## 1 Introduction
### Floodwater Delineation Approaches and Possible Limitations
[63] applied multi-temporal amplitude change assessment to pre- and post-event (flooding) high-resolution X-band (3.1 cm wavelength; [64]) images of the Italian COSMO-SkyMed constellation to delineate the extent of flooding in their study area caused by the landfall of Hurricane Harvey. They validated the SAR-based results using social media data and the hydraulic model. However, their analysis included the assumption that all water areas will have low backscatter, potentially excluding inundated areas with rough water surfaces resulting from different factors. For instance, changes in the roughness of water surfaces following heavy rainfall and windy conditions might alter the backscatter properties of the target surface and might lead to inaccurate interpretation [54]. Moreover, the developed approach did not consider temporal changes in the surface that could be revealed through interferometric approaches. For instance, the amplitude values of contrasted surfaces such as roads and urban areas, where there might be substantial anthropogenic-led land surface changes between acquisitions, may not reveal the changes in the properties of the target at different times. In the case of vegetation-covered areas, ACD-based floodwater mapping approaches might inaccurately classify the changes resulting from seasonal changes in the growth cycle of the vegetation as flooded pixels (beneath vegetation). In a two-step approach, outlined by [50], to map flooding brought about by heavy rains from Hurricane Harvey within the urbanized area of Houston, double-bounce/bright features in SAR images were initially identified through multi-temporal SAR intensity analysis. This was followed by CCD assessment in search of a decline in coherence over the earlier identified features to map flooding. The presence of vegetation in the investigated area and the resulting decline in coherence between acquisitions has been cited as a possible reason for the underdetection of flooded areas [50]. While CCD approaches improve flood mapping by resolving the ambiguities posed by amplitude/intensity-based detections where coherence decline between acquisitions is principally used to map floods, caution must still be applied where surface changes from anthropogenic activities between acquisitions might result in a decrease in coherence and, hence, an inaccurate classification. As stated earlier, floodwater detection approaches based on data acquired by short wavelength sensors, such as C-band Sentinel-1 and X-band COSMO-SkyMed, could potentially underdetect floodwaters in vegetated areas. The flood mapping studies over the study area outlined above could potentially underdetect the spatial distribution of floodwaters due to this fact in the absence of data and approaches to validate the results. A comprehensive approach that integrates robust SAR data-based change detection assessment techniques with relevant datasets and methods could compensate for the limitations stated earlier and could improve the accuracy of flood detections through the calibration or validation of existing models. Many flood mapping studies undertaken over parts of the study area have focused on mapping the extent of the floods caused by Hurricane Harvey. With the projected recurrence of severe weather events and the expected disasters and their adverse implications, an approach that outlines the susceptibility of different localities within the study area to flooding based on assessment of the impacts from earlier events would be beneficial. In this study, an integrated approach using novel SAR-based techniques and ancillary datasets were developed to map land surface changes resulting from major flooding from periods of severe weather-induced heavy rainfall over the city of Houston and parts of the surrounding metro area. Such areas included portions or the entirety of the following counties: Harris, Fort Bend, Waller, Montgomery, Liberty, Brazoria, and Galveston (Figure 1).