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SoilGrids1km--global soil information based on automated mapping.

Tomislav Hengl1, Jorge Mendes de Jesus1, Robert A MacMillan2

  • 1ISRIC - World Soil Information, Wageningen, the Netherlands.

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Summary

SoilGrids1km offers global 3D soil maps at 1 km resolution, improving soil data availability for global models. This system provides consistent, detailed soil property predictions, addressing limitations of previous systems.

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Area of Science:

  • Geosciences
  • Environmental Science
  • Soil Science

Background:

  • Soils are vital non-renewable resources and carbon sinks, necessitating comprehensive global soil information.
  • Existing global soil information systems lack consistency and detailed spatial resolution.
  • There is a growing demand for accurate and accessible global soil data.

Purpose of the Study:

  • To develop and present SoilGrids1km, a global 3D soil information system.
  • To provide spatial predictions of key soil properties at 1 km resolution.
  • To enhance the availability of consistent and detailed global soil data for various applications.

Main Methods:

  • Utilized global spatial prediction models fitted with approximately 110,000 soil profiles.
  • Incorporated around 75 global environmental covariates, including climatic and biomass indices, lithology, and soil survey data.
  • Employed 5-fold cross-validation to assess prediction accuracies.

Main Results:

  • Generated spatial predictions for soil organic carbon, pH, texture, bulk density, cation-exchange capacity, and other properties at six standard depths.
  • Identified climatic and biomass indices, lithology, and taxonomic mapping units as key predictive covariates.
  • Achieved prediction accuracies ranging from 23-51% for various soil properties.

Conclusions:

  • SoilGrids1km offers unprecedented resolution and consistency in global soil data for modeling purposes.
  • Limitations include scale mismatches, challenges in covariate selection, and data sampling density.
  • The automated and flexible SoilGrids system allows for continuous improvement with new data, with data available under a CC-NC license.