Information of paddy rice fields and their spatial distribution is the data source for land use and land cover changes

crop yield estimates

methane emission estimates

food security

and water resources management.A number of studies have explored the potential of remote sensing images to identify paddy rice fields.Those studies that identified paddy rice fields used image classification procedures for TM images and temporal development of the NDVI for AVHRR images.8-day composite MODIS（Moderate Resolution Imaging Spectroradiometer） Surface Reflection products（MOD09A1） provide the potential for the improved characterization of vegetation at large spatial scale.According to the different paddy rice growth stages

multitemporal MODIS images were selected by the flooding and transplanting period

growing period

maturation period

and the fallow period after harvest.Three vegetation indices

Normalized Difference Vegetation Index（NDVI）

Enhanced Vegetation Index（EVI）

and Land Surface Water Index（LSWI） using the shortwave infrared band sensitive to soil moisture and leaf moisture content

were calculated to extract information of paddy rice fields.We compared the paddy rice fields dataset to the dataset extracted from ETM+ images and produced at 40-km gridded data

the results show that the paddy rice extracting algorithm of time-series MODIS data could be applied to monitor paddy rice fields at large spatial scales.