**5. Conclusions**

This study explored the potential of GF-2 optical, GF-3 SAR, and UAV-based DSM data for estimating the AGB of the mangrove plantation of Qi'ao Island in China. The AGB model generated using a combination of GF-2 and GF-3 images from the Chinese civilian HDEOS program yielded a higher accuracy than those of models using only one of these datasets, with a reduction of 2.32% and 3.49%, respectively, in RMSEr. When considering variables of the DSM derived from the UAV platform, the AGB model achieved the highest accuracy with a further reduction of 2.7% decrease in RMSEr. The DSM was the most important input variable for AGB estimation as it deals with saturation problems in optical and SAR images. The resulting AGB map agreed well with field surveys and the growing sequence of mangrove plantations. The results showed that accurate AGB models and spatial distribution maps of mangrove plantation can be obtained using the RF model, and images from multiple sources (GF-2 optical, GF-2 SAR, and UAV-based DSM data). The combination of these data provided canopy-related information, forest structures, and tree heights for AGB modeling.

The study focused on the integration of GF2 optical, GF3 SAR, and UAV data for estimating aboveground biomass in China's largest artificially planted mangroves. The methodology can be used to produce accurate AGB models of mangrove forests, which can be difficult to obtain by field investigation. The AGB maps of *S. apetala* can help measure mangrove carbon sinks and provide baseline data for REDD+ programs, due to mangrove plantation. Future studies should further examine and improve AGB estimation uncertainty, such as accurate radiometric calibration and noise estimation of GF-3.

**Author Contributions:** Conceptualization, Y.Z., K.L. and L.L. conceived and designed the paper. Y.Z., K.L., J.C. and Z.D. conducted field study and data processing. Y.Z., K.L., L.L., S.W.M., Y.L. and Z.W. wrote and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is supported by Research Team Program of Natural Science Foundation of Guangdong Province, China (2014A030312010), China Postdoctoral Science Foundation (2018M633023), Postdoctoral International Training Program of Guangzhou City, Science and Technology Planning Project of Guangdong Province (2017A020217003), the Natural Science Foundation of Guangdong (2016A030313261 and 2016A030313188), and the National Science Foundation of China (Grant No. 41501368).

**Conflicts of Interest:** The authors declare no conflict of interest
