Accurate　and　timely　rice　mapping　is　important　for　food　security　and　environmental　sustainability.　We　developed　a　novel　approach　for　rice　mapping　through　Combined　Consideration　of　Vegetation　phenology　and　Surface　water　variations　(CCVS).　Variation　of　the　Land　Surface　Water　Index　(LSWI)　in　rice　fields　was　relatively　smaller　than　that　in　other　crops　fields　during　the　period　from　tillering　to　heading　dates.　Therefore,　the　ratios　of　change　amplitude　of　LSWI　to　2-band　Enhanced　Vegetation　Index　2　(EVI2)　during　that　period　were　utilized　as　the　primary　metric　for　paddy　rice　mapping.　This　algorithm　was　applied　to　map　paddy　rice　fields　in　southern　China　using　an　8-day　composite　Moderate　Resolution　Imaging　Spectroradiometer　(MODIS)　in　2013.　The　resultant　rice　cropping　map　was　consistent　with　the　agricultural　census　data　(r2　=　0.8258)　and　ground　truth　observations　(overall　accuracy　=　93.4%).　Validation　with　Landsat　Thematic　Mapper　images　in　test　regions　also　revealed　its　high　accuracy　(with　overall　accuracy　of　94.3%　and　kappa　coefficient　of　0.86).　The　proposed　CCVS　method　was　more　robust　to　intra-class　variability　and　other　related　uncertainties　compared　with　other　related　methods　in　rice　mapping.　Its　successful　application　in　southern　China　revealed　its　efficiency　and　great　potential　for　further　utilization.　©　2015　Elsevier　Ltd.　All　rights　reserved.