HJ-1A/HSI Hyperspectral images and ICESat-GLAS waveform were used to estimate regional forest aboveground biomass (AGB) in the Wangqing forestry area of Jilin Province, China. Waveform parameters (e.g., waveform length W and the terrain slope parameter TS) extracted from GLAS waveform, were used to build the maximum forest height model. In addition, the energy parameter I (the ratio of vegetation energy and total energy) extracted from GLAS waveform was used to build the forest canopy density model. The final forest AGB model was built using both the maximum forest height and forest canopy density models. However, since GLAS footprints are geographically discrete, the AGB model was unable to produce the full regional coverage of forest AGB. To overcome the discontinuity limitations, HJ-1A/HSI Hyperspectral images were combined with GLAS waveforms to predict the regional forest AGB based on the support vector regression (SVR) method, to fully map the distribution of forest AGB. Results showed that the adj.R² and RMSE of the maximum forest height model were 0.78 and 2.51 m, respectively, with adj.R² of 0.85 and RMSE of 1.67 m as validation results. In the model, TS effectively reduced the impact of terrain slope. When the below vegetation height was set at 2 m, the forest canopy density model with I as dependent variable produced the best fit, with adj.R² and RMSE of 0.64 and 0.13, respectively, and adj.R² of 0.65 and RMSE of 0.12 as validation results. Overall, the adj.R²and RMSE of the forest AGB model were 0.62 and 10.88 t/hm², respectively, with validation results of adj.R²=0.60 and RMSE=11.52 t/hm². Estimated AGB had a strong linear relationship with field inventory AGB (adj.R²=0.62, RMSE=11.11 t/hm²). This study demonstrates that combining GLAS waveform and HJ-1A/HSI hyperspectral images has significant potential to map the full coverage regional forest AGB distribution with a high degree of accuracy.