Spectral reflectance can reflect the difference of ground objects, which is the theoretical basis of forest aboveground biomass (AGB) in remote sensing inversion. Red edge band is located in the special wavelength and scope which change fast at the junction of near-infrared and red bands, which can quickly respond to the small changes of vegetation canopy structure and chlorophyll content. Compared with other bands, red edge band is more sensitive to vegetation growth and chlorophyll change. In this study, GF-6 and Sentinel-2 multispectral images were used as data sources, and the linear and nonlinear models were constructed on the basis of Larch and Scotch pine field survey data for AGB estimation. The estimation accuracy of all models was compared, and the model with the highest estimation accuracy was regarded as the optimal model for the final AGB mapping in the study area. The results showed that the red edge reflectance of GF-6 and Sentinel-2 images were both significantly correlated with AGB (P<0.05), and red edge bands were more sensitive to AGB estimation than other bands. On the whole, the estimation effect of multivariable estimation model was better than that of univariate model. Multivariate linear regression (MLR) model obtained the highest determination coefficient (R²=0.66 for Larch and 0.55 for Scotch pine) and the lowest root mean square error (RMSE=31.45 t/hm² for Larch and 54.77 t/hm² for Scotch pine). Compared with single data source (GF-6 or Sentinel-2), the estimation effect of multiple linear regression model constructed by combining GF-6 and Sentinel-2 images was significantly improved, and the RMSE of AGB estimation model for Larch and Scotch pine decreased by 22.9% and 11.2% in highest measure, respectively. Taking the red edge band as additional variables for AGB estimation can significantly improve the estimation accuracy and effect of the model. The RMSE of the model was significantly reduced by adding red edge band information to three groups of data sources which are GF-6, Sentinel-2 and Sentinel-2 combined with GF-6. GF-6 has an observation width of 800 km and efficient revisit period, which can provide large-scale time series data quickly. As a remote sensing data source, GF-6 has great potential in forest aboveground biomass estimation and dynamic monitoring.