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SoilGrids250m: Global gridded soil information based on machine learning

Author

Listed:
  • 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-Marschallinger
  • Mario Antonio Guevara
  • Rodrigo Vargas
  • Robert A MacMillan
  • Niels H Batjes
  • Johan G B Leenaars
  • Eloi Ribeiro
  • Ichsani Wheeler
  • Stephan Mantel
  • Bas Kempen

Abstract

This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10–fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0169748
    DOI: 10.1371/journal.pone.0169748
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    References listed on IDEAS

    as
    1. 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.
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    5. Dossou-Yovo, E. R. & Zwart, Sander J. & Kouyate, A. & Ouedraogo, I. & Bakare, O., 2019. "Predictors of drought in inland valley landscapes and enabling factors for rice farmers’ mitigation measures in the Sudan-Sahel Zone," Papers published in Journals (Open Access), International Water Management Institute, pages 11(1):1-17..
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    8. Chesterman, Nathan S. & Entwistle, Julia & Chambers, Matthew C. & Liu, Hsiao-Chin & Agrawal, Arun & Brown, Daniel G., 2019. "The effects of trainings in soil and water conservation on farming practices, livelihoods, and land-use intensity in the Ethiopian highlands," Land Use Policy, Elsevier, vol. 87(C).
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    12. Andrew K. Marondedze & Brigitta Schütt, 2020. "Assessment of Soil Erosion Using the RUSLE Model for the Epworth District of the Harare Metropolitan Province, Zimbabwe," Sustainability, MDPI, Open Access Journal, vol. 12(20), pages 1-24, October.
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    15. Rurinda, Jairos & Zingore, Shamie & Jibrin, Jibrin M. & Balemi, Tesfaye & Masuki, Kenneth & Andersson, Jens A. & Pampolino, Mirasol F. & Mohammed, Ibrahim & Mutegi, James & Kamara, Alpha Y. & Vanlauwe, 2020. "Science-based decision support for formulating crop fertilizer recommendations in sub-Saharan Africa," Agricultural Systems, Elsevier, vol. 180(C).
    16. Wan, Wei & Liu, Zhong & Li, Kejiang & Wang, Guiman & Wu, Hanqing & Wang, Qingyun, 2021. "Drought monitoring of the maize planting areas in Northeast and North China Plain," Agricultural Water Management, Elsevier, vol. 245(C).
    17. Pengyao Qin & Bin Sun & Zengyuan Li & Zhihai Gao & Yifu Li & Ziyu Yan & Ting Gao, 2021. "Estimation of Grassland Carrying Capacity by Applying High Spatiotemporal Remote Sensing Techniques in Zhenglan Banner, Inner Mongolia, China," Sustainability, MDPI, Open Access Journal, vol. 13(6), pages 1-20, March.
    18. Yang, Chenyao & Fraga, Helder & van Ieperen, Wim & Santos, João A., 2020. "Assessing the impacts of recent-past climatic constraints on potential wheat yield and adaptation options under Mediterranean climate in southern Portugal," Agricultural Systems, Elsevier, vol. 182(C).
    19. O. V. Andreeva & G. S. Kust, 2020. "Land Assessment in Russia Based on the Concept of Land Degradation Neutrality," Regional Research of Russia, Springer, vol. 10(4), pages 593-602, October.

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