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The extraction of rice planting area in countylevel depends on the medium and high spatial resolution images of the complete time series. However, it is often difficult to obtain the high spatial resolution images of the complete time series due to the cloud and rain weather and the satellites own visit cycle. Thus causing the problem of low precision in rice planting area based on extraction by single MODIS data. Taking Yuanyang County, an excellent rice planting area in Henan Province, as an example, an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was used to fuse mid and highresolution Landsat data and hightimeresolution MODIS data to obtain the normalized difference vegetation index (NDVI) data of the complete time series. After smoothing by TIMESAT filtering, the characteristics of time series NDVI curves of rice and other features in the study area were used to set reasonable NDVI thresholds. The decision tree classification method was used to extract the rice planting area. The results showed that the overall classification accuracy was 92.23% and the Kappa coefficient was 0.9043. The producer accuracy of rice extraction was 96.73% and the user accuracy was 93.51%, which indicated that the ESTARFM model can well integrate high spatial resolution images, solve the problem of missing data, and provide an effective reference for extracting rice planting area in a countylevel.