Accurate estimation of arbor forest biomass is of great significance for the study of forest ecological function and carbon storage. Because of the spatial heterogeneity of the survey factors, the geographically weighted regression method can estimate the local regression of variables and show a good application advantage. Based on the survey data of carbon sinks in Zhejiang Province, taking the biomass of arbor forest (including aboveground and belowground biomass) as dependent variable and factors with high correlation with dependent variable as the explanatory variables, the biomass of arbor forest was estimated by using the geographically weighted regression and co-Kriging methods and compared the accuracy of the two estimation methods. The results showed that the accuracy of arbor forest biomass estimation model (R2adj was 0.8204, RMSE was 23.0215t/hm2) constructed by geographically weighted regression method was better than that of co-Kriging method (R2adj was 0.7263, RMSE was 28.0549t/hm2).The coefficient of variation (Cv was 0.6189) of the prediction value of biomass of arbor forest using geographically weighted regression method was higher than that of the co-Kriging method (Cv was 0.5854). Because of considering the local variation of the estimated variables, the geographically weighted regression method had better fitting results than co-Kriging method, and the prediction accuracy was high. This study can provide a reference for estimating the forest biomass and other forest parameters in a wide range of tree stands by using the geographically weighting regression method.