###### Abstract  
Accurate forest above-ground biomass (AGB) is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. Recently launched Sentinel imagery offers a new opportunity for forest AGB mapping and monitoring. In this study, texture characteristics and backscatter coefficients of Sentinel-1, in addition to multispectral bands, vegetation indices, and biophysical variables of Sentinel-2, based on 56 measured AGB samples in the center of the Changbai Mountains, China, were used to develop biomass prediction models through geographically weighted regression (GWR) and machine learning (ML) algorithms, such as the artificial neural network (ANN), support vector machine for regression (SVR), and random forest (RF). The results showed that texture characteristics and vegetation biophysical variables were the most important predictors. SVR was the best method for predicting and mapping the patterns of AGB in the study site with limited samples, whose mean error, mean absolute error, root mean square error, and correlation coefficient were 4 \(\times\) 10\({}^{-3}\), 0.07, 0.08 Mg\(\cdot\)ha\({}^{-1}\), and 1, respectively. Predicted values of AGB from four models ranged from 11.80 to 324.12 Mg\(\cdot\)ha\({}^{-1}\), and those for broadleaved deciduous forests were the most accurate, while those for AGB above 160 Mg\(\cdot\)ha\({}^{-1}\) were the least accurate. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using the freely accessible and high-resolution Sentinel imagery, based on ML techniques. s 2018 9 582 19 582  
Traditional field-based measurements provide the most accurate AGB values, but they are destructive and spatially limited [10; 11]. Uncertainty and bias in field measurements obviously exist, particularly those with large trees and tropical issues [4; 5]. Combining remote sensing and sample plot data has become a popular method to generate spatially explicit estimations of forest AGB [12; 13]. Various types of remote-sensing data are used for forest biomass estimation such as optical sensor data, radio detection and ranging (radar) data, and light detection and ranging (LiDAR) data, with each one having certain advantages over the others [14; 15]. Optical sensors were first applied to retrieve the horizontal forest structure and AGB assessments through field sampling, due to their aggregate spectral signatures (reflectance or vegetation indices) with global coverage, repetitiveness, and cost-effectiveness [16; 17]. Optical remote sensing data from a number of platforms, such as IKONOS, Quickbird, Worldview, ZY-3, systeme probatoire d'observation de la terre (SPOT), Sentinel, Landsat, and moderate-resolution imaging spectroradiometer (MODIS), with spatial resolutions varying from less than one meter to hundreds of meters, have been used by numerous researchers for biomass estimation [18; 19; 20]. However, the widespread usage of optical data is limited by its poor penetration capacity through clouds and forest canopies, as well as data saturation problems [21]. Radar data, available internationally from airborne or space-borne systems with different frequency bands, polarizations, and variable imaging geometries, such as Terra-SAR (Terra-Synthetic Aperture Radar), advanced land observing satellite phased array L-band synthetic aperture radar (ALOS PALSAR), and Sentinel, have gained prominence for AGB estimation because of their better penetration ability and detailed vegetation structural information, but these still suffer from signal saturation problems [6; 14; 22].