Abstract. We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by Predictive Soil Mapping (PSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales, and compare them to spatial patterns known from detailed field surveys (gSSURGO). The methods are illustrated with an example: topsoil pH for an area in central New York State. A companion report examines other areas, soil properties, and depth slices. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case we discover and discuss substan- tial discrepancies between PSM products, as well as large differences between the PSM products and legacy field surveys. These differences are in whole-map statistics, visually-identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, co-occurence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine-learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not better than the global model, and in some cases introduced artefacts from a lithology covariate. Uncertainty (5–95% confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the PSM products as a (partial) replacement for field-based soil surveys.

中文翻译：

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预测土壤制图在多大程度上代表了土壤地理？来自美国的调查

摘要。我们提出了评估美国土壤特性地理分布空间模式的方法，如全球（SoilGrids v2）、国家（土壤特性和 100 m 级网格）在全球（SoilGrids v2）生成的网格图所示。美国）和区域（POLARIS 土壤特性）尺度，并将它们与详细实地调查 (gSSURGO) 中已知的空间模式进行比较。这些方法通过一个例子来说明：纽约州中部一个地区的表土 pH 值。一份配套报告检查了其他区域、土壤特性和深度切片。引用了一组 R Markdown 脚本，以便读者可以将分析应用于他们感兴趣的领域。对于测试案例，我们发现并讨论了 PSM 产品之间的重大差异，以及 PSM 产品与传统现场调查之间的巨大差异。这些差异在于全地图统计、视觉上可识别的景观特征、细节水平、空间自相关的范围和强度、景观指标（香农多样性和均匀度、形状、聚合、平均分形维数、共现向量）和空间通过直方图均衡化分类的属性图模式。直方图和变异函数分析揭示了机器学习模型的平滑效果。通过直方图均衡化制作的属性类图有很大不同，但它们的景观指标没有一致的趋势。仅使用国家点和协变量的模型并不比全球模型好，并且在某些情况下引入了来自岩性协变量的人工制品。与 gSSURGO 低估计值和高估计值相比，SoilGrids 和 POLARIS 提供的不确定性（5-95% 置信区间）不切实际，并且显示出明显不同的空间模式。我们讨论了 PSM 产品作为（部分）替代实地土壤调查的潜在用途。