## 1 Introduction
### Floodwater Delineation Approaches and Possible Limitations
For instance, [45] used the manual thresholding approach on pre- and post-Hurricane Harvey Sentinel-1 SAR amplitude images to delineate flooded agricultural fields in Texas and Louisiana. They validated the SAR-based mapping using manually-derived ground reference validation data from the Landsat-7/8 and Sentinel-2 satellites. The wide time gap between the data acquisition of the two datasets (SAR and optical data) and the dense cloud cover conditions during the event makes it difficult to reliably assess the extent of floods in conjunction with the SAR data. Moreover, in the absence of validating data to support flooding beneath the vegetation cover due to the limited capability of the C-band signals from the Sentinel-1 mission to see beyond the top surface, mapping exercises might over/underestimate the extent of flooding [46; 47]. However, this condition is dependent on several factors, including the crop/vegetation growth stage (e.g., leaf-on/leaf-off conditions), distribution (e.g., sparse forest and dense forest), and crop/vegetation type [48; 49]. More importantly, the results and interpretations from single SAR scenes are complex, affected by various forms and sources of errors and factors that affect the backscatter signal that might impact the reliability of the derived products. For instance, SAR shadow zones or large paved and smooth surfaces, such as airport runways, might be erroneously mapped as flooded surfaces because  
Figure 1: (**a**) The study area, including Houston city and parts of the surrounding areas (counties Harris, Fort Bend, Waller (partial), Montgomery (partial), Liberty (partial), Brazoria (partial), and Galveston (partial)). The two rivers (San Jacinto and Brazos) that cut through the northeastern and southwestern parts along with the respective watersheds (West Fork San Jacinto and Lower Brazos, respectively, shown with dotted black lines), and surface elevation data retrieved from a digital elevation model (DEM) are also shown. The inset shows the location of the study area in the USA. (**b**) Land cover distribution map derived from the National Land Cover Database (NLCD; [44]). The landcover classes were re-grouped into the six major dominant land cover types prevalent in the study area. of their low backscatter values [50; 51; 52; 53]. Change detection methods that highlight temporal changes in the property and reflectivity of the same area on the Earth's surface at different times observed using multiple SAR image acquisitions have proven to be more robust in detecting surface changes that have resulted from flooding hazards [37; 54]. Two types of change detection methods are widely employed using SAR data to observe surface changes from flooding events: Amplitude (intensity) change detection (ACD) and coherent change detection (CCD) [55; 56]. SAR datasets acquired before and after an event or series of events with similar geometry and mode of acquisition are used in both methods. The ACD method is based on changes in the intensity/amplitude values of the SAR backscatter signal, while the CCD method infers surface changes through observations of changes in interferometric coherence by taking both the amplitude and phase changes between image acquisitions into consideration [57]. A decline in the coherence value (or a complete decorrelation) is deemed to represent surface changes that have resulted from events and processes that disturbed or altered the arrangement of scatterers within a pixel [58; 59; 60; 61]. Coherence-based change detections are more capable of and sensitive to detecting subtle surface changes that otherwise may not be noticeable in SAR amplitude images due to little changes and variations in backscattering intensity [47; 60; 61; 62]. Numerous studies have examined land surface changes that have resulted from flooding events using change detection methods applied to SAR images and, in some instances, supported by relevant auxiliary data.