县域农田土壤铜含量的协同克里格插值及采样数量优化

 【目的】研究县域农田土壤铜含量的空间分布和采样数量,为农田土壤环境质量调查提供帮助。【方法】采用协同克里格方法,以初始的623个土壤铜含量数据及在此基础上随机抽取的560、498和432个数据为目标变量,并以初始的623个土壤有机质含量数据为辅助变量,对四川省双流县农田土壤铜含量进行插值分析,并对不同样点数量下协同克里格法在县域尺度农田土壤铜含量空间分布研究中的适用性进行评价。【结果】相同取样数量下,协同克里格法的均方根误差相对于普通克里格法可降低0.9%～7.77%,预测值和实测值之间的相关系数可提高1.76%至9.76%。利用协同克里格法,在土壤铜含量数据量缩减10%的情况下,其估值精度仍高于初始的623个土壤铜含量数据的普通克里格估值,且二者的分布图具有高度相似性。【结论】协同克里格作为一种更为精确和经济的方法,可为县域尺度农田土壤重金属含量的空间分布研究提供更多的信息和帮助。

Spatial Interpolation and Sampling Numbers of the Concentration of Copper in Cropland Soil on County Scale Using Cokriging

【Objective】 Studies on the spatial distribution and sampling numbers of the concentration of copper (Cu) on county scale were made to provide a help for the investigation of environmental quality of cropland soil. 【Method】 In this study, Cokriging was used for the interpolation of the concentration of Cu in cropland soil in Shuangliu county in Sichuan province. A total of original 623 measured data of the concentration of Cu in soil and 560, 498, 432 measured data by random were selected as target variable, original 623 measured data of soil organic matter (OM) content as auxiliary variable. The interpolation methods using Cokriging under different sampling numbers were evaluated for the suitability of estimating the spatial distribution of the concentration of Cu in soil on county scale. 【Result】 Compared with the ordinary Kriging under the same sampling numbers, the root-mean-square error produced by Cokriging decreased by 0.9% to 7.77%, correlation coefficient between the predicted value and the measured value increased by 1.76% to 9.76%. The prediction accuracy of original data reduced by 10% using Cokriging was still higher than original 623 data using ordinary Kriging and their interpolation maps were quite similar. 【Conclusion】 Cokriging was shown to be more accurate and economic method which could provide more information and help for the study on the spatial distribution of the concentration of Cu in soil on county scale.