SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty

SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using advanced machine learning methods to generate the necessary models. The qualitative evaluation showed that coarse-scale patterns are well reproduced.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology.
• 2 The aim of this work was the production of global maps of soil properties, with cross-validation, hyper-parameter selection and quantification of spatially explicit uncertainty, as implemented in the SoilGrids version 2.0 product incorporating advanced practices and adapting them for global digital soil mapping with legacy data.
• 3 The paper presents the evaluation of the global predictions produced for soil organic carbon content, total nitrogen, coarse fragments, pH (water), cation exchange capacity, bulk density and texture fractions at six standard depths (up to 200 cm).

Understanding this could lead to better treatments, improved diagnostics, or a deeper grasp of how the human body works — benefiting patient care globally.

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