Report,
Food and Agriculture Organization of the United Nations (FAO), available at: http://www.fao.org/docrep/017/i3161e/i3161e.pdf (last access: 21 May 2021), 2012. a
Oreskes, N.: Evaluation (Not Validation) of Quantitative Models, Environ. Health Persp., 106, 1453–1460, https://doi.org/10.1289/ehp.98106s61453, 1998. a
Pachepsky, Y. and Rawls, W. J. (Eds.): Development of Pedotransfer Functions in Soil Hydrology, in: Developments in Soil Science, Elsevier, 2004. a
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584,
2016. a
Pelletier, J., Broxton, P., Hazemberg, P., Zeng, X., Troch, P., Niu, G.,
Williams, Z., Brunke, M., and Gochis, D.: Global 1 km Gridded Thickness of
Soil, Regolith, and Sedimentary Deposit Layers, https://doi.org/10.3334/ORNLDAAC/1304, 2016. a
Piikki, K., Söderström, M., and Stadig, H.: Local adaptation of a national
digital soil map for use in precision agriculture,
Advances in Animal Biosciences, 8, 430–432, https://doi.org/10.1017/s2040470017000966, 2017. a
Poggio, L. and Gimona, A.: 3D mapping of soil texture in Scotland, Geoderma
Regional, 9, 5–16, https://doi.org/10.1016/j.geodrs.2016.11.003, 2017a. a
Poggio, L. and Gimona, A.: Assimilation of optical and radar remote sensing
data in 3D mapping of soil properties over large areas, Sci. Total Environ., 579, 1094–1110, https://doi.org/10.1016/j.scitotenv.2016.11.078, 2017b. a, b
Poggio, L., Gimona, A., and Brewer, M. J.: Regional scale mapping of soil
properties and their uncertainty with a large number of satellite-derived
covariates, Geoderma, 209–210, 1–14, https://doi.org/10.1016/j.geoderma.2013.05.029, 2013. a
R Core Team: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, available at:
http://www.R-project.org/ (last access: 21 May 2021), 2020. a
Ribeiro, E., Batjes, N., and Van Oostrum, A.: Towards the standardization and harmonization of world soil data, in: Procedures Manual Report 2018, World Soil Information Service (WoSIS), Wageningen, The Netherlands, Report 2018/01, https://doi.org/10.17027/isric-wdcsoils.20180001, 2018. a
Robinson, N., Regetz, J., and Guralnick, R. P.: EarthEnv-DEM90: A
nearly-global, void-free, multi-scale smoothed, 90 m digital elevation model
from fused ASTER and SRTM data, ISPRS J. Photogramm., 87, 57–67, 2014. a
Rockstroem, J., Falkenmark, M., Lannerstad, M., and Karlberg, L.: The planetary water drama: Dual task of feeding humanity and curbing climate change, Geophys. Res. Lett., 39, L15401, https://doi.org/10.1029/2012gl051688, 2012. a
Rossiter, D. G.: Maps and Models Are Never Valid, but They Can Be Evaluated
(with Responses), Pedometron, 41, 19–28, available at:
http://www.pedometrics.org/Pedometron/Pedometron41.pdf (last access: 21 May 2021), 2017. a
Roudier, P., Burge, O. R., Richardson, S. J., McCarthy, J. K., Grealish, G.,
and Ausseil, A.-G.: National Scale 3D Mapping of Soil pH Using a Data
Augmentation Approach, Remote Sens.-Basel, 12, 2872, https://doi.org/10.3390/rs12182872, 2020. a
Sanderman, J., Hengl, T., and Fiske, G. J.: Soil carbon debt of 12 000 years of human land use, P. Natl. Acad. Sci. USA, 114, 9575–9580, https://doi.org/10.1073/pnas.1706103114, 2017. a
Sayre, R., Dangermond, J., Frye, C., Vaughan, R., Aniello, P., Breyer, S.,
Cribbs, D., Hopkins, D., Nauman, R., Derrenbacher, W., Wright, D. J., Brown, C., Convis, C., Smith, J. H., Benson, L., VanSistine, P., Warner, H., Cress, J. J., Danielson, J. J., Hamann, S. L., Cecere, T., Reddy, A. D., Burton, D., Grosse, A., True, D., Metzger, M., Hartmann, J., Moosdorf, N., Durr, H., Paganini, M., Defourny, P., Arino, O., Maynard, S., Anderson, M., and Comer, P.: A new map of
global ecological land units – an ecophysiographic stratification approach,
Association of American Geographers, Washington D.C., USA, 2014. a
Schoeneberger, P., Wysicki, D., Benham, E., and Broderson, W.: Field book for
describing and sampling soils (ver. 3.0), Natural Resources Conservation
Service, National Soil Survey Center, Lincoln, Nebraska, USA, available at:
http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_052523.pdf (last access: 21 May 2021),
2012. a, b
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data set for earth system modeling, J. Adv. Model. Earth Sy., 6, 249–263, https://doi.org/10.1002/2013MS000293, 2014. a
Shrestha, D. L. and Solomatine, D. P.: Machine learning approaches for
estimation of prediction interval for the model output, Neural Networks, 19,
225–235, https://doi.org/10.1016/j.neunet.2006.01.012, 2006. a
Smith, P., Soussana, J.-F., Angers, D., Schipper, L., Chenu, C., Rasse, D.,
Batjes, N. H., van Egmond, F., McNeill, S., Kuhnert, M., Arias-Navaro, C.,
Olesen, J. E., Chirinda, N., Fornara, D., Joosse, P., Wollenberg, L.,
Alvaro-Fuentes, J., and Cobena, A.: How to measure, report and verify soil
carbon change to realise the potential of soil carbon sequestration for
atmospheric greenhouse gas remova, Global Change Biol., 26, 219–241,
https://doi.org/10.1111/gcb.14815, 2019. a
Soussana, J.-F., Lutfalla, S., Ehrhardt, F., Rosenstock, T., Lamanna, C.,
Havlík, P., Richards, M., Wollenberg, E., Chotte, J.-L., Torquebiau, E.,
Ciais, P., Smith, P., and Lal, R.: Matching policy and science: Rationale for
the “4 per 1000 – soils for food security and climate” initiative,
Soil Till. Res., 188, 3–15, https://doi.org/10.1016/j.still.2017.12.002, 2017. a
Springmann, M., Clark, M., Mason-D'Croz, D., Wiebe, K., Bodirsky, B. L.,
Lassaletta, L., de Vries, W., Vermeulen, S. J., Herrero, M., Carlson, K. M.,
Jonell, M., Troell, M., DeClerck, F., Gordon, L. J., Zurayk, R., Scarborough,
P., Rayner, M., Loken, B., Fanzo, J., Godfray, H. C. J., Tilman, D.,
Rockström, J., and Willett, W.: Options for keeping the food system within
environmental limits, Nature, 562, 519–525, https://doi.org/10.1038/s41586-018-0594-0, 2018. a
Stockmann, U., Padarian, J., McBratney, A., Minasny, B., de Brogniez, D.,
Montanarella, L., Hong, S. Y., Rawlins, B. G., and Field, D. J.: Global soil
organic carbon assessment, Global Food Security, 6, 9–16,
https://doi.org/10.1016/j.gfs.2015.07.001, 2015. a
Stoorvogel, J. J., Bakkenes, M., Temme, A. J. A. M., Batjes, N. H., and ten Brink, B.: S-World: a Global Soil Map for Environmental Modelling,
Land Degrad. Dev., 28, 22–33, https://doi.org/10.1002/ldr.2656, 2017. a
Strobl, C., Boulesteix, A.-L., Kneib, T., Augustin, T., and Zeileis, A.:
Conditional variable importance for random forests, BMC Bioinformatics, 9,
307, https://doi.org/10.1186/1471-2105-9-307, 2008. a
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013. a
Tóth, G., Jones, A., and Montanarella, L.: LUCAS Topsoil survey: methodology,
data and results, Report, Land Resource Management Unit – Soil Action,
European Commission Joint Research Centre Institute for Environment and
Sustainability, available at: https://esdac.jrc.ec.europa.eu/ESDB_Archive/eusoils_docs/other/EUR26102EN.pdf (last access: 21 May 2021),
2013. a
UNEP: The benefits of soil carbon – managing soils for multiple, economic,
societal and environmental benefits, United Nations Environmental
Programme, Nairobi, Kenya, 19–33, https://tinyurl.com/2vu4nsf4 (last access: 21 May 2021), 2012.
a
van Bussel, L. G. J., Grassini, P., Van Wart, J., Wolf, J., Claessens, L.,
Yang, H., Boogaard, H., de Groot, H., Saito, K., Cassman, K. G., and van Ittersum, M. K.: From field to atlas: Upscaling of location-specific yield gap estimates, Field Crop. Res., 177, 98–108,
https://doi.org/10.1016/j.fcr.2015.03.005, 2015. a
van der Esch, S., Brink, B. T., Stehfest, E., Bakkenes, M., Sewell, A.,
Bouwman, A., Meijer, J., Westhoek, H., and van den Berg, M.: Exploring future
changes in land use and land condition and the impacts on food, water,
climate change and biodiversity: Scenarios for the UNCCD Global Land Outlook,
Report, UNCCD, available at: https://tinyurl.com/yagvs9vu (last access: 21 May 2021), 2017. a
van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., and Hochman, Z.: Yield gap analysis with local to global relevance – A review, Field Crop. Res., 143, 4–17, https://doi.org/10.1016/j.fcr.2012.09.009, 2013. a
Vitharana, U. W. A., Mishra, U., and Mapa, R. B.: National soil organic carbon estimates can improve global estimates, Geoderma, 337, 55–64,
https://doi.org/10.1016/j.geoderma.2018.09.005, 2019. a, b
Wan, Z.: MODIS land surface temperature products users' guide,
Institute for Computational Earth System Science, University of California,
Santa Barbara, California, USA, available at: https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf, 2006. a
Wilson, A. M. and Jetz, W.: Remotely sensed high-resolution global cloud
dynamics for predicting ecosystem and biodiversity distributions, PLoS
Biol., 14, e1002415, https://doi.org/10.1371/journal.pbio.1002415, 2016. a
WOCAT: Where the land is greener: Case studies and analysis of soil and water
conservation initiatives worldwide, CTA, UNEP, FAO and CDE, Berne, 2007. a
Wright, M. N. and Ziegler, A.: ranger: A Fast Implementation of Random
Forests for High Dimensional Data in C++ and R, J. Stat.
Softw., 77, 1–17, https://doi.org/10.18637/jss.v077.i01, 2017. a, b
Yigini, Y. and Panagos, P.: Assessment of soil organic carbon stocks under
future climate and land cover changes in Europe, Sci. Total Environ., 557–558, 838–850, https://doi.org/10.1016/j.scitotenv.2016.03.085, 2016. a
Yoo, A. B., Jette, M. A., and Grondona, M.: Slurm: Simple linux utility for
resource management, in: Workshop on Job Scheduling Strategies for Parallel
Processing, 44–60, 2003. a
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.
This paper focuses on the production of global maps of soil properties with quantified spatial...