Abstract: The monitoring of rice distribution and area can provide scientific decision-making basis for rice yield estimation, agricultural water resource consumption and evaluation. Till now, there are few researches on rice recognition in single-cropping rice region of North China, so it has great value to find a recognition method suitable for northern rice growing areas. With Tianjin as the research area, Sentinel-1 and Sentinel-2 as the data sources, rice was extracted from the study area based on the temporal variation characteristics of rice backscattering coefficient and spectral characteristics of rice at different growth stages, and the extraction accuracy of both methods was compared. The results show that:(1) The combination of Sentinel-1 images at transplanting, jointing and heading stages can be used to identify rice, and the accuracy of rice producers and users are both above 90%; (2) At the transplanting and maturing stages of rice, the combination of Sentinel-2 near infrared, short wave infrared and visible red light bands could easyly identify rice, and the accuracy of identifying rice producers and users was above 96%. The combination effect of B12+B8+B4 band was the best at the maturing stage; (3) Based on the combination of Sentinel-2 B12+B8+B4 band at rice maturity stage, rice extraction using support vector machine method (SVM) is a suitable method for single-cropping rice recognition in North China.. Using this method, the rice planting area in 2016, 2018 and 2021 was about 399.04, 586.67 and 764.55 km², respectively, with an increase of 365.51 km² in five years, which was consistent with the actual situation of Tianjin. This method is simple and feasible in technique and can provide reference for improving the efficiency and accuracy of rice monitoring in northern China's rice growing areas.

Key words: Sentinel, SAR, optical image, Tianjin, rice, threshold, maximum likelihood, SVM