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

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.