Global　river　monitoring　is　an　important　mission　within　the　remote　sensing　society.　One　of　the　main　challenges　faced　by　this　mission　is　generating　an　accurate　water　mask　from　remote　sensing　images　(RSI)　of　rivers　(RSIR),　especially　on　a　global　scale　with　various　river　features.　Aiming　at　better　water　area　classification　using　semantic　information,　this　paper　presents　a　segmentation　method　for　global　river　monitoring　based　on　semantic　clustering　and　semantic　fusion.　Firstly,　an　encoder-decoder　network　(AEN)-based　architecture　is　proposed　to　obtain　the　semantic　features　from　RSIR.　Secondly,　a　clustering-based　semantic　fusion　method　is　proposed　to　divide　semantic　features　of　RSIR　into　groups　and　train　convolutional　neural　networks　(CNN)　models　corresponding　to　each　group　using　data　augmentation　and　semi-supervised　learning.　Thirdly,　a　semantic　distance-based　segmentation　fusion　method　is　proposed　for　fusing　the　CNN　models　result　into　final　segmentation　mask.　We　built　a　global　river　dataset　that　contains　multiple　river　segments　from　each　continent　of　the　world　based　on　Sentinel-2　satellite　imagery.　The　result　shows　that　the　F1-score　of　the　proposed　segmentation　method　is　93.32%,　which　outperforms　several　state-of-the-art　algorithms,　and　demonstrates　that　grouping　semantic　information　helps　better　segment　the　RSIR　in　global　scale.