# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 4 Results and Discussion
### Houston Flood Case
Comparing the extracted flood extent in Figure 3a with the reference flood mask in Figure 3d  
Figure 2: RGB color composites of the Houston case study: **(a)** Intensity RGB composite, R = \(\sigma^{\circ}\) of 24 August 2017, G = B = \(\sigma^{\circ}\) of 30 August 2017; **(b)** Coherence RGB composite, R = \(\gamma\) of 18–24 August 2017, G = B = \(\gamma\) of 24–30 August 2017; **(c)** Intensity and coherence RGB composite, R = \(\sigma^{\circ}\) of 30 August 2017, G = \(\gamma\) of 18–24 August 2017, B = \(\gamma\) of 24–30 August 2017.  
it can be found that the spatial pattern of flooded areas is extracted accurately. The overestimation is very low (e.g., 0.02), however, a relatively large underestimation can be found, with 0.61 recall. The underestimated inundation areas are mainly flooded dense built-up areas with heavy vegetation. In these areas, pre-event coherence is expected to be low and the canopy attenuates the double-bounce scattering occurring between floodwater surfaces and building walls. Thus, it is difficult to detect either a significant increase in \(\sigma^{\circ}\) or decrease in \(\gamma\) between the pre- and co-event acquisitions. Figure 3b provides an insight of distributions of different flood categories: non-obstructed-flood; obstructed-flooded non-coherent areas such as vegetation and vegetated built-up areas; and obstructed-flooded coherent areas, predominantly built-up areas. When only \(\sigma^{\circ}\) data are used, lower \(\kappa\) (e.g., 0.60) and F1 score (e.g., 0.63) are achieved with slight differences in precision and FPR compared to the joint use of \(\sigma^{\circ}\) and \(\gamma\). Figure 3c shows a larger underestimation especially in built-up areas compared to Figure 3a,b, corresponding to a lower recall value of 0.50. To gain an insight of the contributions from \(\sigma^{\circ}\) and \(\gamma\) in urban flood detection, Figure 3e,f show the flood posterior probability conditioned on \(\sigma^{\circ}\) and \(\gamma\), respectively, providing a perception of distribution of the flood evidence that supported by \(\sigma^{\circ}\) and \(\gamma\). It can be found that the detected non-obstructed-flood in Figure 3b is dominantly determined by \(\sigma^{\circ}\). Flooded built-up areas are captured by both \(\sigma^{\circ}\) and \(\gamma\) but \(\gamma\) adds further comprehensive flood information. To get a better understanding of the roles of \(\sigma^{\circ}\) and \(\gamma\) for flood detection in urban environments, we explore the temporal variation of spatial average values of \(\sigma^{\circ}\) and \(\gamma\) in 4 regions that represent different flooded land-cover types, as shown in Figure 4. R#1 is a homogenous short vegetation area that is (almost) wholly submerged in the flood event, the pre-event \(\gamma\) is low in this area (e.g., the maximum value is 0.27 and mean value is 0.18). Although the appearance of floodwater leads to a lower co-event \(\gamma\) (e.g., 0.12), \(\gamma\) is not reliable information for flood detection in this area because of the low values and large variation of the pre-event \(\gamma\). The pre-event \(\sigma^{\circ}\) is around \(-8.5\) dB characterized by mixed surface and volume backscattering, however, the co-event \(\sigma^{\circ}\) decreases significantly to \(-13.7\) dB due to the specular reflection results from floodwater surface. Thus, \(\sigma^{\circ}\) provides useful information for flood detection in this area. R#2 is a predominantly built-up area. High pre-event \(\gamma\) (e.g., around 0.85) holds for this temporally stable area, the appearance of floodwater results in a significant drop-off in the co-event \(\gamma\) (e.g., 0.35). Besides, the co-event \(\sigma^{\circ}\) also increases substantially by the enhanced double-bounce effect, from around \(-5.5\) dB to \(-0.3\) dB. Therefore, both \(\sigma^{\circ}\) and \(\gamma\) are useful for flood detection in this area. R#3 consists of building blocks with trees.