How well does Predictive Soil Mapping represent soil geography? An investigation from the USA

Abstract

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.

How well does digital soil mapping represent soil geography? An investigation from the USA

“…Since we only consider one example case here, our conclusions are tentative. Comparing the results here with those in the companion case studies report (Rossiter et al, 2021), we know that these are context de- pendent, and no general conclusions can be drawn. All the metrics provide useful information based on different summaries of the maps, so none is redundant.…”

“…These results will differ in different soil geographic regions, for different soil properties and for different depth intervals, as shown in the companion case studies report (Rossiter et al, 2021).…”

“…This paper introduces the methods and data sources, and includes an illustrative example (one area, one soil property, one depth interval), in the context of the soil geography of the selected region. A companion ISRIC (International Soil Reference and Information Centre) Report (Rossiter et al, 2021) presents four case studies in diverse soil geographic contexts, each with different soil properties and depth intervals. We encourage readers to apply the methods to their own study areas within the USA, and to their soil properties of interest, to evaluate the utility of the several DSM products.…”

Validation of SoilGrids 2.0 in an Arid Region of India using In Situ Measurements

“…This data can be downloaded from https://files.isric.org/soilgrids/latest/ or https://maps.isric.org under the Open Database License (ODbL). Recent technical updates, accuracy/uncertainty issues, and database applicability/use cases are described in [17], [18], [19], [20].…”