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SoilGrids1km — Global Soil Information Based on Automated Mapping

Author

Listed:
  • Tomislav Hengl
  • Jorge Mendes de Jesus
  • Robert A MacMillan
  • Niels H Batjes
  • Gerard B M Heuvelink
  • Eloi Ribeiro
  • Alessandro Samuel-Rosa
  • Bas Kempen
  • Johan G B Leenaars
  • Markus G Walsh
  • Maria Ruiperez Gonzalez

Abstract

Background: Soils are widely recognized as a non-renewable natural resource and as biophysical carbon sinks. As such, there is a growing requirement for global soil information. Although several global soil information systems already exist, these tend to suffer from inconsistencies and limited spatial detail. Methodology/Principal Findings: We present SoilGrids1km — a global 3D soil information system at 1 km resolution — containing spatial predictions for a selection of soil properties (at six standard depths): soil organic carbon (g kg−1), soil pH, sand, silt and clay fractions (%), bulk density (kg m−3), cation-exchange capacity (cmol+/kg), coarse fragments (%), soil organic carbon stock (t ha−1), depth to bedrock (cm), World Reference Base soil groups, and USDA Soil Taxonomy suborders. Our predictions are based on global spatial prediction models which we fitted, per soil variable, using a compilation of major international soil profile databases (ca. 110,000 soil profiles), and a selection of ca. 75 global environmental covariates representing soil forming factors. Results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices (based on MODIS images), lithology, and taxonomic mapping units derived from conventional soil survey (Harmonized World Soil Database). Prediction accuracies assessed using 5–fold cross-validation were between 23–51%. Conclusions/Significance: SoilGrids1km provide an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available. Some of the main limitations of the current version of SoilGrids1km are: (1) weak relationships between soil properties/classes and explanatory variables due to scale mismatches, (2) difficulty to obtain covariates that capture soil forming factors, (3) low sampling density and spatial clustering of soil profile locations. However, as the SoilGrids system is highly automated and flexible, increasingly accurate predictions can be generated as new input data become available. SoilGrids1km are available for download via http://soilgrids.org under a Creative Commons Non Commercial license.

Suggested Citation

  • Tomislav Hengl & Jorge Mendes de Jesus & Robert A MacMillan & Niels H Batjes & Gerard B M Heuvelink & Eloi Ribeiro & Alessandro Samuel-Rosa & Bas Kempen & Johan G B Leenaars & Markus G Walsh & Maria R, 2014. "SoilGrids1km — Global Soil Information Based on Automated Mapping," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-17, August.
    • Handle: RePEc:plo:pone00:0105992
    DOI: 10.1371/journal.pone.0105992
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    References listed on IDEAS

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    1. Jörn P W Scharlemann & David Benz & Simon I Hay & Bethan V Purse & Andrew J Tatem & G R William Wint & David J Rogers, 2008. "Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data," PLOS ONE, Public Library of Science, vol. 3(1), pages 1-13, January.
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    1. Huang, Yawen & Tao, Bo & Lal, Rattan & Lorenz, Klaus & Jacinthe, Pierre-Andre & Shrestha, Raj K. & Bai, Xiongxiong & Singh, Maninder P. & Lindsey, Laura E. & Ren, Wei, 2023. "A global synthesis of biochar's sustainability in climate-smart agriculture - Evidence from field and laboratory experiments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    2. Sartori, Martina & Philippidis, George & Ferrari, Emanuele & Borrelli, Pasquale & Lugato, Emanuele & Montanarella, Luca & Panagos, Panos, 2019. "A linkage between the biophysical and the economic: Assessing the global market impacts of soil erosion," Land Use Policy, Elsevier, vol. 86(C), pages 299-312.
    3. Ting-Shuai Shi & Scott L. Collins & Kailiang Yu & Josep Peñuelas & Jordi Sardans & Hailing Li & Jian-Sheng Ye, 2024. "A global meta-analysis on the effects of organic and inorganic fertilization on grasslands and croplands," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    4. Vít Penížek & Tereza Zádorová & Radka Kodešová & Aleš Vaněk, 2016. "Influence of Elevation Data Resolution on Spatial Prediction of Colluvial Soils in a Luvisol Region," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
    5. Katerina Georgiou & Robert B. Jackson & Olga Vindušková & Rose Z. Abramoff & Anders Ahlström & Wenting Feng & Jennifer W. Harden & Adam F. A. Pellegrini & H. Wayne Polley & Jennifer L. Soong & William, 2022. "Global stocks and capacity of mineral-associated soil organic carbon," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    6. Flach, Rafaela & Skalský, Rastislav & Folberth, Christian & Balkovič, Juraj & Jantke, Kerstin & Schneider, Uwe A., 2020. "Water productivity and footprint of major Brazilian rainfed crops – A spatially explicit analysis of crop management scenarios," Agricultural Water Management, Elsevier, vol. 233(C).
    7. Patrick José Jeetze & Isabelle Weindl & Justin Andrew Johnson & Pasquale Borrelli & Panos Panagos & Edna J. Molina Bacca & Kristine Karstens & Florian Humpenöder & Jan Philipp Dietrich & Sara Minoli &, 2023. "Projected landscape-scale repercussions of global action for climate and biodiversity protection," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    8. Vieira Junior, Nilson & Carcedo, Ana Julia Paula & Min, Doohong & Diatta, Andre Amakobo & Araya, Alemie & Prasad, P.V. Vara & Diallo, Amadiane & Ciampitti, Ignacio, 2023. "Management adaptations for water-limited pearl millet systems in Senegal," Agricultural Water Management, Elsevier, vol. 278(C).
    9. Tomislav Hengl & Jorge Mendes de Jesus & Gerard B M Heuvelink & Maria Ruiperez Gonzalez & Milan Kilibarda & Aleksandar Blagotić & Wei Shangguan & Marvin N Wright & Xiaoyuan Geng & Bernhard Bauer-Marsc, 2017. "SoilGrids250m: Global gridded soil information based on machine learning," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-40, February.
    10. Katharina Schulze & Žiga Malek & Dmitry Schepaschenko & Myroslava Lesiv & Steffen Fritz & Peter H. Verburg, 2023. "Pantropical distribution of short-rotation woody plantations: spatial probabilities under current and future climate," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 28(5), pages 1-22, June.

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