###### Abstract
Common indices include the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface water index (LSWI), and the enhanced vegetation index (EVI). The indices are often used with multispectral data with medium-low spatial resolution, e.g., SPOT vegetation (VGT), or moderate resolution imaging spectroradiometer (MODIS). Due to the large number of mixed pixels, however, additional high-resolution imagery is needed for estimating the fractional coverage of rice paddies in each mixed pixel, and the overall classification accuracies are typically not high [3; 38; 39; 40]. Some recent research has also used time-series remote sensed vegetation index products from Landsat or Chinese HJ-1 satellites with a high spatial resolution of 30 m [6; 10; 18; 30; 41; 42]. Although most of these studies provide effective performance for revealing the spatial heterogeneity of rice paddies, they also have limitation. Since most of the available imagery with a spatial resolution of 30-1000 m, it is difficult to identify the smaller rice paddy areas with a spatial resolution of less than 30 m, which may be the dominant planting pattern and size in agriculturally developing countries [43]. The Sentinel-2 A/B optical satellites launched by European Space Agency (ESA) in 2015 and 2017, respectively, can provide the imagery with a higher resolution of 10-20 m, which facilitates the identification of very small rice paddies. It may be difficult to separate rice from other crops or vegetation types in humid tropical and subtropical areas. This is because of substantial unfavorable cloudy or rainy weather, which inevitably leads to unusable imagery in the series, constraining the application of optical time series images [19; 44; 45]. Thus, mapping rice paddies with a higher spatial resolution, short revisit time, wide coverage area, and all-weather capabilities is urgently needed. Contrary to the optical satellites' sensors, Synthetic aperture radar (SAR) is free from the influence of weather conditions. Moreover, SAR data has the special backscattering characteristics to the inundated vegetation, which can distinguish rice paddies from other crops or vegetation types, so it can be used to identify the rice fields [40; 46; 47]. Thus, the availability of SAR data in multiple frequencies and polarizations with frequent observations offers an important alternative for rice mapping. However, only a few SAR datasets (ERS-1/2, JERS-1, RADARSAT-1/2, ENVISAT advanced synthetic aperture radar (ASAR), TerraSAR-X, TanDEM-X, and ALOS Phased Arraytype L-band SAR (PALSAR)) could be obtained in the past decades for capturing dynamic water signals for rice mapping. Furthermore, the cost of using radar datasets is too high, which leads to the limitation of large-scale practical applications [19; 23; 40; 45; 46; 48; 49; 50; 51]. With the appearance of free data from Sentinel-1, it has become easier to use SAR datasets for the research of rice paddies identification [19; 41; 44; 52; 53; 54]. Similar to optical remote sensing classification, there are two main strategies for rice paddy mapping based on SAR data. The first strategies in classification algorithms for rice detection were exploitation of backscatter properties, the polarization ratio, or polarimetric decompositions (e.g., volume scattering, double-bounce scattering, anisotropy (A), entropy (H), alpha or beta angle (a or b), amplitudes, and relative phase) [40; 46; 49; 50; 55; 56]. However, these methods commonly require local prior knowledge of rice paddies at local scales. The second strategy mainly relies on a time-series analysis of SAR backscatter to capture the dynamic water signals and gradually increasingly backscattering characteristics during the rice growth process [45; 48; 50; 53; 57; 58].