Song Genxin, Zhang Jing, Wang Ke
Institute of Agricultural Remote Sensing and Information Technique, Zhejiang University, Hangzhou, Zhejiang, China; and Ministry of Education Key Laboratory of Environmental Remediation, Ecological and Health, Zhejiang University, Hangzhou, Zhejiang, China.
PLoS One. 2014 Jun 13;9(6):e99695. doi: 10.1371/journal.pone.0099695. eCollection 2014.
In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes.
为了探索在使用协同克里格法进行土壤属性插值时最佳辅助变量(BAV)的选择,本文研究了在对土壤养分(有机质、全氮、有效磷和速效钾)进行协同克里格插值时,从地形参数、土壤微量元素和土壤养分属性中选择BAV的情况。在富阳共采集了670个土壤样本,并测定了土壤样本的养分和微量元素属性。基于土壤属性的空间自相关性,结合富阳的数字高程模型(DEM)数据,探讨地形参数、微量元素和土壤养分属性之间的坐标关系。将与土壤养分属性相关性高的变量选为土壤养分协同克里格插值的BAV,将相关性差的变量选为劣质辅助变量(PAV)。然后比较了使用BAV和PAV进行协同克里格插值的结果。结果表明,使用BAV进行协同克里格插值比使用PAV进行协同克里格插值得到的结果更准确(有机质、全氮、有效磷和速效钾的BAV插值结果的平均绝对误差分别为0.020、0.002、7.616和12.4702,PAV插值结果的平均绝对误差分别为0.052、0.037、15.619和0.037)。结果表明,使用BAV进行协同克里格插值可以显著提高土壤养分属性协同克里格插值的精度。本研究为协同克里格插值应用于土壤养分属性时辅助参数的选择提供了有意义的指导和参考。