Hand-feel soil texture observations to evaluate the accuracy of digital soil maps for local prediction of soil particle size distribution: A case study in Central France

Digital maps of soil properties are now widely available. End-users now can access several digital soil mapping (DSM) products of soil properties, produced using different models, calibration/training data, and covariates at various spatial scales from global to local. Therefore, there is an urgent need to provide easy-to-understand tools to communicate map uncertainty and help end-users assess the reliability of DSM products for use at local scales. In this study, we used a large amount of hand-feel soil texture (HFST) data to assess the performance of various published DSM products on the prediction of soil particle size distribution in Central France. We tested four DSM products for soil texture prediction developed at various scales (global, continental, national, and regional) by comparing their predictions with approximately 3 200 HFST observations realized on a 1:50 000 soil survey conducted after release of these DSM products. We used both visual comparisons and quantitative indicators to match the DSM predictions and HFST observations. The comparison between the low-cost HFST observations and DSM predictions clearly showed the applicability of various DSM products, with the prediction accuracy increasing from global to regional predictions. This simple evaluation can determine which products can be used at the local scale and if more accurate DSM products are required.

Automated summary

Digital maps of soil properties are now widely accessible and there is a need to provide tools to communicate map uncertainty and assess reliability of digital soil mapping (DSM) products at local scales. This study evaluated the performance of various published DSM products for soil texture prediction in Central France using hand-feel soil texture data. The results showed that the prediction accuracy increased from global to regional predictions, providing a simple evaluation method to determine the suitability of DSM products at local scales and the need for more accurate predictions.