# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 3 Data and Experiments  
Two case studies were conducted with different SAR sensors to test our method. The first case is the Houston (US) flood accompanying Hurricane Harvey which took landfall on 25 August 2017 on Texas. Harvey moved on to Houston on August 26 and remained there for four days. The local National Weather Service office in Houston observed daily rainfall accumulations of 370 mm and 408 mm on August 26 and 27, respectively [36]. Multiple flash flood emergency alerts were issued in the Houston area by the night of August 26. The study area is located at the western part of Houston city with an extension of ca. 590 km\({}^{2}\), which is mainly occupied by residential houses/apartments, commercial and industrial districts, parks, and reservoirs. Eleven Sentinel-1 (C band, 20 m resolution, five Sentinel-1A and six Sentinel-1B) VV polarized Interferometric Wide Swath (IW) mode Single Look Complex (SLC) data acquired between 1 July 2017 and 30 August 2017 (with 6 days repetition rate) were used. Intensity and coherence data details are shown in Tables 1 and 2, respectively, with the flood acquisition marked in blue color. Intensity images were preprocessed by radiometric calibration, speckle reduction with the Refined Lee speckle filter (window size of \(7\times 7\) pixels), and converted from linear to dB. Coherence images were obtained by sequential image pairs with a \(28\times 7\) (Range \(\times\) Azimuth) window. Multi-looking with a \(4\times 1\) window was performed to all images to get a square pixel. All intensity and coherence images were stacked and geocoded with the 30 m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) to WGS1984 UTM Zone 15 N with a square pixel size of 15 m. Each image was scaled to the range (0,255) before the subsequent processing. The coherent (built-up) area filter was set as \(t=0.5\) (0.5 * 255 for the scaled coherence images) suggested by [PERSON] et al. (2015) and [PERSON] et al. (2016). The number of Gaussian components in this case study was \(k=100\). The validation dataset was virtually digitized based on aerial photographs with a spatial resolution of 35 cm acquired on 30-31 August 2017 by the National Oceanic and Atmospheric Administration (NOAA) Remote Sensing Division (2017). The second case study is the Joso (Japan) flood caused by the Kanto-Tohoku heavy rainfall on 9-11 September 2015 and the collapsed bank of Kinugawa River. The maximum rainfall accumulations exceeded 600 mm in the Kanto region and 500 mm in the Tohoku region, respectively. The water volume of the Kinugawa River increased rapidly in the city of Joso in the early morning of 10 September, and the floodwater quickly covered almost the entire area between Kinugawa River and Kokai River (Figure 5) at 12:50 pm local time (2017). The inundation area decreased from ca. 31 km\({}^{2}\) on 11 September to ca. 2 km\({}^{2}\) on 16 September (2017). The study area consists of the Joso city area and a wide rice paddy field located to the north of Joso city with an extension of ca. 114 km\({}^{2}\). Seven ALOS-2/PALSAR-2 (L band, 3 m resolution) HH polarized Stripmap mode (SM1) SLC data were obtained for this study case. Intensity and coherence data details are shown in Tables 3 and 4, respectively, with the flood acquisition marked in blue color. The same preprocessing procedures as in the above case were performed concerning intensity and coherence except that the multi-looking step was omitted and coherence images were obtained with a \(7\times 7\) (Range \(\times\) Azimuth) window. All images were geocoded with the 30 m SRTM DEM to WGS1984 UTM Zone 54N with a square pixel size of 2.5 m. The same experimental configuration of the Houston case was set: each data was scaled to the range (0,255) and \(t=0.5\) was assigned. The number of Gaussian components in this case study was \(k=40\). It is worth noting that the acquired data span across several seasons.