Picea schrenkiana (Schrenk's spruce) forest is a major water conservation forest located in the Tianshan Mountains of Xinjiang Province.Therefore, estimating the biomass of Picea schrenkiana forest accurately and characterizing its spatial pattern exactly are significant for assessing the biological productivity and ecological service function of its ecosystem. Based on the data acquired by Landsat 8 OLI in remote sensing as well as a survey of 66 sample plots of Picea schrenkiana forest in the Tianshan Mountains, this study selects 42 independent variables including the gray value of each band,the linear and non-linear combinations of gray values in different bands (including 5 vegetation indexes), and adopts the multiple stepwise regression analysis method, the partial least squares method, and principal component analysis method to establish a biomass estimation model for the Picea schrenkiana forest in the Tianshan Mountains. The results reveal as follows: the multiple stepwise regression method adopts three independent variables to establish a model. In this model, the average fitting accuracy is 69.07%, the absolute error is 64.50 t/hm², the average relative error is 10.89%, and the correlation coefficient between the measured value and predicted values of the biomass in the sample plots is 0.465; the partial least squares regression method applies 11 independent variables to establish a model. In this model, the average fitting accuracy is 74.36%, the absolute error is 144.94 t/hm², the average relative error is 28.78%, and the correlation coefficient is 0.717; the principal component analysis method extracts 3 principal components to establish a model. In this model, the average fitting accuracy is 71.22%, and the correlation coefficient is 0.730. Hence, the partial least square method is better than the principal component analysis method and the multiple stepwise regression method. The biomass of the Picea schrenkiana forest in the Tianshan Mountains decreases with the increase of latitude and longitude. The overall trend is high in the west and low in the middle and the east; it changes in a ″single peak″ shape with increasing altitude; the biomass of the sample plots is mainly distributed in the ridge position, which tends to decrease first, then increases with the increase of slope, and decreases ultimately; with the change of slope direction (from the shaded slope to the sunny slope), the biomass of the sample plot decreases gradually.