SOIL, 7, 217–240, 2021 https://doi.org/10.5194/soil-7-217-2021 © Author(s) 2021. This work is distributed under SOIL the Creative Commons Attribution 4.0 License. SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty Laura Poggio, Luis M. de Sousa, Niels H. Batjes, Gerard B. M. Heuvelink, Bas Kempen, Eloi Ribeiro, and David Rossiter ISRIC – World Soil Information, Wageningen, the Netherlands Correspondence: Laura Poggio (
[email protected]) Received: 14 October 2020 – Discussion started: 9 November 2020 Revised: 9 April 2021 – Accepted: 18 April 2021 – Published: 14 June 2021 Abstract. SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. 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. 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 state-of-the-art practices and adapting them for global digital soil mapping with legacy data. 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). The quantitative evaluation showed metrics in line with previous global, continental and large-region studies.