Mangrove forests are among the ecosystems with the highest net primary productivity in the world， and they play an important role in the study of global climate change and the evolution of coastal zone geography. Rapid and accurate acquisition of the spatial distribution of mangroves on a large scale is vital for effectively managing and exploiting mangrove resources. Landsat satellite images have become an important data source for extracting large-scale and long-period mangrove distribution information. Yingluo Bay and Pearl Bay along the coast of Guangxi， China are selected as the study sites in this study. Landsat-8 OLI images are used to construct five indices to extract the distribution of mangroves， including Normalized Difference Mangrove Index （NDMI）， Combined Mangrove Recognition Index （CMRI）， Modular Mangrove Recognition Index （MMRI）， Mangrove Index （MI） and Mangrove Vegetation Index （MVI）. This study compared the efficiency of different indices used for mangrove extraction to determine the optimal mangrove extraction index. Optimizing the mangrove distribution information extraction is proposed by combining Normalized Difference Water Index （NDWI） index. The aim is administrator improve the remote sensing classification accuracy of mangroves. It is also applied to the extraction of coastal mangroves in Guangxi. The results showed that： Mangrove distribution can be effectively extracted based on Landsat-8 OLI satellite images and index method. By comparing the extraction accuracy of five indices of mangroves， we found that the MVI has the best extraction effect and the CMRI has the worst extraction effect. The combination of NDWI can better optimize the extraction accuracy of mangroves， and the optimized MVI applied to Guangxi coastal mangroves showed the best extraction results with an overall accuracy of 97.10%. The research strategy and the range of mangrove index thresholds in this paper can provide reference and decision support for large-scale mangrove distribution extraction.