###### Abstract
LiDAR, an active remote-sensing technology, captures forests' vertical structures in great detail and provides 3D information, such as the geoscience laser altimeter system (GLAS), which has found favor in biomass estimation with an improved accuracy, but with complex data-processing, and the lack of space continuity problems [23; 24]. Additionally, some research using 3-D terrestrial LiDAR has shown bias in biomass, especially from tropical species, mainly because of the underestimation of tree height [25; 26]. In other words, the accuracy of forest AGB estimates could be improved by a combined use of multi-source remote sensing data. The above-mentioned Sentinel satellite constellation series, including the Sentinel-1 C-band Synthetic Aperture Radar (SAR) and the Sentinel-2 multispectral instrument by the European Space Agency (ESA), provide new capabilities for AGB mapping with wide coverage, a short return cycle, and a long lifespan as the same data format [27; 28; 29]. In other words, the Sentinel series may have a high synergetic potential for overcoming the limitation of single remote sensing techniques for forest AGB estimation. The Sentinel imagery have been applied in a number of previous vegetation studies, focusing on classification [30; 31; 32], vegetation parameters on agricultural fields [33; 34; 35], grassland [36; 37; 38], and forests [39; 40], as well as the damage extent of disasters [41; 42; 43], while forest AGB mapping based on Sentinel imagery is still insufficient. The techniques for estimating forests' AGB based on remote sensing data have allowed for'scale-up' or extrapolation of the field data collected for larger scales [8; 44]. It is a predictive mapping process for an estimation of the value at a location without direct observation. It depends on the values of points at nearby sites where observations were made, and/or values of other factors at the sites, through various methods. Those methods can be divided into two categories: parametric and non-parametric algorithms [45; 46]. The former refers to statistical regression methods, such as the stepwise regression models (SWR) and geographically weighted regression (GWR), by which the expression relating to the dependent variable (AGB) and the independent variables is easy to calculate [2]. However, there is no simple global linear relationship like the SWR model, between remote sensing data and forest AGB, because it is affected by many factors. The GWR method explores spatial heterogeneity, as well as the non-stationarity of relationships, and it estimates the parameters for each sample location, which makes it a very attractive tool for remotely-sensed biomass modeling [47; 48]. Non-parametric techniques, including machine learning (ML) methods such as the k-nearest neighbor (KNN), artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF), have a better ability for identifying complex relationships between predictors and the forest AGB [2; 49]. Despite a variety of forest AGB models, quite a few research studies merely focused on one parametric or non-parametric model are unpersuasive. Thus, a systematic comparison of GWR, ANN, SVR, and RF for mapping forest AGB based on Sentinel imagery is fairly urgent, but also rare in the literature. In this study, the ability of Sentinel-1 and Sentinel-2 imagery for the retrieval and predictive mapping of forests' AGB estimation was evaluated. The specific objectives included the following: (1) to determine and model the relationship between field-measured forests AGB and Sentinel-based predictors, including Sentinel-1 SAR backscatter information and Sentinel-2 multispectral indices based on GWR and ML; (2) to evaluate and compare the accuracy of the biomass prediction models, including GWR, ANN, SVR, and RF models; and (3) to map forest AGB spatial distribution by four optimal models.