Publication date: 1 April 2017
Source:Geoderma, Volume 291
Author(s): Kévin Vaysse, Philippe Lagacherie
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 5–15 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.
Source:Geoderma, Volume 291
Author(s): Kévin Vaysse, Philippe Lagacherie