Using quantile regression forest to estimate uncertainty of digital soil mapping products

Abstract

Digital Soil Mapping (DSM) products are simplified representations of more complex and partially unknown patterns of soil variations. Therefore, any prediction of a soil property that can be derived from these products has an irreducible uncertainty that needs to be mapped. The objective of this study was to compare the most current DSM method – Regression Kriging (RK) – with a new approach derived from RandomForest – Quantile Regression Forest (QRF) – in regard to their ability of predicting the uncertainties of GlobalSoilMap soil property grids. The comparison was performed for three soil properties, pH, organic carbon and clay content at 5–15cm depth in a 27,236km2 Mediterranean French region with sparse sets of measured soil profiles (1/13.5km2) and for a set of environmental covariates characterizing the relief, climate, geology and land use of the region. Apart from classical performance indicators, comparisons involved accuracy plots and the visual examinations of the uncertainty maps provided by the two methods.The results obtained for the three soil properties showed that QRF provided more accurate and more interpretable predicted patterns of uncertainty than RK did, while having similar performances in predicting soil properties. The use of QRF in operational DSM is therefore recommended, especially when spatial sampling of soil observations are too sparse for applying RK.