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.

中文翻译：

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SoilGrids 2.0：生成具有量化空间不确定性的全球土壤信息

SoilGrids 使用最先进的机器学习方法以中等空间分辨率（250 m 单元大小）生成全球土壤特性图，以生成必要的模型。它以来自全球约 240 000 个地点的土壤观测和 400 多个描述植被、地形形态、气候、地质和水文的全球环境协变量作为输入。这项工作的目的是制作土壤特性的全球地图，并在 SoilGrids 2.0 版产品中实施了交叉验证、超参数选择和空间明确不确定性的量化，并结合了最先进的实践和适应它们用于使用遗留数据进行全球数字土壤制图。本文介绍了对土壤有机碳含量、总氮、粗碎片、pH（水）、阳离子交换容量、堆积密度和六个标准深度（高达 200 厘米）的质地分数。定量评估显示的指标与之前的全球、大陆和大区域研究一致。定性评估表明，粗尺度模式可以很好地再现。全球尺度的空间不确定性凸显了进行更多土壤观测的必要性，尤其是在高纬度地区。