# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
## 4 Results and Discussion
### Joso Flood Case  
The intensity RGB composite (R = pre-event, G = B = co-event) of the Joso study area is shown in Figure 5a. The red color indicates flooded rice paddy, whereas cyan color hints potential flooded built-up  
Figure 4: Sentinel-1 \(\sigma^{\circ}\) and \(\gamma\) temporal variation for several typical landscapes within the Houston study area: (**a**) \(\sigma^{\circ}\) temporal trends; (**b**) \(\gamma\) temporal trends. areas and partially submersed vegetation, also including broadly spread vegetation that presents higher co-event \(\sigma^{\circ}\) than the pre-event one probably due to potential phenological variation. The white color displays built-up areas and some paddy fields on which occur strong double-bounce effects in both acquisitions. Figure 5b visualizes the coherence RGB composite (R = pre-event, G = B = co-event). The red color illustrates flooded built-up areas, some rural roads, and fields which are characterized by a high value of \(\gamma\) in L band pre-event data. The colorful appearance of RGB composite of intensity and coherence (R = co-event \(\sigma^{\circ}\), G = pre-event \(\gamma\), and B = co-event \(\gamma\)) in Figure 5c holds a wealth of information on the flood situation in different land-cover classes. The white color shows non-flooded built-up areas whereas flooded built-up areas are shown in yellow color. The green color illustrates the flooded coherent rural roads and fields that have low co-event \(\sigma^{\circ}\), high co-event \(\sigma^{\circ}\), and low co-event \(\gamma\). The black color reveals flooded rice paddy fields and permanent water such as the course of the rivers through the northern and southern part of the study area. Vegetation is depicted in red color which is largely distributed along the rivers. The purple color locates some non-flooded paddy fields that have high co-event \(\gamma\) and low pre-event \(\gamma\) probably due to the larger temporal baseline of pre-event \(\gamma\) (e.g., 168 days across seasons) compared to the co-event \(\gamma\) (e.g., 42 days in the same season). As the study area contains both urban and rural areas and we intend to mainly focus on flood detection in urban areas in this paper. We report the quantitative evaluations for both the whole study area (Figure 5) and the urban area of Joso city which is located in the yellow dashed rectangle in Figure 6a. Table 6 lists the evaluations of results produced by the fusion of \(\sigma^{\circ}\) and \(\gamma\) (Figure 6a,g) and  
Figure 5: RGB color composites of the Joso case study: (**a**) Intensity RGB composite, R = \(\sigma^{\circ}\) of 31 July 2015, G = B = \(\sigma^{\circ}\) of 11 September 2015; (**b**) Coherence RGB composite, R = \(\gamma\) of 13 February–31 July 2015, G = B = \(\gamma\) of 31 July–11 September 2015; (**c**) Intensity and coherence RGB composite, R = \(\sigma^{\circ}\) of 11 September 2015, G = \(\gamma\) of 13 February–31 July 2015, B = \(\gamma\) of 31 July–11 September 2015. \(\sigma^{\circ}\) alone (Figure 6c,i). When combining \(\sigma^{\circ}\) and \(\gamma\), the result for the urban area is slightly worse than for the whole area in terms of OA (e.g., 84.3% vs. 89.6%) and \(\kappa\) (e.g., 0.66 vs. 0.72). This degradation is mainly due to a larger underestimation in urban areas, e.g., 0.66 of recall compared to 0.70 in the whole area. This difference is enlarged when only \(\sigma^{\circ}\) data are used (e.g., 74.0% vs. 86.0% for OA and 0.42 vs. 0.61 for \(\kappa\)). The occurrence of specular surfaces, such as parking lots and shadowing caused by buildings/trees, can degrade the performance in urban areas. According to the mapping result shown in Figure 6a,b, the underestimation primarily appears in parking lots, densely distributed built-up areas and bare fields that show a similar \(\sigma^{\circ}\) in dry and flooded situations in the rural area. A subtle overestimation is found in both validation areas (e.g., 0.03 in both areas).