## 4 Discussion  
### Comparison with Other Phenological-Based Studies  
Since the launch of the first Earth resource satellite, various studies have been proposed to identify rice cultivation using remote sensing data. So far, there have been dozens or even hundreds of studies on the application of various satellite data sources [3; 45; 46; 48; 52; 66], and various identification algorithms [3; 52; 54]. Currently, the phenological-based studies mainly focus on the application of optical satellite imagery for rice extraction, among that the MODIS data are used widely. The auxiliary rice products in this study are extracted by IRRI based on the MODIS data [14; 22]. Compared to the MODIS data with 250 m, Sentinel-1 data has a much higher spatial resolution of 10 m, which greatly reduces the impact of mixed pixels on rice extraction. Since the rice paddies area in our Mun River Basin is mainly composed of large paddy fields and other smaller non-rice land types (pathways, irrigation canals, \begin{table}
\begin{tabular}{c c c c c} \hline
**Class Value** & **Rice Paddy** & **Other Land Use/Land Cover** & **Total** & **User’s Accuracy (\%)** \\ \hline Rice paddy & 659 & 67 & 726 & 90.77 \\ Other land use/land cover & 63 & 451 & 514 & 87.74 \\ Total & 722 & 518 & 1240 & 100 \\ Producer’s accuracy (\%) & 91.27 & 87.07 & & \\ \hline Overall accuracy (\%) & & 89.52 & & \\ \hline F1-score & & 0.91 & & \\ \hline \end{tabular}
\end{table} 
Table 2: Rice classification accuracies of time-series Sentinel-1 data compared with field plot data. \begin{table}
\begin{tabular}{c c c c c} \hline
**Class Value** & **Rice Paddy** & **Other Land Use/Land Cover** & **Total** & **User’s Accuracy (\%)** \\ \hline Rice paddy & 542 & 133 & 675 & 80.30 \\ Other land use/land cover & 180 & 385 & 565 & 68.14 \\ Total & 722 & 518 & 1240 & \\ Producer’s accuracy (\%) & 75.07 & 74.32 & & \\ \hline Overall accuracy (\%) & & 74.76 & & \\ \hline F1-score (\%) & & 0.78 & & \\ \hline \end{tabular}
\end{table} 
Table 3: Rice classification accuracies of moderate resolution imaging spectroradiometer (MODIS) rice products from IRRI compared with field plot data. trees, and other vegetation patches). Therefore, most rice pixels may contain some small non-rice cover, which may cause an overestimation of the rice-cultivation area. Moreover, this overestimation will be more obvious at coarse resolutions of 250 m. Compared with the rice area acquired from IRRI using MODIS data, our classified rice results greatly reduced the overestimation of rice caused by mixed pixels and improved the accuracy of rice identification. With the popularization of Landsat data, the Landsat based time series data with the spatial resolution of 30 m and revisit cycle of 16 days has gradually been used to extract a rice paddy [6; 10; 18; 30; 41; 42]. Compared to the Landsat data, Sentinel-1 not only has a certain improvement in spatial resolution, but also has a greater advantage in temporal resolution with the revisit cycle of 6/12 days. In addition, during the growing period of rice, there is frequently cloudy, rainy weather, which seriously affects the usefulness of Landsat optical satellites, and severely may make it impossible to construct a time series data set. In contrast, SAR has the capability of penetrating clouds and fog, and is less affected by clouds and rain [15; 52; 54; 67]. Thus, Sentinel-1 can provide sufficient images to generate dense time-series imagery for rice identification in rainy study areas like the Mun River basin. It also has a similar advantage compared to the Sentinel-2 time-series images. In addition, there are also some phenological-based rice extraction studies based on time-series SAR data [40; 45; 54; 58; 64; 68; 69; 70].