## 3 Results
### Sentinel-1 SAR-based Flood Mapping
#### 3.1.1 The Tax Day Storm and Hurricane Harvey Flood Events  
The results of the analysis of SAR data to detect flood recurrence during the 2016 and 2017 flood events are shown in Figure 3. For the pre-event stack (Table 1), pixels that exhibited consistency in  
Figure 6: Validation of the Sentinel-1-based flood-prone areas using unsupervised classification applied to the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) images (Figure 6a–d) and National Oceanic and Atmospheric Administration (NOAA) aerial photos acquired during Hurricane Harvey (Figure 6e–g). Four sites (Sites I–IV) in different parts of the study area were selected to demonstrate the reliability of the floodwater detection approach using Sentinel-1 data as the input. Two sites in which there is an overlap of UAVSAR and NOAA aerial image acquisitions (Sites I and III) were selected to correlate the flood mapping from the two sources and to further demonstrate the accuracy of the proposed approach. values from the overall pre-event coherence stack (mean = 0.97; standard deviation = 0.013) were retained through the approach explained earlier. The approach retained 87% of the total pixels in the pre-event stack, and these pixels were compared against the co-event (20160407-20160419; 20170824-20170905; Table 1) coherence pixels to identify those pixels whose values had declined by more than 45%. The final flood map for the 2016-2017 flood event revealed that 7.5% of the total initial pixels (prior to the pre-event coherence analysis) and only 8.6% of the total pixels that exhibited coherence consistency were found to have lost their values by more than 45% when compared to the co-event stack. These pixels are presumed to represent areas that have repeatedly experienced severe weather-induced flooding and are assumed to be prone to the risk of flooding in possible future events. The two flood events recurrently impacted nearly 781 km\({}^{2}\) of land surface in the Greater Houston area (Figure 3). The predominantly inundated land cover types include forest and woody areas (28%), agricultural fields (25%), and herbaceous and shrub-covered areas (35%) (Figures 1b and 3). It should be noted that even though the dominant land cover types affected by the flood hazards in these areas are those listed earlier, there are pockets of land with developed land cover type along with dense population distribution within these areas. For instance, large swaths of land affected by flooding in Fort Bend county (population density >600 people/square mile [79]) are covered with crops, hay, shrub, and forest (Figures 1b and 3), yet patches of developed areas (Figures 1b and 3) inhabited by dense populations within and surrounding these areas were also impacted by the flooding. The impact of the flood hazards is not uniform throughout the study area. Some of the areas that were widely impacted by the events include areas along the courses of the San Jacinto and Brazos rivers, the creeks in Houston and its surrounding areas, the Galveston Bay area, and large swaths of land to the northeast, southeast, and northwest of downtown Houston (Fort Bend, Waller, Harris, and Liberty counties, respectively; Figure 3). Assessment of the temporal changes in coherence in these areas revealed a decline in the coherence values that we attributed to changes that resulted from the severe weather-induced flooding. In the following section, the changes/decline in the coherence values in selected areas that represent various sections (and land cover types) of the study area, identified using the approach outlined above as being recurrently impacted by the two flood events, are discussed. The Cypress Creek area of Houston (yellow box in Figure 3) has a documented history of flood recurrence [80] and the two flood events discussed herein also affected areas within and surrounding the creek.