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Mangrove forest extents and distributions are fundamental for conservation and restoration efforts. According to previous studies, both the commercial Gaofen-2 (GF-2) imagery (0.8 m spatial resolution and 4 spectral bands) and freely accessed Sentinel-2 (S2) imagery (10 m spatial resolution and 13 spectral bands) have been successfully used to map mangrove forests. However, the efficiency and accuracy of mangrove forest mapping based on these two data is not clear, especially for large-scale applications. To address this issue, firstly, we developed a robust classification approach by integrating object-based image analysis (OBIA) and random forest (RF) algorithm; and then, applied this approach to GF-2 and S2 images to map the extents of mangrove forest along the entire coasts of Guangxi, China, respectively; at last, compared the efficiency and accuracy of GF-2 and S2 imagery in mangrove forest mapping. Results showed that: (1) based on OBIA and RF integrated classification approach both mangrove forest maps derived from GF-2 and S2 obtained high mapping accuracies (the overall accuracy was 96% and 94%, respectively); (2) areal extent of mangrove forests in Guangxi extracted from GF-2 and S2 images was 8182 ha and 8040 ha, respectively; (3) GF-2 imagery has extraordinary abilities in detecting fragmented mangrove forest patches located along landward and seaward edges; (4) S2 imagery performed better in detecting seaward submerged mangrove forests and separating mangrove forest from terrestrial vegetation. Results and conclusions of this study can provide basic considerations for selecting appropriate data source in mangrove forest or wetland vegetation mapping tasks.