Mapping paddy rice areas based on vegetation phenology and surface moisture conditions

•Combined Consideration of Vegetation phenology and Surface water variations.•Dynamic relationship of LSWI and EVI2 during vegetation growing period.•Establishing decision rules before grouping or clustering.•Robust to intra-class variability and other related uncertainties.•Efficiency in dealing with data noise, rapid VI decrease when harvested and rainfall events. 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.