Synthetic aperture radar (SAR) data has unique advantages for rice identification and monitoring in cloudy and rainy weather. Multi-temporal SAR and multi-features can provide rich information for rice extraction, but too many feature variables will cause dimension disaster and information redundancy to some extent. Therefore, a rice extraction method based on multi-temporal backscattering characteristics and coherent coefficient optimization features was proposed. Based on the multi-temporal Sentinel-1 SAR data during the rice growth cycle in the study area, the feature sets of backscattering coefficient and coherence coefficient were constructed, and the importance of the features was sorted by ReliefF algorithm. At the same time, JM distance was used to determine the optimal number of features to complete the optimal features selection. According to the optimal features, the rice planting area in the study area was extracted by the random forest classification algorithm. The results showed that the error of rice area extraction based on the optimal features was 4.96%, the overall accuracy planting was 92.48%, and the Kappa coefficient was 0.90. Excluding coherence coefficient features from the optimal features to extract rice, the area error was increased by 2.39 percentage points, and the overall classification accuracy and Kappa coefficient were decreased by 4.03 percentage points and 0.06, respectively, which showed that coherence coefficient was beneficial to rice information extraction. Based on the characteristics of multi-temporal backscattering and coherence coefficient, data redundancy was reduced, operation efficiency was improved, and large-scale and high-precision rice extraction can be realized.