## 4 Experimental Result and Discussion
### Permanent Water Mapping  
The permanent water surface will slightly change over the years due to human impacts on rivers, lakes and aquaculture areas. In order to obtain flood maps more accurately, we created a permanent water map for 2018 (Figure 8a). The permanent water surface was then excluded to get a temporal water map which might be caused by flooding.  
For different study areas, VV and VH data result in different efficiencies for water detection because of different environmental conditions such as windy and calm. Since both VV [61; 71] and VH [72; 73] are proved to be good at detecting water, we conducted a further experiment in the RRD region. We found that VV is better at detecting permanent water surface areas than both VH data and VV+VH data. Meanwhile, VH data is better in the detection of submerged rice areas. Figure 8b,c are the VV-derived and VH-derived permanent water maps, respectively. It shows that the water map derived from VV has a smoother surface than the one derived from VH data. Besides, VH data is not capable of detecting small canals.  
The permanent water validation results on the test data are shown in Tables 8 and 9. The water map has a fairly high accuracy of OA = 90.94% on the test data. However, the precision of the water  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Source** & **Year** & **Study Area** & **Data Type** & **Mapped Data (ha)** & **Statistical Data (ha)** & **Diff(ha)** & **Method** & **OA(\%)** \\ \hline \multirow{4}{*}{[29]} & 2013 & & & 550,100 & 594,524 & 44,424 & & 91.53 \\  & 2014 & & & 546,600 & 593,706 & 47,106 & & 90.74 \\  & 2015 & & & 542,100 & 580,361 & 38,216 & & 90.48 \\  & 2016 & & & 522,496 & 603,050 & 80,554 & & 89.42 \\  & 2016 & & & 216,784 & 235,700 & 18,916 & RF & 93.50 \\  & 2007–2011 & RRD excludes QN & SAR & 1,182,600 & 1,115,100 & 67,500 & Threshold-based & 83.90 \\
**Ours** & **2018** & **RRD** & **SAR** & **542,211** & **529,667** & **12,544** & **SVM** & **90.50** \\ \hline \hline \end{tabular}
\end{table}
Table 7: A comparison of rice area estimated from related studies in the RRD. QN, VP, HY, BN, HN1, HN2 are abbreviated for Quang Ninh, Vinh Phuc, Hung Yen, Bac Ninh, Ha Noi and Ha Nam, respectively.  
class (0.8) indicates that the water map has not yet detected certain water areas. By visual inspection, we observed that some small canals, located in metropolitan areas with curving footprints, were often misclassified because near-shore pixels might not be pure water pixels (Figure 9a). In some cases, these small canals could be in the dry season (Figure 9b) or could be covered by vegetation (Figure 9c,d). However, this does not often happen in the aquaculture areas, as they are usually distributed side by side. These canals typically have a flow width from 10 to 40 m, so pure water pixels in the center of the stream might be removed by the opening process in our morphological transformation step. Besides, the Otsu approach, which is sensitive to the global distribution of the target and background classes [74] and high spatial resolution of S1A data (i.e., 10 m), had created a misclassification of the permanent water map for the RRD in 2018. Similar issues have also been pointed out in previous studies [36; 39].  
Figure 8: The permanent water map estimated from VV data for the RRD in 2018 (a) and Specified scenes of permanent water derived from VV data (b) and VH data (c).