Gerard B. M. Heuvelink

TL;DR: 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%.

TL;DR: In this paper, a methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression, which can adopt both continuous and categorical soil variables in a semi-automated or automated manner.

TL;DR: SoilGrids1km provides an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available and results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices, lithology, and taxonomic mapping units derived from conventional soil survey.

TL;DR: This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies, and addresses pragmatic issues: implementation of RK in existing software packages, comparison ofRK with alternative interpolation techniques, and practical limitations to using RK.

TL;DR: Results indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors.