# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 1 Introduction  
Flooding is a widespread and dramatic natural disaster that affects lives, infrastructures, economics and local ecosystems in the world. It is reported that flood events were the main cause of internal displacement in 2008 to 2015 [1; 2], and global economic losses due to floods in economically strong and populated areas are projected to reach US $597 billion in 2016-2035 [3]. Remote sensing data can offer a synoptic view over large areas systematically and provides useful information about the extent and dynamics of floods. Several international initiatives such as the International Charter \"Space and Major Disasters\" and the European Copernicus Emergency Management Service - Mapping have leveraged Earth Observation (EO) data to provide products and services for crisisresponse in the context of disaster management. Synthetic aperture radar (SAR) sensors are the most widely used EO sources in flood mapping due to their all-weather and day-night imaging capability. Nowadays, the growing number of Synthetic Aperture Radar (SAR) satellite missions in orbits such as the Constellation of small Satellites for Mediterranean basin Observation (COSMO-SkyMed) [4], TerraSAR-X [5], Sentinel-1 [6], RADARSAT-2 [7], and the Phased-Array L-band SAR-2 (PALSAR-2) aboard the Advanced Land Observation Satellite-2 (ALOS-2) [8] have shortened the revisit periods (e.g., 6 days with the Sentinel-1A/B constellation, and 1 day with the full COSMO-SkyMed constellation) and facilitated rapid flood mapping within the context of emergency response. SAR-based flood mapping in rural areas (e.g., bare soils and sparse vegetation) has been extensively studied and explored [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. The specular reflection occurring on smooth water surfaces results in a dark tone in SAR data, which makes floodwater distinguishable from dry land surfaces. Both uni- [10; 14; 15] and multi-temporal [9; 16; 17; 18; 19] SAR data have been employed in flood mapping based on either supervised [14; 19] methods with available training data or unsupervised [9; 10; 12; 15; 16; 17; 18] methods without any training data. Urban areas with low slopes and a high percentage of impervious surfaces are vulnerable to flooding and the increased risk of loss of human lives and damage to economic infrastructures makes urban flood mapping greatly valuable in terms of disaster risk reduction. However, flood detection in urban areas is challenging to SAR due to the complex backscatter mechanisms associated with varying building types and heights, vegetation areas, and different road topologies [20]. Several studies [21; 22; 23; 24; 25] have led to noteworthy progress in the understanding of SAR backscatter characteristics in the urban environment and further made considerable advances in flood mapping in urban areas. Nevertheless, it is not easy to incorporate knowledge about backscatter phenology into generic analysis algorithms and specific algorithms are required. A couple of studies have demonstrated the success of high-resolution SAR data in urban flood mapping. [PERSON] et al. [26; 27] proposed a near real-time approach for urban flood detection based on high-resolution TerraSAR-X image of the Tewkesbury (England) flood in the summer of 2007. They used a SAR simulator in conjunction with a very high-resolution LiDAR digital surface model (DSM) to account for misclassification due to layover and shadow. Automatic change detection based on bi-temporal TerraSAR-X data on the same flood event was suggested by [PERSON] et al. [28] to suppress false alarms caused by shadow and water look-alike surfaces. [PERSON] et al. [29] adopted the GO-GO scattering model to detect floodwater in layover areas via double-bounce scattering. More recently, [PERSON] et al. [30] applied high-resolution RADARSAT-2 data combined with hydraulic data (flood return period) for flood detection in urban areas based on case studies of the 2011 Richelieu River flood (Canada) and achieved promising results.