## 1 Introduction
The dielectric constant of tarmac is considerably lower than that of water, and undisturbed water is smoother than tarmac, implying an increase of surface reflectivity and a consequent reduction in backscatter [38]. As a result of these difficulties, less attention has been given to research into urban flood detection using SAR. Despite this, several studies have now been performed that have employed a number of different techniques. These include analysing the backscatter returns in a post-flood SAR image [39; 40; 41; 42; 43], considering changes in backscatter intensities between pre- and post-flood SAR images [29], exploiting interferometric coherence, as well as backscatter intensities, using pre- and post-flood SAR images [8; 44; 45; 38], and analysing SAR image time series [46]. An approach to urban flood detection that requires only pre- and post-flood Sentinel-1 imagery, allowing any flooding to be rapidly detected, is described in [44]. Strong double scattering in the pre-flood image from buildings roughly aligned with the satellite direction of travel is first used to detect urban areas [34; 41; 47]. Coherence changes between pre- and post-flood images are then used to refine the urban flooding determined using SAR intensity. Coherence should be high in urban areas that are not flooded, but low if there is flooding. The method achieved good results using imagery of flooding in Houston, Texas due to Hurricane Harvey in 2017. [PERSON] et al. (2019a) also employed pre- and post-flood Sentinel-1 data from the same flood to demonstrate that coherence provides valuable additional information to intensity in urban flood mapping, using an unsupervised Bayesian network fusion of intensity and coherence data [8]. In a similar manner, [PERSON] et al. (2019b) performed urban flood mapping with an active self-learning neural network based on TerraSAR-X intensity and coherence, again using the Houston flooding as a case study [45]. Good detection accuracy was obtained in areas containing fairly low-density detached suburban housing. These papers demonstrate significant progress towards urban flood detection using SAR data alone. Nevertheless, the flood detection accuracies obtainable in dense urban areas using HR SAR data were not fully explored. Many existing towns have much higher housing densities than the Houston suburbs (new estates in England are \(\sim\)\(\times\)8 denser). [PERSON] et al. (2021) [48] describe a change detection method of detecting flooding in dense urban areas using Sentinel-1 and the WorldDEM Digital Surface Model (DSM) [49]. Flood levels in urban areas are estimated at double scatterers using increased SAR backscatter in the post-flood image due to double scattering between water and adjacent buildings compared to the unflooded case, wherein the double scattering is between ground and buildings. Areas of urban flooding are detected by comparing an interpolated flood level surface to the DSM. Considering flood detection in urban areas of both high and low housing density using VHR SAR, [PERSON] et al. (2018) used LiDAR data of the urban areas in conjunction with a SAR simulator to predict areas of layover and shadow in the image caused by buildings and taller vegetation [42]. Flooding was detected in urban areas not in shadow or layover by analysing the backscattered intensities from a single-polarisation VHR SAR image acquired during the flooding. Flooding detected in these areas was propagated into adjacent areas of shadow and layover, provided they were of similar elevation to the flooded areas, irrespective of their backscatter. Considering the percentage of the urban flood extent visible in the validation data that was detected by the SAR, the flood detection accuracy averaged over the three test examples studied was 79%, with a false alarm rate of 10%.