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
Identifiers and Pagination:Year: 2015
First Page: 1022
Last Page: 1027
Publisher Id: TOCIEJ-9-1022
Article History:Received Date: 3/2/2015
Revision Received Date: 3/4/2015
Acceptance Date: 25/5/2015
Electronic publication date: 29/10/2015
Collection year: 2015
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: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.