Rice-fish co-culture， as a model of modern ecological cycle agricultural， with significant social， economic， and ecological benefits on ensuring stable food production， reducing pollution， improving soil fertility， and lowering CH₄ emissions. Therefore， obtaining information on distribution and area of rice-fish fields by using remote sensing technology， is helpful in enhancing the level of agricultural digital management and improving the efficiency of resource utilization efficiency. In this study， we selected the typical rice-crayfish model in the Chengdu Plain for remote sensing identification. First， the time-series data of Sentinel-1 VH polarization backscatter coefficients from 2019~2021 were collected and preprocessed in the Google Earth Engine， to reduce the noise of SAR time-series data. Then the time-series characteristics of typical ground objects were analyzed， including rice-crayfish fields， paddy fields， lotus root fields， orchards， traditional aquaculture， etc， and the characteristic parameters statistical of the backscatter coefficients time-series were statistically analyzed. Finally， the information of rice- crayfish fields， rice fields and lotus root fields were extracted by the classification method of random forest. The results showed that the backscattering coefficients of rice-crayfish fields exhibited typical time-series variation characteristics. Specifically， the annual variation trend of backscattering coefficients began with a smooth transition at low value， then increased rapidly， and finally decreased sharply to low value， due to the state of rice-crayfish fields changed from water body to vegetation and then back to water body. Moreover， the range of coefficient variation and the time of curve peak were significantly different from paddy fields and lotus root fields， respectively. The overall accuracy and Kappa coefficient based on random forest classification were 94.32% and 0.91， respectively. This suggested that time-series data of Sentinel-1 can effectively identify rice-crayfish fields in cloudy regions. The results can provide a reference for remote sensing identification of rice-crayfish fields in cloudy areas.