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At present, various interpolation methods have been used for obtaining the spatial variability of soil organic carbon (SOC) in some regions. However, systematic research on accuracy comparison and the influence of method selection on revealing SOC variability is still lacking for the hilly red soil region of China. In this study, the spatial distributions of SOC were interpolated via three kriging methods (ordinary kriging (OK), kriging combined with soil type information (KSt), and with land-use information (KLu)) and three polygon-based methods (linking sample points data to the sampling grid (PGr), to soil type polygon (PSt), and to land-use polygon (PLu)) in Yujiang County, Jiangxi Province, China. To illustrate the influence of method selection on revealing SOC variability, the prediction efficiencies of these methods were compared with soil validation samples. The results showed that the KLu and PLu had high prediction accuracy, followed immediately by the KSt and PSt, while the OK and PGr had very low accuracies. Meanwhile, the SOC distribution contours obtained by KLu, PLu, KSt, and PSt could better reflect the real conditions of the study area, as they were consistent with the variation of land-use or soil types. However, the contours from the OK and PGr had poor performance with sketchy borderlines and regular homogeneous grid cells, respectively. Therefore, great differences exist in the prediction efficiency of different methods, and the interpolation method selection has an important impact on revealing SOC variability in the hilly red soil region of China. The results can provide a useful reference for the efficient SOC prediction and simulation in similar soil regions.

Influence of the Selection of Interpolation Method on Revealing Soil Organic Carbon Variability in the Red Soil Region, China