Abstract
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. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations, especially in high-latitude regions.
Highlights
Healthy soils provide important ecosystem services at the local, landscape and global level and are important for the functioning of terrestrial ecosystems (Banwart et al, 2014; FAO and ITPS, 2015; UNEP, 2012)
The best available soil data are required to support the Land Degradation Neutrality (LDN) (Cowie et al, 2018) initiative, achieve several of the Sustainable Development Goals and provide input for, for example, Earth system modelling by the IPCC (Dai et al, 2019; Luo et al, 2016; Todd-Brown et al, 2013) and crop modelling (Han et al, 2019; van Bussel et al, 2015; van Ittersum et al, 2013), among many other applications
Such information can in turn help inform international conventions such as the United Nations Framework Convention on Climate Change (UNFCCC), the United Nation Convention to Combat Desertification (UNCCD) and the United Nations Convention on Biological Diversity (UNCBD)
Summary
Healthy soils provide important ecosystem services at the local, landscape and global level and are important for the functioning of terrestrial ecosystems (Banwart et al, 2014; FAO and ITPS, 2015; UNEP, 2012). The best available soil data are required to support the Land Degradation Neutrality (LDN) (Cowie et al, 2018) initiative, achieve several of the Sustainable Development Goals and provide input for, for example, Earth system modelling by the IPCC (Dai et al, 2019; Luo et al, 2016; Todd-Brown et al, 2013) and crop modelling (Han et al, 2019; van Bussel et al, 2015; van Ittersum et al, 2013), among many other applications Such information can in turn help inform international conventions such as the United Nations Framework Convention on Climate Change (UNFCCC), the United Nation Convention to Combat Desertification (UNCCD) and the United Nations Convention on Biological Diversity (UNCBD). The soil geographic data from the DSMW provided the basis for generating a range of de-
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