Site index information given from forest site map only exist in the sampled locations. In this study, site index for unsampled locations were estimated using kriging interpolation method which can interpolate values between point samples to generate a continuous surface. Site index of Pinus densiplora in Danyang area were calculated using Chapman-Richards model by plot unit. Then site index for unsampled locations were interpolated by theoretical variogram models and ordinary kriging. Also in order to assess parameter selection, cross-validation was performed by calculating mean error (ME), average standard error (ASE) and root mean square error (RMSE). In result, gaussian model was excluded because of the biggest relative nugget (37.40%). Then spherical model (16.80%) and exponential model (8.77%) were selected. Site index estimates of Pinus densiplora throughout the entire area in Danyang showed 4.39~19.53 based on exponential model, and 4.54~19.23 based on spherical model. By cross-validation, RMSE had almost no difference. But ME and ASE from spherical model were slightly lower than exponential model. Therefore site index prediction map from spherical model were finally selected. Average site index from site prediction map was 10.78. It can be expected that regional variance can be considered by site index prediction map in order to estimate forest biomass which has big spatial variance and eventually it is helpful to improve an accuracy of forest carbon estimation.