## 2 Materials and Methods  
We used temporal changes in coherence as a measure of land surface changes that resulted from three severe weather events (i.e., Tax Day storm, Hurricane Harvey, and Tropical Storm Imelda) to map the spatial extent of the recurrently flood-impacted areas in Houston city and its surrounding areas. Eleven descending Interferometric Wide Swath (IW) mode Single Look Complex (SLC) Sentinel-1A/B radar images acquired before and after the three investigated flood seasons (spanning from 14 March 2016 to 25 September 2019; Table 1) were used for the multitemporal CCD-based analyses. Four SAR images (three pre-event and one post-event) for each of the 2016 and 2017 flood events and three images (two pre-event and one post-event) for the recent (2019) flood event were used to generate the coherence products (Table 1). Although SAR imaging is capable of depicting surface conditions under varying weather conditions, intense precipitation can potentially create image distortions during heavy precipitation conditions caused by the attenuation of the SAR signal by rain cells [65]. As a result, SAR images in which attenuation attributed to intense rainfall has been noted over wide zones of the study area, characterized by a wide dark patch, were excluded from the data analysis. Result generation was facilitated through the Hybrid Pluggable Processing Pipeline (HyP3; [66])--a cloud-based platform of the Alaska Satellite Facility that uses GAMMA software to processes Sentinel-1 SLC data. The data processing steps for generating the coherence products include co-registration of the Sentinel-1 imagery using the matching and spectral diversity method, resampling the reference and \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**SAR Data Type**} & \multicolumn{1}{c}{**Flight Path**} & \begin{tabular}{c} **Perpendicular** \\ **Baseline (m)** \\ \end{tabular} & \begin{tabular}{c} **Temporal** \\ **Baseline (Days)** \\ \end{tabular} & \
\begin{tabular}{c} **Severe Weather** \\ **Event** \\ \end{tabular} \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Sentinel-1A/B SLC** \\ **Granule Pairs** \\ **(yyyyymmdd)** \\ \end{tabular} } & 20160314–20160326 & Descending & 97.6625 & 12 \\ \cline{2-5}  & 201603026–20160407 & Descending & 14.3211 & 12 \\ \cline{2-5}  & 20160407–20160419 & Descending & –39.2588 & 12 \\ \cline{2-5}  & 20170812–20170818 & Descending & –55.1226 & 6 \\ \cline{2-5}  & 20170818–20170824 & Descending & 79.709 & 6 \\ \cline{2-5}  & 20170824–20170905 & Descending & –20.2613 & 12 \\ \cline{2-5}  & 20190907–20190913 & Descending & 36.2323 & 6 \\ \cline{2-5}  & 20190913–20190925 & Descending & 28.6556 & 12 \\ \hline \multicolumn{1}{c}{**UAVSAR GRD**} & 20170902 (Brazos\_14938) & Descending & \multicolumn{1}{c}{} & Hurricane Harvey \\ \cline{2-5}  & 20170902 (sunjac\_14939) & Descending & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}  
* Note: GRD refers to ground range detected; SLC, Single Look Complex; UAVSAR, Uninhabited Aerial Vehicle Synthetic Aperture Radar.  
\end{table} 
Table 1: Synthetic Aperture Radar (SAR) datasets used for mapping and validating the flood occurrences following severe weather events in the study area. secondary scenes, generation of differential interferograms, and estimation of the interferometric coherence [67]. The final product is multilooked by a factor of 4 in azimuth and 20 in range, resulting in a spatial resolution of 80 m. Coherence (\(\Upsilon\)), the amplitude of the complex correlation coefficient between SAR images \(A\) and \(B\), is estimated using the following equation [68; 69]:  
\[\Upsilon=\ \left|\frac{\frac{1}{n}\sum_{i=0}^{n}A_{i}B_{i}^{*}}{\sqrt{\frac{1}{n} \sum_{i=0}^{n}A_{i}A_{i}^{*}\frac{1}{n}\sum_{i=0}^{n}B_{i}B_{i}^{*}}}\right| \tag{1}\]  
where \(i\) represents individual pixels of images \(A\) and \(B\), respectively; \(A^{*}\) and \(B^{*}\) are the complex conjugates of SAR images \(A\) and \(B\), respectively; n signifies the number of neighboring pixels to be estimated [69].