Due to the abilities of all-time and all-weather data acquisition, the microwave remote sensing holds great potentials to identify rice in regions dominated by cloudy and rainy weather. The aim of this study was to analyze the consistency of classification results for single and double cropping rice by using optical and SAR remote sensing data, and then to explore the optimal SAR imagery features for rice classification.

In this study, using the object-based random forest classifier on the Google Earth Engine platform, Sentinel-1 and Sentinel-2 images were adopted to extract the single and double cropping rice from four typical rice growing areas in the Dongting Lake Plain. To analyze the optimal SAR features for the single and double cropping rice identification and the consistency of classification results based on Sentinel-1 and Sentinel-2 images, nine scenarios were established by the combination of different sensors and features and compared the performances of different scenarios. Furthermore, the R² and DTW distance between the NDVI time series and the SAR backscatter coefficient time series (VH, VH/VV) were calculated, respectively.

The overall accuracy of single and double rice cropping identification by using VH, VV and VH/VV time series was 90.42%, 82.08% and 88.33%, respectively. Moreover, the combination of VH and VH/VV time series could achieve a better performance (91.67%) for mapping single and double cropping rice. The derived R² and DTW distance between VH (VH/VV, VV) time series and NDVI time series were 0.870 (0.915, 0.986) and 4.715 (1.896, 5.506) for single cropping rice, as well as 0.597 (0.783, 0.673) and 2.396 (1.839, 3.441) for double cropping rice, respectively. Higher R² and lower DTW distance suggested that VH/VV time series, like NDVI, was more sensitive to the rice growth cycle. Furthermore, the flooding signals in rice transplanting phase could be well captured by VH time series. Additionally, the overall accuracy of single and double cropping rice classification based on optical and SAR features (S-2: NDVI, EVI, LSWI; S-1: VH, VH/VV) in six time windows was 91.25% and 90.00%, respectively, and their consistency was high, with the area correlation of 95.70%.

There was high consistency of classification results for single and double cropping rice based on optical and SAR imagery. Thus, Sentinel-1 imagery held great potentials to identify rice area in cloudy and rainy regions. Specifically, VH and VH/VV backscatter coefficient were optimal features for mapping rice. This study provided vital technical supports for feature optimization by using SAR imagery in cloudy and rainy regions to identify single and double cropping rice accurately.