The Biomass model of Simao pine(Pinus kesiya var.langbianensis)was built based on the data collected from 120 Simao pine sampling trees,Landsat TM images in 2005 and the data of forest resource inventory in 2006 in Jinggu County,Yunnan Province.Then the remote sensing biomass estimation Model of Simao Pine were built by the ordinary least square(OLS)and geographically weighed regression(GWR).The results showed that:GWR model had a better fitting effect than OLS,in which coefficient of determination(R ²)was significantly bigger than the OLS model,Akaike information index(AIC)reduced by 7.832;It was obviously depicted from the sample test of independence that model prediction accuracy was improved from 72.70%(OLS)to 75.06%(GWR).The unit-area biomass was 49.02t / hm ² by inversion,and basically consistent with the measured data;it was lower than the measured data 1.229%,and less than the estimation value of OLS.The total biomass of Simao pine in Jinggu County was 2.101×10 ⁷ t based on GWR model.The study indicated that forest aboveground biomass estimation based on geographically weighed regression(GWR)model could improve effectively the fitting accuracy of forest biomass estimation model,and could be used to estimate the biomass of Simao pine forest by remote sensing.