# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 3 Results
### Virtual Experiment of Flood Monitoring  
The virtual experiment of flood monitoring using a SAR small-satellite constellation was executed in the two flood event case studies. Because flood simulation outputs also included the permanent water area in the river channel, we masked this area in advance. Figure 4 presents the results of the virtual observation of flood extent with each constellation (\(T\) = 8, 12, 20, 24, 28, 40, and 56) in case 1 (heavy rainfall in the Kyushu region from 14:00 on July 12 to 14:00 on 13 July 2012). In this figure, the simulated flood extent at the time of observation is represented by a blue line, and the observed simulated flood extent is represented in a multi-colored bar graph. This multi-color graph expresses the proportion of flood extent that was observed for the first time (1 st), second time (2 nd), third time (3 rd), fourth time (4 th), fifth time (5 th), and sixth time and above (>6 th) after the flood occurred at 14:00 on 12 July 2012. In other words, these components express how much new information (1 st) and overlapped information (2 nd to >6 th) regarding flood extent are acquired at each observation with respect to the past observations.  
Here, we also introduced the damage extent and the observed damage extent. Damage extent was defined as the area that had experienced flooding up to each time in this event, so it was assumed to have already been damaged. In other words, the damage extent is the cumulative flood extent, including the area where flood water has already receded. Observed damage extent denotes how much damage extent area was acquired as a result of multiple observations until each time. This is calculated as the summation of the '1 st' first area until each time because it gives the cumulative observed flood extent with no redundancy. Comparison of the two dotted lines of damage extent and observed damage extent demonstrates the extent to which the observation system can trace the real situation on a spatial and temporal scale. Figure 5 presents the results for case 2 (Typhoon No. 19 from 13:00 on 12 October 2019 to 11:00 on 13 October 2019). Here, the definition of legends is the same as that in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(T\) & \(P\) & **(F)** & **Revisit Rates (/day)** & \(P\) & **(F)** & **Mean Revisit Time** & \(P\) & **(F)** & **Maximum Revisit Time** \\ \hline
8 & 2 & (1) & 3.47 & 2 & (1) & 6 h 44 min & 4 & (2) & 21 h 16 min \\
12 & 1 & (-) & 5.22 & 1 & (-) & 4 h 30 min & 6 & (2) & 10 h 25 min \\
20 & 5 & (4) & 8.68 & 5 & (4) & 2 h 43 min & 10 & (7) & 6 h 11 min \\
24 & 8 & (7) & 9.98 & 2 & (1) & 2 h 16 min & 12 & (7) & 5 h 23 min \\
28 & 7 & (2) & 12.16 & 7 & (2) & 1 h 58 min & 4 & (3) & 4 h 11 min \\
40 & 10 & (9) & 17.37 & 10 & (9) & 1 h 22 min & 20 & (1) & 3 h 32 min \\
56 & 28 & (10) & 23.38 & 28 & (10) & 1 h 1 min & 8 & (1) & 2 h 32 min \\ \hline \end{tabular}
\end{table}
Table 2: The best parameter setting of \(P\) and \(F\), and the corresponding results of the mean revisit rates, mean revisit time, and maximum revisit time for each \(T\).  
Figure 4: The virtual observation of the simulated flood extent with constellations of (**a**) \(T=8\), \(P=4\); (**b**) \(T=8\), \(P=2\); (**c**) \(T=12\), \(P=6\); (**d**) \(T=12\), \(P=1\); (**e**) \(T=20\), \(P=10\); (**f**) \(T=20\), \(P=5\); (**g**) \(T=24\), \(P=12\); (**h**) \(T=24\), \(P=8\); (**i**) \(T=28\), \(P=4\); (**j**) \(T=28\), \(P=7\); (**k**) \(T=40\), \(P=20\); (**l**) \(T=40\), \(P=10\); (**m**) \(T=56\), \(P=8\); (**n**) \(T=56\), \(P=28\) in case 1 (heavy rainfall in the Kyushu region from 14:00 on 12 July 2012 to 14:00 on 13 July 2012).  
Figure 5: _Cont._