## 1 Introduction  
As floods presently rival earthquakes and hurricanes in terms of economic losses [1], insurance companies face the problem of a substantial increase in flood claims. Their frequency in Germany and central Europe has increased by a factor of two since 1980 [2]. In 2013, around 45% of the insured losses derived from inland flooding. To improve their services, insurance companies require detailed global and national disaster impact databases to identify areas vulnerable to flooding and to support parametric insurance, i.e., to trigger payouts in case of emergency using pre-defined criteria. Several international Earth Observation (EO) initiatives have set up a research and development agenda to propose new products to serve this user community and more in general to improve the services provided for crisis response and mitigation. For example, the Working group on Disasters of the Committee on Earth Observation Satellites (CEOS) highlighted the need to exploit the full potential of new EO data sets to support flood management in its various phases [3]. In this context, SAR data play a major role in operational services for flood risk management thanks to their high sensitivity to water and the ability to provide data during day and night, regardless of cloud cover. Nowadays, several satellite constellations, such as COSMO-SkyMed (CSK) [4], TerraSAR-X (TSX) [5] and Sentinel-1 (S-1) allow for a considerable reduction in the revisit time of satellite images, which is an important requirement stated by the Disaster Working group. Many studies have demonstrated that SAR systems are suitable tools for floodwater mapping on bare soil or scarcely vegetated areas [6; 7; 8; 9; 10], where the water has a well-defined backscattering signature [11]. Water bodies appear as dark areas in SAR images, being smooth surfaces that typically reflect the radar signal in the specular direction away from the antenna, thereby producing a very low backscatter. For this reason, a single radar observation of a flood can detect floodwater in such an environment [7; 12; 13], although change detection approaches are often used to mask out permanent water or false alarms caused by shadows or smooth surfaces such as tarmac [8; 14; 15]. Indeed, the use of SAR data is nowadays well-established in operational flood management thanks to automatic algorithms able to generate floodwater maps in near real-time (NRT) [16; 17; 18]. Although in recent years much progress has been made in the development of NRT floodwater mapping procedures, the detection of inundation in complex environments, such as vegetated and urban areas, still requires further studies [19; 20; 21; 22; 23; 24; 25; 26]. This is because radar signatures of such targets may be quite complex to predict and are often ambiguous. Other challenges that may be encountered are related to atmospheric conditions, heavy precipitation, and high winds, inducing surface roughening, which can cause misdetection due to the reduced contrast between backscatter from flooded and non-flooded soils [27]. The presence of water in urban areas, e.g., on a road in front of a building, can be detected thanks to a significant enhancement of the double-bounce scattering mechanism, as the smooth and high permittivity water increases the surface reflectivity [19]. The double-bounce increase, due to the presence of floodwater, may not be high enough if the building facades are not parallel to the SAR flight direction. The Interferometric SAR (InSAR) coherence, \(\rho\), provided by Very High-Resolution (VHR) SAR data, has been used to reduce under detection in urban areas [21; 22]. Also, the bistatic InSAR coherence, from TanDEM-X, has shown some potentiality for more accurate floodwater mapping in a complex landscape with urban and vegetation areas [28]. The high sensitivity of \(\rho\) to surface changes is well-documented and enables the detection of damages caused by catastrophic events such as volcano eruptions, earthquakes, and floods [29; 30; 31; 32; 33].