Abstract: Sentinel-2 time series remote sensing images from March to October 2019 in the southern rice planting area of Wuhu County, Wuhu City, Anhui Province were used, and the rice planting information was extracted by using the support vector machine method, the maximum likelihood method based on pixel classification and the classification method based on the combination of 3 Normalized Difference Vegetation Index （NDVI）, Ratio Vegetation Index （RVI） and Normalized Difference Greenness Index （NDGI）. The results showed that sentinel-2 remote sensing image could quickly and effectively extract the rice planting information in the study area, and the maximum likelihood method was more suitable for extracting rice information than the support vector machine method. The use of multi temporal image data and related vegetation index could significantly improve the accuracy of rice information extraction. The overall accuracy of the best combination of rice was as high as 95.5%, and the kappa coefficient was 0.922, which could be used as an effective supplementary method for rice resources investigation.

Key words: Sentinel-2, support vector machine classification, maximum likelihood classification, time series, vegetation index