In order to improve the accuracy of winter wheat identification, the difference between radar and optical remote sensing data on winter wheat area extraction was compared and analyzed based on Google Earth Engine (GEE) platform and random forest algorithm. The importance analysis of multiple feature variables was performed to study the influence of feature optimization on the accuracy of winter wheat extraction. The Sentinel-1 and Sentinel-2 images during the main growth period of winter wheat (from March 1 to May 31, 2019) were chosen as the data sources. The polarization and texture features of Sentinel-1 data as well as the spectral, vegetation index and vegetation index change rate features of Sentinel-2 data were constructed. Six winter wheat identification schemes were constructed based on different remote sensing data sources and feature combinations, and the accuracies of the schemes were compared and analyzed. Then the feature variables were optimized and the optimal feature combination was obtained to extract the planting area of winter wheat in Zhumadian City, Henan Province. The results showed that regardless of feature optimization, the results of winter wheat area extraction based on multi-source remote sensing data were superior to those by using only optical or radar data. After feature optimization, the classification accuracy of each scheme was further improved, indicating that both the combination of multi-source feature variables and feature optimization can improve the winter wheat identification accuracy. In addition, the feature variables of different months and types had different contribution rates to classification accuracy, and the months with contribution rates from high to low were April, March and May. The feature types with contribution rates from high to low were polarization, vegetation index change rate, vegetation index, spectral features and texture. The accuracy of winter wheat extraction in Zhumadian based on both multi-source satellite data and feature optimization were the best, with the overall accuracy of 95.60% and Kappa coefficient of 0.93. The relative error between the extracted area of winter wheat and official statistical data was 2.23%. The research result can provide an important theoretical reference for crop planting area extraction based on multi-source optical and radar remote sensing images.