Abstract: Jianghan Plain is an important commodity grain base in China, the acquisition of high-precision rice planting area is of great significance to the country’s agricultural development and planning. However, there are many cloud and rain weathers in southern China affected by climate, and the optical remote sensing images are seriously missing. At the same time, due to the satellite revisiting cycle, the available data is less, which affects the accuracy of rice planting area extraction. Obtaining high spatial-temporal resolution remote sensing images is the key to extracting rice growing areas in southern China. In order to solve the problem of high spatial-temporal resolution image loss, the fusion of Landsat 8 OLI and MODIS data based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) is used to obtain Landsat Normalized Difference Vegetation Index(NDVI) time series data with high spatial and temporal resolution. The ESTARFM model could improves the accuracy of heterogeneous landscape extraction for more heterogeneous and fragmented areas with high precision. In the existing crop area information extraction research, the classification relies on a single NDVI data, and the phenological feature information in the process of crop growth has not been fully utilized in the remote sensing classification structure. In this article, we use time NDVI series data to analyze the phenological characteristics of rice and combining key phenological parameters, so that a variety of machine learning methods could be used to extract rice planting areas, in this article, machine learning classification methods including Support Vector Machine(SVM), random forest and neural network were used to extract rice planting area and evaluate which method works best. The results show that this method can extract the rice planting area in the study area well, and the SVM method has the best classification effect. In the meanwhile, the overall classification accuracy of rice planting area extraction is 93.31%, and the Kappa coefficient is 0.920 2. This study provides an effective technical means for the extraction of rice planting area in the southern region, and providing technical support for regional land use planning and food policy.