SoilGrids

This page refers to SoilGrids250m version 2.0 : https://soilgrids.org

What is “SoilGrids”?

SoilGridsTM (hereafter SoilGrids) is a system for global digital soil mapping that makes use of global soil profile information and covariate data to model the spatial distribution of soil properties across the globe. SoilGrids is a collection of soil property maps for the world produced using machine learning at 250m resolution. Predictions are made at six standard depths. SoilGrids uses global models that are calibrated using all available input observations and globally available environmental covariates. This results in globally consistent predictions (no abrupt changes in predicted values at country boundaries, etc). SoilGrids spatial predictions (layers) are produced using a reproducible soil mapping workflow, and can therefore be regularly updated as new soil data or covariates become available, after quality control and data standardisation/harmonisation.

SoilGrids maps are a global soil data product generated at ISRIC — World Soil Information as a result of international collaboration. For more technical and scientific information about SoilGrids contact the development team at soilgrids@isric.org. For more information about ISRIC and collaboration possibilities please contact soilgrids@isric.org and your request will be forwarded to the ISRIC management team.

  • SoilGrids250m = set of global maps of soil properties for six depth intervals at 250 m spatial resolution.
  • SoilGrids = system for global digital soil mapping.
  • SoilGrids.org = portal to web-services providing access to SoilGrids maps.

References

  • Common soil chemical and physical properties:

    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, 2021. DOI

  • Soil water content at different pressure heads:

    Turek, M.E., Poggio, L., Batjes, N. H., Armindo, R. A., de Jong van Lier, Q., de Sousa, L.M., Heuvelink, G. B. M. : Global mapping of volumetric water retention at 100, 330 and 15 000 cm suction using the WoSIS database, International Soil and Water Conservation Research, 11-2, 225-239, 2023. DOI

Survey

We are collecting information on SoilGrids’ user base to improve the products and better understand their uses.

Please help us by filling this survey: https://forms.office.com/r/Hmi1GCG7pn

What if I did not find an answer to my question?

If you have a technical question about SoilGrids that is not answered in this FAQ, please post it to GIS.StackExchange, under the tag soilgrids. ISRIC staff are subscribed to this tag and will be automatically notified of any new question arising. GIS.StackExchange makes it easier for other SoilGrids users to find quality answers to their questions.

GIS.StackExchange is not a message board or a discussion forum, but a platform for technical questions. If you never used the website before, please take the tour describing the basic functionalities. Please, start by searching the website for similar questions to avoid replicates. If you need to submit a new question please follow the website rules.

Acknowledgements

SoilGrids was funded from ISRIC’s core funding, with additional support from the EU-H2020 CIRCASA project.

A wide range of agencies and experts have provided data for the WoSIS/SoilGrids effort; we gratefully thank them for their contributions.

Cited Sources

  1. Batjes N.H, Calisto L and de Sousa LM (2024). Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023). Earth System Science Data, 16(10): 4735–4765. doi: 10.5194/essd-16-4735-2024

  2. Grunwald, S., Thompson, J. A., & Boettinger, J. L. (2011). Digital soil mapping and modeling at continental scales: Finding solutions for global issues. Soil Science Society of America Journal, 75(4), 1201-1213. doi:10.2136/sssaj2011.0025

  3. Hengl T, de Jesus JM, MacMillan RA, Batjes NH, Heuvelink GBM, et al. (2014) SoilGrids1km — Global Soil Information Based on Automated Mapping. PLoS ONE 9(8): e105992. doi:10.1371/journal.pone.0105992

  4. Hengl T, Heuvelink GBM, Kempen B, Leenaars JGB, Walsh MG, Shepherd KD, et al. (2015) Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE 10(6): e0125814. doi:10.1371/journal.pone.0125814

  5. Meinshausen, N. (2006). Quantile regression forests. Journal of Machine Learning Research, 7(Jun), 983-999.

  6. Omuto, C., Nachtergaele, F., and Vargas Rojas, R. (2012). State of the Art Report on Global and Regional Soil Information: Where are we? Where to go? Global Soil Partnership technical report. FAO, Rome.

  7. de Sousa, L.M., Poggio, L., Kempen, B.: Comparison of FOSS4G Supported Equal- Area Projections Using Discrete Distortion Indicatrices. ISPRS International Jour- nal of Geo-Information 8(8), 351 (2019) doi:10.3390/ijgi8080351

  8. Jackson, R.B, Lajtha, K, Crow, S.E., Hugelius, G., Kramer, M.G., Piñeiro, G. (2017). The Ecology of Soil Carbon: Pools, Vulnerabilities, and Biotic and Abiotic Controls. Annual Review of Ecology, Evolution, and Systematics, 48:1, 419-445

  9. Scharlemann, JPW, Tanner, EVJ, Hiederer, R., and Kapos, V. (2014) Global soil carbon: understanding and managing the largest terrestrial carbon pool, Carbon Management, 5:1, 81-91