# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 4 Results and Discussion
### Joso Flood Case
Figure 6b,h show the distribution of different flood categories and indicate that the obstructed-flooded non-coherent areas are largely distributed accompanying the obstructed-flooded coherent areas. This is rational as the presence of vegetation and anthropogenic activities reduce the coherence of built-up areas. The overall performance degrades when \(\sigma^{\circ}\) is used by itself. A large area falsely detected as flooded can be seen in Figure 6c (close to the right bottom corner of the yellow rectangle) due to the variation of \(\sigma^{\circ}\) in rice paddy field, and a severe underestimation can be found in the Joso city area (Figure 6i) with a low recall value of 0.45. Figure 6e,f illustrate flood posterior probability conditioned on \(\sigma^{\circ}\) and \(\gamma\), respectively. It can be found that \(\sigma^{\circ}\) provides very strong evidence in flooded rice paddy areas but a weaker one in flooded built-up areas. However, \(\gamma\) complements the flood information of built-up areas and other coherent areas such as rural roads. Figure 6: \(Cont.\)  
Figure 6: Flood extent maps of the Joso study area: **(a)** Binary flood extent based on the fusion of \(\sigma^{\circ}\) and \(\gamma\); **(b)** Flood category map of **(a)**; **(c)** Binary flood extent based on \(\sigma^{\circ}\) alone; **(d)** Reference flood mask derived from high-resolution aerial photograph provided by the Geospatial Information Authority of Japan (GSI); **(e)** Flood posterior probability conditioned on \(\sigma^{\circ}\); **(f)** Flood posterior probability conditioned on \(\gamma\); **(g)** Zoom-in of the yellow box in **(a)**; **(h)** Zoom-in of the yellow box in **(b)**; **(i)** Zoom-in of the yellow box in **(c)**; **(j)** Zoom-in of the yellow box in **(e)**; **(k)** Zoom-in of the yellow box in **(f)**. Figure 7 shows the temporal variation of the spatial average values of \(\sigma^{\circ}\) and \(\gamma\) over 6 different regions. R#1 is a completely flooded homogeneous rice paddy field. Flooding causes a significant decrease of \(\sigma^{\circ}\) in this area, ranging from around \(-2.5\) dB of pre-event \(\sigma^{\circ}\) (only considering acquisitions on 29 August 2014 and 31 July 2015) to \(-16\) dB of co-event \(\sigma^{\circ}\), whereas the change of \(\gamma\) (e.g., 0.03) between pre- and co-event data is hardly detectable. R#2 and R#3 are flooded built-up areas observed with different \(\phi\) angles. R#2 has a smaller \(\phi\) than R#3. It can be found that the increase of co-event \(\sigma^{\circ}\) caused by double-bounce in R#2 is larger than R#3 (e.g., around 3.6 dB against 1.6 dB). However, the drop-off in co-event \(\gamma\) has less difference between R#2 and R#3 (e.g., 0.30 against 0.31). It indicates that \(\gamma\) as a complement to \(\sigma^{\circ}\) can help identify floodwater in built-up areas where might fail to detect by \(\sigma^{\circ}\) alone. Besides, the effect of \(\phi\) can be mitigated by combing the descending and ascending acquisitions that observe the same area from different viewing angles and increase the chance of floodwater detection. R#4 consists of flooded trees. It shows a strong double-bounce effect between floodwater and tree trunks thanks to the high penetration capability of L-band. The backscattering \(\sigma^{\circ}\) increases from around \(-8.0\) dB to \(-5.3\) dB when inundation appears. Without surprise, the variation between pre- and co-event \(\gamma\) (e.g., 0.04) is as subtle as for R#1. The regions of R#5 and R#6 are corresponding to underestimated flooded parking lots and dense building blocks, respectively. For R#5, \(\gamma\) changes from around 0.31 in pre-event to 0.23 in co-event data. Due to busy anthropogenic activities, the low pre-event \(\gamma\) attenuates the \(\gamma\) variation caused by flooding over time and thus hampers flood detection based on \(\gamma\). On the other hand, both pre- and co-event \(\sigma^{\circ}\) are very low (e.g., \(-14.8\) dB against \(-14.6\) dB), as the backscattering from parking lots in dry conditions is also specular.