A time-series filtering algorithm named the best slope temporal segmentation filtering (BSTS) algorithm was developed to improve the estimation accuracy of above-ground biomass (AGB) of Pinus densata Mast. In this algorithm, the best slope was used to interrupt the time series. Following interruptions, linear regression was employed to simulate each segment of time series. By splicing the time-series fragments, a new reconstructed time series would be generated. The algorithm was used to reconstruct the annual synthetic Landsat surface reflectance images collection in Shangri-La from 1987 to 2019. Permanent sampling plots of Pinus densata Mast. in 1987, 1992, 1997, 2002, 2007, 2012, and 2017 were collected. Then the multiple linear regression and random forests regression (RFR) were used to estimate the AGB of the Pinus densata Mast. The Landsat time series used before and after filtering were evaluated based on the quantitative analysis. The results with RFR showed that for modeling, the R² of the time series reconstructed by the BSTS is 0.913, which is higher than the data before reconstructed (R²  =  0.869). The root-mean-square error (RMSE) of the reconstructed time series (RMSE  =  35.38  t  /  ha) is significantly lower than the data before reconstructed (RMSE  =  43.43  t  /  ha). For validation, the average prediction accuracy (p) before filtering is 61.92%, and the relative root-mean-square error (rRMSE) is 48.52%; whereas the p after filtering is increased to 70.66%, and the rRMSE is reduced to 35.38%. In summary, the BSTS has improved the accuracy of remote sensing-based estimation of AGB of Pinus densata Mast., which provided a method for large area AGB mapping for Pinus densata Mast. in Shangri-La.