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• W1979972384 abstract "a b s t r a c t 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. There- fore, 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 Spectrora- diometer (MODIS) in 2013. The resultant rice cropping map was consistent with the agricultural census data (r 2 = 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% 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" @default.
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• W1979972384 date "2015-09-01" @default.
• W1979972384 modified "2023-10-17" @default.
• W1979972384 title "Mapping paddy rice areas based on vegetation phenology and surface moisture conditions" @default.
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• W1979972384 doi "https://doi.org/10.1016/j.ecolind.2015.03.039" @default.
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