## 1 Introduction  
Natural disasters occur frequently in China due to its complex geographic environment. Typhoons and extreme precipitation are two kinds of primary natural disasters, particularly occurring in southeast China along the coast and middle and lower Yangtze River [1]. Flood disaster risks induced by flooding events have been causing increasing concerns due to the dense population and highly developed economies in these regions [2].  
The timely and accurate mapping of the areas most impacted by flooding events can reinforce the capacity building of emergency response teams when disasters occur. Satellite remote sensing is an effective approach to mapping flooded regions [3]. However, optical remote sensing (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat) does not work during flood events due to the influence of clouds and rain. Microwave remote sensing, in contrast, is not affected by severe weather. However, microwave observations are either at coarse spatial resolutions (10-70 km for passive sensors, e.g., Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Scanning Multi-channel Microwave Radiometer (SMMR) and Advanced MicrowaveScanning Radiometer - Earth Observing System (AMSR-E)) or temporal resolutions (6-16 days for Synthetic Aperture Radar (SAR), e.g., TerraSAR-X, Advanced Land Observing Satellite-2 (ALOS-2), and Sentinel-1 SAR).  
The Global Navigation Satellite System Reflectometry (GNSS-R) is a bistatic radar approach to infer surface properties through analyzing the L band-reflected GNSS signals over the Earth's surface. This technique is cost-effective since it uses operational GNSS satellites as microwave transmitters. Compared to monostatic radar, which measures backscattered signals, this technique is less sensitive to surface roughness since it measures the forward-scattered GNSS signals from the surface [4]. Since the launch of NASA's Cyclone GNSS (CYGNSS) mission in 2016, spaceborne GNSS-R has revealed its potential to observe ocean/land surfaces at a fine spatial (from a few kilometers to tens of kilometers) and temporal (from a few hours to 1-3 days) resolution [5; 6]. CYGNSS is a constellation consisting of eight small satellites carrying GNSS receivers specially designed to capture surface-reflected GNSS signals over the tropics to retrieve ocean surface wind speeds. To explore additional capabilities, previous studies have shown initial applications to monitor land surface hydrological components such as soil moisture [7; 8], lake ice thickness/water levels [9; 10], and wetlands [11]. The first attempt, described in [6], used CYGNSS data to map flood inundation during the 2017 Atlantic hurricane season.  
This paper presents the advantages and limitations of using CYGNSS data to monitor flood inundation during typhoons and extreme precipitation events of 2017 in southeast China. The results show the capability of CYGNSS for both monitoring flood inundation dynamics and acquiring flood inundation areas. The primary methodologies for processing CYGNSS data and SMOS/SMAP data in this study were similar to those used in [6; 12], and the results further consolidate the conclusions presented in [6; 12]. This work provides supporting evidence for the further use of GNSS-R constellations to monitor land surface hydrology.