## 6 Conclusions  
To map the distribution of forest AGB at a regional scale, Sentinel SAR and multispectral imagery were selected for a group of field quadrats with a resolution of 10 m. Four AGB models, one GWR model, and three ML models were built using these field measurements and remote-sensing datasets. The results demonstrated that SVR with SMO algorithms are the best for spatially predicting and mapping the patterns of AGB in the study site. The results also showed that the texture characteristics of Sentinel-1 and the vegetation biophysical variables of Sentinel-2 were the most relative and important predictors for explaining the observed variability of AGB in the area, and that the contributions of the other Sentinel-derived factors were only marginal. The AGB estimation of broadleaved deciduous forests was the most accurate, while the AGB above 160 Mg\(\cdot\)ha\({}^{-1}\) had the least accuracy, indicating data saturation of Sentinel imagery. Overall, the performance of the models in this study will inform the selection of predictive mapping techniques for forest AGB modeling, while the map that is generated will be instrumental for formulating spatially-targeted climate change mitigation and sustainable land management strategies. In the future, the model performance will be improved by incorporating other important environmental data (e.g., distance to the city center and roads, as well as human  
Figure 7: The mean errors of the above-ground biomass predictions for (**a**) different forest types, (**b**) different AGB using geographically weighted regression (GWR), the multi-layer perception neural network (ANN), support vector machines for regression (SVR), and random forests (RF). EC, BD, DC, and MBC represent evergreen coniferous, broadleaved deciduous, deciduous coniferous, and mixed broadleaf-conifer forests, respectively.  
disturbance) and other up-to-date remote sensing techniques (e.g., Tandem-X and LiDAR), as well as the stochastic component of AGB.  
Although SAR C band and optical multispectral techniques have few advantages for detecting the sensibility of forest AGB compared to SAR P band or LiDAR, the available free Sentinel series at a relatively high spatial resolution with full coverage is indeed useful information for applications in global forest AGB estimation. The study demonstrated encouraging results in forest AGB mapping of the normal vegetated area using Sentinel imagery; thus, it is helpful and valuable for vital information mining from the Sentinel series when it is applied to global forest AGB estimation.  
[PERSON], [PERSON], and [PERSON] designed this research. [PERSON] and [PERSON] conducted field sampling and performed the experiments. [PERSON] conducted the analysis and wrote the paper. [PERSON], [PERSON], and [PERSON] drafted the paper and revised it critically. All authors reviewed the manuscript.  
This study was supported by the National Key Research and Development Project of China (No. 2016 YFC0500300).  
The authors are grateful to the ESA ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/))) and USGS ([[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/))) for providing the Sentinel imagery and SRTM DEM.  
The authors declare no conflict of interest.