Sonneratia apetala is an exotic mangrove species introduced from outside Guangxi Zhuagn Autonomous Region. Quantitative algorithm is used to accurately estimate the aboveground biomass (AGB) of Sonneratia apetala, which provides experience and methods for mangrove ecological restoration and marine blue carbon monitoring. This paper takes the Sonneratia apetalous mangrove of Mawei Sea Nature Reserve in Guangxi as the research object,and takes the field measurd aboveground biomass data of Apetalous mangrove and the backscatter data, band data, vegetation index data and texture index data extracted by Sentinel-1/2 satellite as the data sources. EXtreme Gradient Boosting (XGBoost) machine learning algorithm was used to compare the effects of different variable combinations on the model accuracy by analyzing the importance relationship between each remote sensing variable and the measured AGB of Sonneratia apetala mangrove. Finally, the AGB of Sonneratia apetala mangrove was retrieved based on the optimal combination of variables. The results showed that:(1) The measured height of Sonneratia apetala mangrove in the study area ranged from 1.55m to 13.58m, with an average of 8.37m, and the diameter at breast height(DBH) ranged from 0.7 cm to 41 cm, with an average of 15.62 cm. (2) The fitting effect of the 21 feature variables combination model optimized by XGBoost algorithm was better, and its model R²=0.7237 and RMSE=21.70Mg/hm² in the testing phase. The AGB of Sonneratia apetala mangrove in the study area ranged from 19.14Mg/hm² to 138.46Mg/hm², with an average of 51.92Mg/hm². (3) Cross polarization(VH) backscattering coefficient derived from Sentinel-1 data contributed the most to AGB of Sonneratia apetala mangrove. (4) The high-value areas of the aboveground biomass of Sonneratia apetala mangrove are mainly distributed in the north, northwest and southwest regions to the west, and the low-value areas are mainly distributed in the east and southeast regions to the east. The inversion results were consistent with the actual survey results. In conclusion, XGBoost machine learning algorithm shows good application ability in AGB of Sonneratia apetala mangrove.