Abstract: The research on the identification of dominant tree species in the coniferous forest of Shangri-La was carried out to provide a reference for subsequent forest resource management and research in the area. Based on the Google Earth Engine（GEE） platform and Sentinel-1/2 time series images of 2020, the temporal characteristics of vegetation were constructed, and a total of 43 features were combined with radar features, spectral features, texture features, and terrain features. Through different combination schemes of features, hierarchical classification and random forest classification algorithms were used to finely identify the dominant tree species of four coniferous forests of Shangri-La: Pinus densata, Pinus yunnanensis, Picea asperata and Larix gmelinii. The results showed that the classification accuracy of multi-source time series data combined with all features was the highest at three levels. The overall accuracy of forest and non-forest types in the study area was 98.73%, and the Kappa coefficient was 0.97, harmonic average of user accuracy and mapping accuracy F₁ was 98.71%. The overall accuracy of coniferous and broad-leaved forests was 92.80%, the Kappa coefficient was 0.85, F₂ was 92.58%. The overall accuracy of 4 dominant tree species was 89.51%, the Kappa coefficient was 0.86, F₁ was 89.36%. Different tree species had separability on different features. The combination of multiple features can improve the accuracy of tree species identification to a certain extent. Based on GEE platform and Sentinel-1/2 multi-source time series data can perform fine identification of forest dominant tree species at a spatial resolution of 10 m.