Ancillary information improves kriging on soil organic carbon data for a typical karst peak cluster depression landscape

文献类型： 外文期刊

第一作者： Zhang, Wei

作者： Zhang, Wei;Wang, Kelin;Chen, Hongsong;He, Xunyang;Zhang, Wei;Wang, Kelin;Chen, Hongsong;He, Xunyang;Zhang, Jiguang

作者机构：

关键词： southwestern China;karst peak cluster depression landscape;kriging;soil organic carbon;spatial variability

期刊名称：JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE （ 影响因子：3.638； 五年影响因子：3.802 ）

ISSN： 0022-5142

年卷期： 2012 年 92 卷 5 期

页码：

收录情况： SCI

摘要： BACKGROUND: Soil carbon management at landscape scale requires reliable information on the spatial distribution of soil organic carbon (SOC). However, how to improve the accuracy of spatial prediction is not well addressed in the karst region of southwestern China. This study evaluates the performance of univariate kriging (ordinary kriging (OK)) and hybrid kriging (co-kriging (CK), regression kriging (RK) and residual maximum likelihood (REML)) in mapping the spatial distribution of SOC at a depth of 015 cm. Terrain attributes and the normalised difference vegetation index (NDVI) were used as ancillary variables. RESULTS: The distribution of SOC was significantly related to NDVI and terrain attributes. Furthermore, geostatistical analyses reflected a moderately structured spatial correlation of SOC. Regression analyses identified the NDVI and slope as the best predictors for describing the spatial pattern of SOC. Combined with NDVI and slope gradient, REML and RK performed better in increasingmap prediction accuracy and decreasing the soothing effect of kriging. CONCLUSION: The spatial pattern of SOC was controlled by topography and cultivation activity. The predictive abilities of OK and CK were limited. Combined with the auxiliary variables, REML and RK can improve the prediction accuracy. This study is beneficial for the further research of precise SOC management in the typical karst landscape. (C) 2012 Society of Chemical Industry

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