## 2 Materials and Methods
Figure 4: Flood mapping of Houston and its surrounding areas following Tropical Storm Imelda (2019) derived using Sentinel-1 data. The results demonstrate the reliability of the developed CCD-based approach for identifying the recurrently flood-affected areas of Houston and its surrounding areas using datasets from earlier severe weather events (i.e., Tax Day storm and Hurricane Harvey). The George Bush International Airport (red box) area was selected as a test site to demonstrate the change in coherence observed as a result of flooding following the three severe weather events. Figure 5: Changes in the coherence values of the pixels in the George Bush Intercontinental Airport area (for location, see Figure 4). With few exceptions, Hurricane Harvey seems to have inflicted significant surface changes in the area, as revealed by the higher percentage of decline in coherence values (64.64%), followed by the Tax Day storm (58.96%) and then Tropical Storm Imelda (50.32%). Moderate- to high-resolution topographic datasets that depict the variation in the morphology of the surface are critical inputs in flood models and simulations. In this study, further calibration of the SAR-derived flood mapping product was undertaken with the support of the moderate spatial resolution (12.5 m) Phased Array type L-band Synthetic Aperture Radar (PALSAR) Digital Elevation Model (DEM). The products derived from analyzing the DEM data, such as slope, flow direction, and watershed, and stream network delineation were incorporated into the SAR-based model to help reduce false detections by eliminating flood pixels from steep slopes, shadowed regions, or on the surface of existing water bodies [72]. Sections of the study area are covered with various types of vegetation with varying density and distribution (Figure 2). As a result, the quality of the proposed methodology that is based on the C-band Sentinel-1 dataset needs to be tested to see whether the approach is able to map the flood underneath dense vegetation, noting that coherence-based detections of flood occurrence will be challenging across scenes of varying seasons, especially if vegetation in the area experiences a strong annual growth cycle (e.g., crops versus slower-growing forest). Land cover products from the recent (2016) National Land Cover Database (NLCD) [44] were used to select woody and forested areas that could possibly limit the capability of the sensor to observe the land surface conditions beyond the top canopy/woody surfaces. We applied the polarimetric decomposition technique on targeted Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) datasets with flight paths adjacent to the San Jacinto River and Brazos rivers (Table 1) acquired post-Hurricane Harvey (2 September 2017) to classify/map inundation beneath the vegetation of three selected sites (Figure 6). We then attempted to qualitatively compare the findings with the C-band-based flood mapping approach. The UAVSAR mission acquires fully polarimetric L-band SAR data, flying from an altitude of 12.5 km and mapping a 20 km swath area [73]. We selected two densely vegetated areas located near the courses of the two major rivers to test the robustness of the proposed approach in detecting floods outside sparsely vegetated and urbanized areas (Figures 1b and 6). Data analysis and result generation, that is, flood detection, were performed using the quadrature polarization (quad-pol) ground range detected/projected (GRD) UAVSAR product as the input in PolSARpro software [74]. The key data processing steps included: (1) Deriving coherency matrix [T3] products from the input UAVSAR GRD files; (2) modeling/decomposing the coherency elements as a contribution of three-component scattering mechanisms--surface (termed \"Odd\") scattering, double-bounce (termed \"Dbl\") scattering, and volume (termed \"Vol\") scattering--using the Freeman-Durden polarimetric decomposition method [74; 75; 76]. An empirical threshold value of 0.5 was set to clearly distinguish the contribution of the three components.