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