Abstract. SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 metres 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 quality-assessed global maps of soil properties, with cross-validation, hyper-parameters 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 regions 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：为全球提供经过质量评估的土壤信息

摘要。SoilGrids使用最先进的机器学习方法来生成必要的模型，以中等空间分辨率（单元大小为250米）生成整个地球的土壤特性图。它以来自全球约24万个地点的土壤观测数据为输入，并以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质量评估的全球土壤特性图，并进行交叉验证，超参数选择以及对空间上明确的不确定性进行量化，这是在结合了最先进实践和适应性的SoilGrids 2.0版产品中实现的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的全球预测的评估，总氮，粗碎屑，pH（水），阳离子交换容量，堆积密度和六个标准深度（最大200厘米）的织构分数。定量评估显示的指标与先前的全球，大陆和大区域研究一致。定性评估表明，粗纹图案被很好地再现。全球范围内的空间不确定性凸显了对更多土壤观测的需求，特别是在高纬度地区。