Spatial modeling of organic carbon in degraded peatland soils of northeast Germany

Koszinski, S., B.A. Miller, W. Hierold, H. Haelbich, and M. Sommer. 2015. Organic carbon stocks in degraded peatland soils of northeast Germany in relation to elevation, electrical conductivity, and peat thickness. Soil Science Society of America Journal 79(5):1496-1508. doi: 10.2136/sssaj2015.01.0019.


Spatial variation of carbon stocks within peatlands is an overall challenge for monitoring global carbon cycle processes, which is critical for responding to climate change induced by greenhouse gases. The objective of this study was to evaluate the ability of high-resolution, minimally invasive sensor data to predict spatial variation of soil organic carbon 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 years, 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 carbon content, and thickness to calculate soil organic carbon stocks and to test for relationships with overall peat thickness, elevation, and electrical conductivity (ECa). Elevation was determined by LiDAR and electrical conductivity  by an EM38DD, both producing maps of high resolution (1 m).

Soil organic carbon density (SOCd) was related to elevation, electrical conductivity, 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.

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