## 1 Introduction  
The forest ecosystem plays a key role in carbon cycling, gas and matter exchange processes between the biosphere and atmosphere, and accounts for 45-60% carbon stock of a terrestrial ecosystem [1; 2; 3]. As the accumulated organic matter of a living standing tree or forest stand during a certain period [4], forest AGB works as a pivotal parameter to help quantitatively assess the carbon sequestration capacity of a forest ecosystem [5]. Thus, it is necessary to dynamically monitor the spatiotemporal distribution of the forest AGB and its annual increment [6; 7] for studying the forest ecosystem carbon cycle and understanding impacts of global climate changes. So far, three kinds of methods have been proposed to estimate the forest AGB, including national forest inventory (NFI)-based methods, remotely sensed data-based methods and process model-based methods. Field-based methods can observe accurate parameters such as tree height and diameter at breast height (DBH), which relate closely with AGB estimation. However, these methods mostly focus on some of the representative wood--rather than all forest biomass--and are always labor intensive and time consuming. Although the remote sensing-based methods have advantages of large-coverage and short revisitation period, they cannot observe tree height and DHB directly. In most cases, the optical remotely sensed-based methods retrieve LAI and coverage degree based on canopy reflectance from sensors, then estimate the AGB. However, given that the canopy reflectance is influenced by both overstory and background vegetation, the remotely sensed-based AGB is always overestimated or underestimated for sparse or dense forests, respectively. The ALS-based methods can retrieve tree height information, but have difficulty in obtaining DHB. Combining field data and optical remotely sensed data for mapping forest AGB is a more ideal way at the landscape level [5; 8; 9; 10; 11; 12; 13; 14]. For example, [PERSON] et al. [4] obtained the spatial distribution map of forest AGB in Wisconsin (USA) using Landsat 7 ETM+ data combined with NFI data. The biomass for boreal forest was estimated by [PERSON] et al. [14] using ASTER satellite data combined with standwise NFI data. Nevertheless, restricted by the characteristics of optical imaging, only two dimensional (2-D) structural information of land surface objects can be captured by satellite sensors. This shortcoming could not be overcome until the emergence of light detection and ranging (lidar) by the active scanning system such as ALS. The lidar-based point cloud data generated by ALS have shown great potential to effectively improve the accuracy of retrieving biophysical parameters of forest canopies from three dimensional (3-D) perspective [15; 16; 17; 18; 19; 20; 21]. For instance, Siberian timber volume was estimated by combining Moderate Resolution Imaging Spectroradiometer (MODIS) and ICESat/GLAS data [15]. [PERSON] et al. [16] developed an approach to estimate the single tree branch biomass of Norway spruce using ALS data. Although the lidar-based method does have relatively high precision when applied to calculate the forest AGB, it is not the most ideal method to be used at national or regional scales because of the expensive cost of ALS data acquisition and the unavailable space-borne lidar systems since the ICESat mission (2003-2009) termination [21]. Therefore, in consideration of the pros and cons of ALS data, they were only used as validation data in this study. In the past few decades, physical process-based ecological models [22; 23; 24], such as the BEPS model [24], have been used to simulate key physiological processes including both carbon and water cycles at national or regional scales. Based on the interaction mechanisms of the electromagnetic waves and vegetation, the BEPS model could be used for mapping and updating the spatiotemporal distribution of forest AGB at regional scales driven by multi-source remotely sensed data.