Knowledge on spatial distribution and sampling size optimization of soil copper(Cu) could lay solid foundations for environmetal quality survey of agricultural soils at county scale.In this investigation,cokriging method was used to conduct the interpolation of Cu concentraiton in cropland soil in Shuangliu County,Sichuan Province,China.Based on the original 623 physicochmically measured soil samples,560,498,and 432 sub-samples were randomly selected as target variable and soil organic matter(SOM) of the whole original samples as auxiliary variable.Interpolation results using Cokriging under different sampling numbers were evaluated for their applicability in estimating the spatial distribution of soil Cu at county sacle.The results showed that the root mean square error(RMSE) produced by Cokriging decreased from 0.9 to 7.77%,correlation coefficient between the predicted values and the measured increased from 1.76 to 9.76% in comparison with the ordinary Kriging under the corresponding sample sizes.The prediction accuracy using Cokriging was still higher than original 623 data using ordinary Kriging even as sample size reduced 10%,and their interpolation maps were highly in agreement.Therefore,Cokriging was proven to be a more accurate and economic method which could provide more information and benefit for the studies on spatial distribution of soil pollutants at county scale. Knowledge on spatial distribution and sampling size optimization of soil copper (Cu) could lay solid foundations for environmetal quality survey of agricultural soils at county scale. In this investigation, cokriging method was used to conduct the interpolation of Cu concentraiton in cropland soil in Shuangliu County , Sichuan Province, China. Based on the original 623 physicochmically measured soil samples, 560,498, and 432 sub-samples were randomly selected as target variable and soil organic matter (SOM) of the whole original samples as auxiliary variable. Interpolation results using Cokriging under different sampling numbers were evaluated as their applicability in estimating the spatial distribution of soil Cu at county sacle. The results showed that the root mean square error (RMSE) produced by Cokriging decreased from 0.9 to 7.77%, correlation coefficient between the predicted values ​​and the measured increased from 1.76 to 9.76% in comparison with the ordinary Kriging under the corresponding sample sizes. The prediction accuracy using Cokriging was still higher than original 623 data using ordinary Kriging even as sample size reduced 10%, and their interpolation maps were highly in agreement.Therefore, Cokriging was proven to be a more accurate and economic method which could provide more information and benefit for the studies on spatial distribution of soil pollutants at county scale.