River　system　is　critical　for　the　future　sustainability　of　our　planet　but　is　always　under　the　pressure　of　food,　water　and　energy　demands.　Recent　advances　in　machine　learning　bring　a　great　potential　for　automatic　river　mapping　using　satellite　imagery.　Surface　river　mapping　can　provide　accurate　and　timely　water　extent　information　that　is　highly　valuable　for　solid　policy　and　management　decisions.　However,　accurate　large-scale　river　mapping　remains　challenging　given　limited　labels,　spatial　heterogeneity　and　noise　in　satellite　imagery　(e.g.,　clouds　and　aerosols).　In　this　paper,　we　propose　a　new　multi-source　data-driven　method　for　large-scale　river　mapping　by　combining　multi-spectral　imagery　and　synthetic　aperture　radar　data.　In　particular,　we　build　a　multi-source　data　segmentation　model,　which　uses　contrastive　learning　to　extract　the　common　information　between　multiple　data　sources　while　also　preserving　distinct　knowledge　from　each　data　source.　Moreover,　we　create　the　first　large-scale　multi-source　river　imagery　dataset　based　on　Sentinel-1　and　Sentinel-2　satellite　data,　along　with　1013　handmade　accurate　river　segmentation　mask　(which　will　be　released　to　the　public).　In　this　dataset,　our　method　has　been　shown　to　produce　superior　performance　(F1-score　is　91.53%)　over　multiple　state-of-the-art　segmentation　algorithms.　We　also　demonstrate　the　effectiveness　of　the　proposed　contrastive　learning　model　in　mapping　river　extent　when　we　have　limited　and　noisy　data.