Abstract: Mangrove forests are periodically inundated by tidal water. This characteristic opens up an opportunity but also poses a challenge for the information extraction of mangrove forests using remote sensing technology. To explore the contribution of the red-edge band of GF-6 satellite data in information extraction of mangrove forests under the condition of random tides, this study investigated the southeastern Dongzhaigang area-the largest mangrove forest area in Hainan Province and obtained standard samples using the GF-2 satellite data. The reflectance spectral curves of typical surface features were constructed based on the standard samples and the GF-6 satellite data. Then, a baseline was established based on the bands strongly absorbed by vegetation, and the intertidal mangrove forest index (IMFI) applicable to the GF-6 satellite data was defined using the average reflectance of bands above the baseline. Meanwhile, the red-edge normalized difference vegetation index (RENDVI) was also established. The two indices were compared with commonly used indices, such as the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI), using box-whisker plots. Then, using the decision tree model constructed based on IMFI and RENDVI, information on typical mangrove forest in the study area were extracted. The precision of the extraction results was verified through comparison with visual interpretation results of the samples extracted from the GF-2 satellite data. The results show that: ① Because mangrove forests are periodically inundated by tidal water, the reflectance spectral curves of intertidal mangrove forests were relatively scattered between the standard spectral curves of water bodies and mangrove forests; ② IMFI and RENDVI can reflect the differences in the reflectance spectra of the red-edge and near-infrared bands and thus effectively separated the intertidal mangrove forests, mangrove forests, and water bodies; ③ The decision tree model constructed based on IMFI and RENDVI can effectively extract the distribution information of the mangrove forests, with an overall accuracy of 0.95 and a Kappa coefficient of 0.90. The introduction of the red-edge band plays an important role in the information extraction of mangrove forests and has great potential for application. This study can be used as a reference for the ecological applications of red-edge data from domestic satellites.