## 1 Introduction
### Floodwater Delineation Approaches and Possible Limitations  
Remote sensing datasets complement in situ observations and modeling when investigating large-scale processes regarding the monitoring of hazards, specific disasters, and post-disaster response and recovery activities because of the wide spatial coverage and routine data acquisition capability [24; 25]. Historically, datasets acquired by optical sensors, such as from Landsat and other coarse- to medium-resolution Earth observation missions, have been widely used for numerous natural hazard and disaster management and response studies, including flooding from hurricanes and cyclones [26; 27; 28; 29; 30]. Floodwater delineation studies utilizing datasets from optical sensors are mostly based on one of the three main techniques and approaches. The first, a fairly simple approach, is to discern flooded pixels through visual interpretation of multiband composite images [31]. The second and most widely utilized method is image classification algorithms based on the spectral properties of pixels in images retrieved by a single or combination of sensors or platforms (e.g., [32]). Classification of fused imagery from sensors of varying temporal and spatial resolution, such as Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS), have also been used to map the impact of flood disasters from extreme precipitation [33]. Third, the analysis and interpretation of the derived indices from the multispectral satellite data, such as that of the Normalized Difference Water Index (NDWI), is another technique that is being used to detect inundations from floodwaters following extreme weather conditions [34]. In all the above-stated studies, the prevalence of cloud conditions has been cited as a principal limitation for using such datasets to monitor the impacts during and following severe weather events. Heavy rainfall events include cloud cover as the rain falls and often lingering cloud cover post-event that obscures optical remote sensing. Night-time conditions lack the illumination necessary for land and water mapping using optical sensors and, hence, the effectiveness of these sensors during the hazard event and immediately following under such conditions is partly limited. Synthetic Aperture Radar (SAR) remote sensing complements optical remote sensing approaches in natural hazard and disaster-related studies through active acquisition of Earth observations at day and at night and under widely varying weather conditions [35]. SAR sensors emit electromagnetic pulses and receive the signal returned from the surface, termed as backscatter, in the form of amplitude and phase. The amplitude component of the backscattered energy is a measure of the strength of the reflectance or reflectivity of the surface, while the phase is proportional to the distance between the sensor antenna and the ground target. The SAR backscatter signal from a target surface is dependent on several factors. These can be broadly classified as radar system properties (wavelength, polarization, etc.) and target surface properties (e.g., dielectric constant, the geometry of the target, and surface roughness) [36; 37; 38; 39]. The launch of the European Space Agency (ESA) Sentinel-1 SAR satellite constellation has broadened disaster monitoring and response studies through the acquisition and free, open access distribution of high-resolution temporal (6-12 days) and spatial (5 m range \(\times\) 20 m azimuth) wide-swath imagery of the Earth's surface [40; 41]. Sentinel-1 SAR missions operate in the C-band wavelength (5.6 cm wavelength and 5.4 GHz frequency) and provide dual polarimetric data for a wide range of applications [42; 43]. Figure 1: _Cont._  
Several studies have relied on the analysis and interpretations of individual SAR datasets to assess the impacts of floods during and following extreme weather events.