Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at the field scale for food security and monitoring long-term changes related to soil health and climate change. In Germany, SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and (here: Landsat-based) multi-temporal soil reflectance composites (SRC) as explanatory variables. In the example of a Bavarian test of about 8000 km2, relations between 220 SOC content samples as well as different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, and the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters leads to a significant model improvement at fieldrelated scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2 = 0.84 and RMSE = 1.99.