## Materials and Methods  
### Data  
There were eight typhoon events and two extreme precipitation events in China during 2017 [1]. Since most typhoon events in southeast China occur from July to September, in this study we defined a typhoon season as ranging from the first typhoon event in July to the last typhoon event in September, i.e., Days of Year (DOYs) 202-253. There were six typhoon events during the defined typhoon season (Table 1 and Figure 11), which are analyzed in Section 3.1. Note that two typhoon events, Nasha and Haitang, occurred successively and were studied further as typical cases for monitoring the flood inundation dynamics in Section 3.2. Two extreme precipitation events also occurred in southeast China, i.e., one from 22 June-2 July, and the other from 11 August-14 August 2017. Considering the spatial coverage of the CYGNSS data, in this study we only analyzed the first event, see Section 3.3. The Poyang Lake basin, located in the middle of the worst-hit area of this event, was studied as a typical case for comparisons between flood inundation derived from CYGNSS data and that from other data sources.  
The CYGNSS data used in this study were from level 1, version 2.0, available via [[https://podaac.jpl.nasa.gov/](https://podaac.jpl.nasa.gov/)]([https://podaac.jpl.nasa.gov/](https://podaac.jpl.nasa.gov/)). Delay-Doppler maps (DDMs) were the main observable in the data. DDMs are created by cross-correlating the received surface-reflected signal with a locally generated replica for different path delays (resulting from the path distance between the transmitter, reflecting surface, and receiver) and Doppler shifts (resulting from the relative motions of the transmitter, reflecting surface, and receiver) [13]. It is defined as the power \(\mathrm{P}(\tau,\mathrm{f})\) of the correlator output as a function of the applied delay \(\tau\) and Doppler \(\mathrm{f}\) offsets with respect to reference [14]. The CYGNSS DDMs are made of 17 delay pixels by 11 Doppler pixels, with a Doppler resolution of 500 Hz and a delay resolution of 249.4 ns. Areas with a surface elevation higher than 600 m above sea level were masked out due to a limitation in the CYGNSS on-board data compression algorithm [5]. The data were then filtered for (1) a signal-to-noise ratio (SNR) of the peak DDM value greater than 5 dB (since there have been uncertainties reported in the measured antenna gain patterns that increase with increasing distance from the peak of the main lobe [13]); and (2) the elevation angle of the specular points higher than 30\({}^{\circ}\) (to prevent multipath effects).  
To qualitatively compare to the CYGNSS-derived surface reflectivity (SR), which is the main method of monitoring flood inundation, in this study, the Integrated Multi-satelliE Retrievals for Global Precipitation Measurement (GPM IMRG) precipitation products (Level 3, daily, 10 km, downloaded from [[https://pmm.nasa.gov/](https://pmm.nasa.gov/)]([https://pmm.nasa.gov/](https://pmm.nasa.gov/))) were used. To compare with the flood inundation area calculated from the CYGNSS SR, two types of brightness temperature (T\({}_{\text{b}}\)) data, i.e., SMOS (Level 1C, \(\sim\)1.4 GHz), and SMAP (Level 2, \(\sim\)1.4 GHz) were used. All the T\({}_{\text{b}}\) data were downloaded from the NASA National Snow and Ice Data Center (NSIDC). The 2015 land cover/use classification data of China with 1 km resolution was derived from [[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)). The NASA Shuttle Radar Topographic Mission (SRTM) 90 m Digital Elevation Models (DEM) Database v4.1 were used to calculate the elevation of the CYGNSS covered areas.