To acquire the accuracy distribution information of soil heavy metal, improving interpolation precision is very important for agricultural safety production and soil environment protection. In the present study, the spatial variation and Cokriging interpolation of soil Cd was studied in a sewage irrigation area. Fifty two soil samples were collected to measure the contents of soil total Cd (TCd), available Cd (ACd), pH, organic matter (OM), iron oxide (Fe2O3) and soil reflection spectrum. Through correlation analysis, it was found that TCd and ACd had a significant correlation with soil first-order differential spectrum (-0.585** at 759 nm and -0.551** at 719 nm, respectively), which were much higher than the correlation coefficients between soil Cd contents and other environmental variables (pH, OM and Fe2O3). The spatial patterns of soil Cd were predicted by Cokriging which used soil first-order differential spectrum as covariate. Compared with the Kriging, the root-mean-square error decreased by 8.22% for TCd and 20.09% for ACd, respectively; the correlation coefficients between the predicted values and measured values increased by 27.45% for TCd and by 53.13% for ACd, respectively. Meanwhile, the prediction accuracy improved by Cokriging with soil spectrum as covariate was still higher than by Cokriging with soil environment variables (OM and Fe2O3). Therefore, it was found that Cokriging was a more accurate interpolation method which could provide more precise distribution information of soil heavy metal. At the same time, soil reflection spectrum was shown to be more economic, time-saving and easier to acquire than these usual environment variables, which indicated that soil spectrum information is more suited as a covariate used in Cokriging.