Global spatial predictions of soil properties and classes at 250 m resolution

Citation:

• Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, et al. (2017) SoilGrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2): e0169748. doi:10.1371/journal.pone.0169748

What can you find on this github repository:

• R scripts documenting processing steps,
• Sample code explaining the modelling framework,
• Functions for Cross-validation of ensemble models with examples,
• Examples of predictions, outputs and visualizations,
• Technical specifications of the system,