A meta-analysis of the research on the extent and species identification of mangroves has been conducted using remote sensing since 2000. The study clarified the overall accuracy status of mangrove extent and species identification, as well as the influence of remote sensing data source, classification algorithm, feature type and species number on the overall accuracy. The results showed that the overall accuracy range of mangrove extent identification was 55.7% – 99.7% and about 66% of the researches were based on Landsat series satellite data, and had the highest overall accuracy (75% – 99.7%). Optical remote sensing and radar data fusion could effectively improve the overall accuracy of extent identification (>90%). The simpler the type of ground features (≤3 types) or the more complex (≥6 types), the overall accuracy of extent identification is higher and more stable. The overall accuracy of mangrove species identification ranged from 64% to 98.6%; the closer the spatial resolution is to the size of the plant canopy, the higher the overall accuracy of species identification. Among the high spatial resolution remote sensing data sources, the overall accuracy of the species identification of data sources with shortwave infrared bands was higher than that of non-shortwave infrared bands. Multi-source remote sensing data fusion and plants themselves feature information that helps to improve the overall accuracy of category identification. Support vector machines (SVM), maximum likelihood classification (MLC), and random forest (RF) algorithms in supervised machine learning algorithms were the most widely used and had better overall accuracy. As the number of species increases, the overall accuracy of species identification varies with remote sensing data sources and classification algorithms. In summary, the identification accuracy of mangrove extent and species may be improved to a certain extent, and remote sensing data sources, classification algorithms, feature types, and the number of species will all affect the identification accuracy.