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Remote sensing technology had become an effective method to extract planting area of bulk crop. With the aim to avoid the lack of optical data in winter wheat extraction, the validity of time series Sentinel-1 synthetic aperture radar（SAR）backscattering coefficients on winter wheat identification was explored based on random forest（RF）and Google Earth Engine（GEE）cloud platform. And Sentinel-1 and 2 active and passive remote sensing data was integrated to explore the improvement of winter wheat identification accuracy on combining various features groups of backscattering coefficients, spectral features, vegetation index features and texture features. The result indicated that the overall classification accuracy of the monthly average multi-temporal Sentinel-1 SAR polarization data was 85.93%, the Kappa coefficient was 0.75 and the winter wheat identification accuracy was above 95%. By integrating the monthly average time serious multi-temporal SAR data and the single-temporal optical data, the overall classification accuracy was 95.78% and the Kappa coefficient were 0.92. Integrating data fully used the polarization and spectral information and the overall classification accuracy and the Kappa coefficient were improved by 9.85 percentage points and 22.67%. The identification accuracy of winter wheat was improved by about 2 percentage points. The identification accuracy of winter wheat was affected by less than 0.9% by analyzing the influence of texture features under different features combinations. Therefore, the method and platform used accurately and efficiently obtained winter wheat planting area and it had a good promotion value.