Comparing digital soil mapping techniques for organic carbon and clay content : case study in Burundi's central plateaus

Whereas contemporary land use and land management planning require specific and quantitative georeferenced soil information, often only general-purpose and qualitative soil maps are available. With a view to fill this gap for topsoil clay and organic carbon content in the central plateaus of Burundi, we tested for a representative 15 km(2) hilly landscape, six types of SCORPAN models. The SCORPAN models were first applied as standalone trend models and next extended with a component accounting for the spatial autocorrelation of the residuals from the trend. Various sets of predictors, including class variables derived from the available soil map and continuous derivatives from a Digital Elevation Model (DEM) and from Landsat-imagery were incorporated. For clay, the best prediction method was a Residual Kriging (RK) using a Generalized Additive Model (GAM) as trend built with only DEM derivatives and spectral normalized difference vegetation index (NDVI). Furthermore, the classical and simplest RK, i.e. using a Least Squares Linear Regression (LR) trend built with only continuous covariates, outperformed all standalone trend models. For organic carbon, residuals from the trend models were not significantly auto-correlated, making RK meaningless. In this case the best model was a GAM combining lithologic units with DEM derivatives and NDVI. Overall, the contribution of soil map derived predictors to the model performance was rather weak. It was concluded that, for prediction of specific soil characteristics in the study area, a SCORPAN approach is preferred the more as the performance can be boosted by kriging of trend residuals if auto-correlated