## 1 Introduction  
Remote sensing has been used in studying floods for more than 40 years [1], where data from air- and space-borne optical, thermal and microwave sensors [2] are exploited to support the different stages of flood risk management [3]. Each of the sensors categories has advantages and disadvantages, and thus the selection of the most appropriate type of devices and data for the monitoring of the specific events can be not easy. Space-borne data are widely used for flood monitoring providing different spatial and temporal resolutions. Satellite optical images, although the most straightforward to interpret, are usually not available during floods because of the cloud cover during these events [4]. Synthetic aperture radar (SAR) techniques on the other hand overcome this weather limitation, and also provide day/night visibility [5], but are challenged to detect inundations in urban areas [6] and under vegetation [7]. The latter is overcome with passive microwave sensors in low microwave frequency which have better penetration through dense vegetation [8]. GNSS-R falls in the latter category of passive low frequency radars, and had proven successful in detecting wetlands during floods (e.g., [9]). In general, space-borne remote sensing data for flood monitoring have the disadvantage of long revisit time (>2.5 days) [10] and thus they do not provide enough repeating visits needed during these events, and in worst cases they even miss the peak of the event [11] like in flash flooding (for example [3]). Also, the satellites with short revisit times have low resolution (in kilometers), which make them not sensitive enough to detect the details needed for floods [12]. Moreover, even the meter level space-borne resolution sensors are challenged in delineating inundation in headwater regions which require sub-meter image resolution [7]. From a general perspective, aircraft flights are able to overcome the weather limitations of optical sensors and the resolution and revisit limitation of space-borne remote sensing, but they are expensive and sometimes, due to the need of runways and the difficult topology of the areas, they are not feasible [13]. Moreover, they are not able to provide the temporal resolution provided today by space-borne sensors that scan the earth continuously. In fact, today hybrid data from different space- and air-borne sensors are fused together to complement each other and provide the different aspects of global flood monitoring, local detailed inundation maps, temporal data availability and quick data availability of floods [14; 15]. From this point on, this paper will focus on GNSS-R sensors. For comprehensive literature on floods remote sensing using the various platforms and sensors refer to [1; 2; 3; 5; 7; 10]. The processing of GNSS signals that are back-scattered from the ground allows for using GNSS-R in monitoring the parameters of the Earth's surface. It is a passive radar where all the GNSS satellites are the transmitters, and any sensor capable of processing their reflected GNSS signals is the receiver [16]. GNSS-R advantages include, beside penetration of vegetation cover and resilience to clouds and smoke, the frequent global coverage of the transmitters from multiple constellations which avoids the limitation of current active remote sensing techniques that rely on a single transmitting satellite [17]. Also, the low cost of the receivers and the small size of the antennas regardless of the targeted resolution, makes GNSS-R an interesting sensing technique. A wide range of applications has been reported in the literature for GNSS-R including: water basins detection (e.g., [18; 19]), river level monitoring (e.g., [20]), estimation of the surface roughness and wind retrieval (e.g., [21; 22]), measurement of the soil moisture (e.g., [23; 24; 25; 26; 27]), altimetry (e.g., [28; 29]), monitoring the presence of vegetation (e.g., [30; 31]), and estimation of snow/ice thickness (e.g., [32]).