Mapping detailed wetland types can offer useful information for wetland management and protection, which can strongly support the Global Biodiversity Framework. Many studies have conducted wetland classification at regional, national, and global scale, whereas fine-resolution wetland mapping with detailed wetland types is still challenging. To address this issue, we developed an integration of pixel- and object-based algorithms with knowledge (POK) by combining pixel-based random forest and an object-based hierarchical decision tree. Taking the Guangxi Beibu Gulf Economic Zone (GBGEZ) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as our study areas, we produced wetland maps with 10 wetland types and 6 non-wetland types using Landsat-8 time series. In addition, to comprehensively evaluate the accuracy of our wetland classification, we implemented accuracy validation based on test samples and data inter-comparison based on existing datasets, respectively. The results indicate that the overall accuracy of our wetland map was 91.6% +/- 1.2%. For wetland types, agricultural pond, coastal shallow water, floodplain, mangrove, reservoir, river, and tidal flat achieved good accuracies, with both user accuracy and producer accuracy exceeding 88.0%. For non-wetland types, most accuracies were greater than 72.0%. By comparison with existing datasets, it was found that our wetland map had good consistencies with the China Ecosystem-type Classification Dataset (CECD) land use dataset, MC_LASAC mangrove dataset, and Tidal Wetlands in East Asia (TWEA) tidal flat dataset. In 2020, the wetland area was 4,198.8 km2 in the GBGEZ and 10,932.2 km2 in the GBA. The main wetland types in the two coastal urban agglomerations were agricultural ponds, coastal shallow waters, mangroves, reservoirs, rivers, and tidal flats. Our study successfully mapped detailed wetland types in the GBGEZ and GBA, serving the Global Biodiversity Framework of Convention on Biological Diversity.
