It is important to improve the accuracy of models estimating aboveground biomass (AGB) in large areas with complex geography and high forest heterogeneity. In this study, k-nearest neighbors (k-NN), gradient boosting machine (GBM), random forest (RF), quantile random forest (QRF), regularized random forest (RRF), and Bayesian regularization neural network (BRNN) machine learning algorithms were constructed to estimate the AGB of four forest types based on environmental factors and the variables selected by the Boruta algorithm in Yunnan Province and using integrated Landsat 8 OLI and Sentinel 2A images. The results showed that (1) DEM was the most important variable for estimating the AGB of coniferous forests, evergreen broadleaved forests, deciduous broadleaved forests, and mixed forests; while the vegetation index was the most important variable for estimating deciduous broadleaved forests, the climatic factors had a higher variable importance for estimating coniferous and mixed forests, and texture features and vegetation index had a higher variable importance for estimating evergreen broadleaved forests. (2) In terms of specific model performance for the four forest types, RRF was the best model both in estimating the AGB of coniferous forests and mixed forests; the R-2 and RMSE for coniferous forests were 0.63 and 43.23 Mg ha(-1), respectively, and the R-2 and RMSE for mixed forests were 0.56 and 47.79 Mg ha(-1), respectively. BRNN performed the best in estimating the AGB of evergreen broadleaved forests; the R-2 was 0.53 and the RMSE was 68.16 Mg ha(-1). QRF was the best in estimating the AGB of deciduous broadleaved forests, with R-2 of 0.43 and RMSE of 45.09 Mg ha(-1). (3) RRF was the best model for the four forest types according to the mean values, with R-2 and RMSE of 0.503 and 52.335 Mg ha(-1), respectively. In conclusion, different variables and suitable models should be considered when estimating the AGB of different forest types. This study could provide a reference for the estimation of forest AGB based on remote sensing in complex terrain areas with a high degree of forest heterogeneity.
