Abstract: Forest aboveground biomass (AGB) serves as a significant indicator for monitoring forest resources and a crucial part of forest carbon stock. AGB estimation methods, characterized by simple models and physical significance, play a significant role in improving the monitoring efficiency of forest resources. Based on previous studies, this study proposed an allometric model method for AGB estimation by integrating the height features and forest canopy closure derived from the airborne light detection and ranging (LiDAR), and the vegetation indice derived from satellite multispectral data (also referred to as ModelB_N). This study investigated Genhe City in Inner Mongolia using LiDAR data and Sentinel-2A multispectral data acquired in 2022, combined with sample plot data obtained around this period. By comparatively analyzing the correlations of LiDAR-derived height features and vegetation indices with AGB, this study applied optimal LiDAR-derived height features and vegetation indices to ModelB_N. Finally, this model was compared with models using only height features (ModelB), integrating both height features and vegetation indices (ModelB_Y), and combining height features and canopy closure (ModelB_HC). The results indicate that among the LiDAR-derived height features, the 90th height percentile (H90) exhibited the highest correlation with AGB in the study area. Among the vegetation indices, the kernel normalized difference vegetation index manifested the highest correlation with AGB. Among the four models, the ModelB_N achieved the highest adjusted R-square value ($R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2$, 0.78), the highest estimation accuracy (EA, 83.25 %), and the lowest root mean square error (RMSE, 15.87 t/m²). The ModelB_N outperformed the ModelB_HC, with improvements in $R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2$ value and EA by 0.05 and 1.75 %, respectively, and a reduction in RMSE by 1.66 t/hm². The ModelB_Y outperformed the ModelB, with improvements in $R_{\mathrm{a}\mathrm{d}\mathrm{j}}^2$ value and EA by 0.03 and 1.19 %, respectively, and a reduction in RMSE by 1.12 t/hm². These results demonstrate the rationality of using vegetation indices as an exponential power. Despite the failure to possess the lowest uncertainty in all pixels, the ModelB_N showed the optimal performance. Overall, the ModelB_N demonstrates the highest accuracy, a simple and efficient process, and certain physical significance. Therefore, the ModelB_N can function as a novel technique for AGB estimation to provide technical support for forest resource monitoring.