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This article in SSSAJ

  1. Vol. 74 No. 3, p. 922-935
     
    Received: Aug 31, 2009


    * Corresponding author(s): peter.fiener@uni-koeln.de
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doi:10.2136/sssaj2009.0325

Layer-Specific Analysis and Spatial Prediction of Soil Organic Carbon Using Terrain Attributes and Erosion Modeling

  1. Verena Dlugoß,
  2. Peter Fiener * and
  3. Karl Schneider
  1. Dep. of Geography, Univ. of Cologne, Albertus Magnus Platz, 50923 Köln, Germany

Abstract

High-resolution soil organic C (SOC) maps are a major prerequisite for many environmental studies dealing with C stocks and fluxes. Especially in hilly terrain, where SOC variability is most pronounced, high-quality data are rare and costly to obtain. In this study, factors and processes influencing the spatial distribution of SOC in three soil layers (<0.25, 0.25–0.50, and 0.5–0.90 m) in a sloped agricultural catchment (4.2 ha) were statistically analyzed, utilizing terrain parameters and results from water and tillage erosion modeling (with WaTEM/SEDEM). Significantly correlated parameters were used as covariables in regression kriging (RK) to improve SOC mapping for different input data densities (6–38 soil cores ha−1) and compared with ordinary kriging (OK). In general, patterns of more complex parameters representing soil moisture and soil redistribution correlated highest with measured SOC patterns, and correlation coefficients increased with soil depth. Analogously, the relative improvement of SOC maps produced by RK increased with soil depth. Moreover, an increasing relative improvement of RK was achieved with decreasing input data density. Hence, the expected decline of interpolation quality with decreasing data density could be reduced, especially for the subsoil layers, by incorporating soil redistribution and wetness index patterns in RK. The optimal covariable differed among the soil layers. This indicates that bulk SOC patterns derived from topsoil SOC measurements might not be appropriate in sloped agricultural landscapes; however, generally more complex covariables, especially patterns of soil redistribution, exhibit a great potential to improve subsoil SOC mapping.

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