Spatial Estimation of Soil Organic Matter in Karst Peak-cluster Depression

Li Hui*, 1, Jiang Zhong-Cheng2, Yang Qi-Yong3, Yin Hui1, 2, Wang Yue3
1 Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin, Guangxi 541004, China
2 Tourism Department, Huizhou University, Huizhou, Guangdong 516007, China
3 College of Environment & Resources, Guangxi Normal University, Guilin, Guangxi 541004, China

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© 2015 Hui et al;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin, Guangxi 541004, China; Tel: +8613607738053; E-mail:


In order to enhance the accuracy of spatial estimation of soil organic matter (SOM), spatial predictions of SOM in 0~20 cm depth were conducted in Guohua Ecological Experimental Area of Minister of Land and Resource of the People’s Republic of China. Analysis of multiple linear stepwise regressions showed that the two terrain attributes of relief degree of land surface (RS) and distance from ridge of mountains (DFR) entered into the regression equation. Therefore, RS and DFR were selected as auxiliary variables to predict SOM by MCOK and RK methods. The accuracy of spatial estimation of SOM was compared among methods of ordinary kriging (OK), multivariable cokriging (MCOK) and regression kriging (RK). Results showed that RK and MCOK methods with terrain attributes as auxiliary variables could enhance the accuracy of spatial estimation of SOM, and MCOK method could promote the accuracy notable by 31.33%. This study can provide a new idea and method for evaluation of soil quality in karst areas.

Keywords: Auxiliary variable, cokriging, regression kriging, soil organic matter, terrain attribute.

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