## Materials and Methods
### Calculation of the Flood Inundation Area  
The SR derived from CYGNSS is strongly linked to the hydrological conditions of the surface. Referring to the bistatic radar equation [15] and the methods proposed in [6], for each specular reflection point, the SR (in dB) was calculated from the SNR observation of the DDMs:  
\[\text{SR}\propto\text{SNR}-10\text{log}\text{P}_{\text{r}}^{\text{t}}-10\text{ log}\text{G}^{\text{t}}-10\text{log}\text{G}^{\text{r}}-20\text{log}\lambda+20\log( \text{R}_{\text{ts}}+\text{R}_{\text{sr}})+20\log(4\pi) \tag{1}\]  
where P\({}_{\text{r}}^{\text{t}}\) is the transmitted power; G\({}^{\text{t}}\) and G\({}^{\text{r}}\) are the gains of the transmitting antenna and the receiving antenna respectively; \(\lambda\) is the GPS wavelength (0.19 m); R\({}_{\text{ts}}\) is the distance between the transmitter and the specular reflection point; and R\({}_{\text{sr}}\) is the distance between the specular reflection point and the receiver.  
As shown in the CYGNSS metadata, the SNR (in dB) is defined as \(10\log(\text{S}_{\text{max}}/\text{N}_{\text{avg}})\), where S\({}_{\text{max}}\) is the maximum value (in raw counts, this means peak cross-correlation of the DDM) in a single DDM bin, and N\({}_{\text{avg}}\) is the average per-bin raw noise counts. The value of the SNR is related to surface dielectric constant at the specular reflection point of the GNSS signal, and the dielectric constant can be related to the moisture content of the surface. Since SNR is not equal in magnitude to the reflected power, the observations and corrections made in Equation (1) result in magnitudes greater than 100 dB. In this study, for the SR to be in a range that visually makes sense [6], 115 dB was subtracted from the original SR. To calculate the flood inundation areas for SR, a simple threshold was used to distinguish water from non-water areas. We collected observations over known permanent waterbodies within the study area and took the average of these observed values over all time to determine the final threshold (i.e., 17 dB). This threshold was different from that used in [6] (i.e., 12 dB). More tests considering environmental conditions (e.g., vegetation cover, and roughness) should be performed if this method is used to obtain flooding information.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Name** & **Landfall Time** & \multirow{2}{*}{**Landfall Location**} & **Maximum Wind** \\  & & & & **Speed When** \\  & & & & **Landing (m/s)** \\ \hline \multirow{8}{*}{**Typhoon**} & Luoke & Jul. 23 & Hongkang (22.4 N, 114.3 E) & 20 \\  & Nasha & Jul. 29 & Yilan, Taiwan (24.7 N, 121.8 E) & 40 \\  & Jul. 30 & Fuqing, Fujian (25.5 N, 119.6 E) & 33 \\ \cline{1-1}  & Haitang & Jul. 30 & Pingdong, Taiwan (22.5 N, 120.5 E) & 23 \\ \cline{1-1}  & Jul. 31 & Fuqing, Fujian (25.5 N, 119.6 E) & 18 \\ \cline{1-1}  & Tange & Aug. 23 & Zhuhai, Guangdong (22.0 N, 113.2 E) & 45 \\ \cline{1-1}  & Paka & Aug. 27 & Taishan, Guangdong (22.0 N, 113.5 E) & 33 \\ \cline{1-1}  & Mawa & Sep. 3 & Shanwei, Guangdong (22.9 N, 115.9 E) & 20 \\ \cline{1-1}
**Extreme precipitation** & Southeast China (20–31 N, 100-122 E), lasting for 11 days from Jun. 22 (DOY 173) to Jul. 2 (DOY 183) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The typhoon and extreme precipitation events of 2017 investigated in this study.  
To provide consistency with the time series analysis, the original SRs of the specular points were converted to gridded data. Three types of grids with appropriate spatial scales were applied, considering the number of data points within the corresponding study area and time period, i.e., 10 km \(\times\) 10 km for the SR of the entire typhoon/non-typhoon season over southeast China (Figure 1), 50 km \(\times\) 50 km for the daily SR of Fujian and Taiwan (Figure 2), and 30 km \(\times\) 30 km for the daily SR of the Poyang Lake basin (Figure 3). The data value per grid cell was determined as the average value of the specular points that dropped into the grid.