Spatial variation of c stocks within peatlands is an overall challenge for monitoring global c cycle processes, which is critical for responding to climate change induced by greenhouse gases (GHGs). the objective of this study was to evaluate the ability of high-resolution, minimally invasive sensor data to predict spatial variation of soil organic c (soc) stocks within highly degraded peatland soils in northeast Germany. within the rhin-Havelluch, a paludification mire that has been cultivated and drained for about 300 yr, seven fields were sampled by soil cores up to 2 m in depth, nine points for each field. soil horizons were examined for dry bulk density, soil organic c content (socc), and thickness to calculate soc stocks and to test for relationships with overall peat thickness, elevation, and electrical conductivity (eca). elevation was determined by light detection and ranging (LIDAR) and eca by an eM38dd, both producing maps of high resolution (1 m). soil organic c density (socd) was related to elevation, eca, and peat thickness. Based on these relationships, maps of socd were produced. within field variation of socd was high, which could be modeled by use of the covariate maps. if available, eca maps can improve the prediction of socd based on elevation. Modeling peat thickness based on sensor data needs additional research, but seems to be a valuable covariate in digital soil mapping. © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA. All Rights reserved.
