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Spatial Modeling of Organic Carbon in Degraded Peatland Soils of Northeast Germany
Author(s) -
Koszinski Sylvia,
Miller Bradley A.,
Hierold Wilfried,
Haelbich Henny,
Sommer Michael
Publication year - 2015
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj2015.01.0019
Subject(s) - peat , environmental science , soil carbon , soil water , soil science , spatial variability , soil map , bulk density , digital elevation model , elevation (ballistics) , hydrology (agriculture) , remote sensing , geology , ecology , statistics , mathematics , geometry , geotechnical engineering , biology
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 (SOC C ), 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 (SOC d ) was related to elevation, ECa, and peat thickness. Based on these relationships, maps of SOC d were produced. Within field variation of SOC d was high, which could be modeled by use of the covariate maps. If available, ECa maps can improve the prediction of SOC d based on elevation. Modeling peat thickness based on sensor data needs additional research, but seems to be a valuable covariate in digital soil mapping.