SoilGrids1km — Global Soil Information Based on Automated Mapping

By Tomislav Hengl, J. D. de Jesus, R. MacMillan, N. Batjes, Gerard B. M. Heuvelink, E. Ribeiro, A. Samuel-Rosa, Bas Kempen, J. Leenaars, M. Walsh and 1 more. PLoS ONE, 2014

1,112 citations recorded by the source corpus.

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.

Subfields: Environmental Engineering, Management, Monitoring, Policy and Law, Soil Science

View Paper: https://www.semanticscholar.org/paper/875a2c812f75b82966738e6b80f120f8ea89c0fe

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Citations in the corpus

Impacts of nitrogen addition on plant species richness and abundance: A global meta‐analysis (2018, methodology): “For the same reason, we extracted esti‐ mates of CEC and soil pH from the 250‐m resolution global SoilGrids data (Hengl et al., 2014, 2017), by averaging values provided for soil depths of 0–5, 5–15 and 15–30 cm. Data on temperature and pre‐ cipitation were derived from the global Climate Research…”
Global effects of soil and climate on leaf photosynthetic traits and rates (2015, methodology): “5 cm layer, ISRIC, 2013) – an automated system that produces soil datasets derived from digital soil mapping (Hengl et al., 2014) – and the Harmonized World Soil Database (0–30 cm layer, FAO et al.”
Statistical Forecasting of Current and Future Circum‐Arctic Ground Temperatures and Active Layer Thickness (2018, methodology): “To account the effect of organic material on ground thermal regime (Harris et al., 2009), we included soil organic carbon (SOC, g · kg 1) content information from global SoilGrids1km data (Hengl et al., 2014) to our analyses.”
Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species (2018, background): “Recent efforts to develop digital soil maps, such as SoilGrids, have increased the availability of high-resolution gridbased soil information (Hengl et al., 2014), which include chemical and physical properties of the soil, as well as occurrence probabilities of soil classes.”
Mapping the global depth to bedrock for land surface modeling (2017, methodology): “We develop this framework within domain of automated soil mapping as part of the SoilGrids system [Hengl et al., 2014], in which spatial predictions can be gradually improved by adding new training data.”
Modeling Soil Processes: Review, Key Challenges, and New Perspectives (2016, methodology): “Another initiative is the Soilgrids by ISRIC (http://soilgrids1km.isric.org/, accessed 27 Apr. 2016), which used the GlobalSoilMap specification except that the spatial resolution is 1 km (Hengl et al., 2014).”
Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions (2015, methodology): “In the first framework, we predicted soil properties at 250 m resolution, that had previously been mapped at 1 km using global soil prediction models [21], e.”