This paper presents four methods for estimating spatial distribution of soil organic matter and examines these methods’sensitivity to the sampling number.The four methods are ordinary kriging(OK),simple linear regression(RG),cokriging(COK),and regression-kriging(RGK).All sampling sites are randomly divided into two groups: interpolation dataset(200 points) and validation dataset(62 points).The organic matter of interpolation subset and alkalizable nitrogen of all observations are used to mapping soil organic matter.Among four methods,ordinary kriging,only using the information of organic matter,yields lowest accurate predictions and smallest proportion of the total variation,while regression-kriging using secondary data(alkalizable nitrogen) yields highest accuracy and largest variation explainable.To examine the effect of sampling number on the performance of four mapping methods,four subsets of 40,80,120,160 sampling sites are randomly selected from the interpolation dataset.For each subset,organic matter is estimated over the study area by four methods,respectively.The results show that the accuracy performances of four methods are RGK>COK>RG>OK.Moreover,the results indicate that the performance of simple linear regression remain stable,and that others perform better when the sample size of organic matter increased.
LI Xiang, PAN Yu-Chun, ZHAO Chun-Jiang, WANG Ji-Hua
. Estimation of Soil Organic Matter Based on Four Methods and Effect of Sampling Number on Estimation Accuracy[J]. SCIENTIA GEOGRAPHICA SINICA, 2007
, 27(5)
: 689
-694
.
DOI: 10.13249/j.cnki.sgs.2007.05.689
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