## 6 Conclusions  
For the first time, a fully automatic algorithm capable of mapping floodwater in both rural and built-up areas using 20 m SAR data has been presented. It was designed to exploit the short temporal and perpendicular baselines of S-1 image pairs, thus taking advantage not only of SAR intensity,  
Figure 8: Sensitivity analysis of InSAR coherence in urban areas for the Flooded (F) and Non-Flooded (NF) classes. (**a**) InSAR coherence vs. temporal baseline; (**b**) InSAR coherence vs. perpendicular baseline.  
but also of InSAR coherence data. The algorithm was tested using the images acquired during the hurricane season in 2017, which caused large-scale flooding in and around the city of Houston (Texas). The floodwater maps obtained by applying the proposed SAR-based automatic floodwater mapping algorithm agree with those derived from a visual analysis of VHR optical images acquired shortly after the hurricane's passage, when cloud cover no longer hampered their use. Moreover, a qualitative cross-comparison of our results with Digital Globe crowdsourcing points and the FEMA hydrological model has shown a strong agreement for most affected buildings.  
An analysis of the S-1 image stack available revealed that a short temporal baseline of the co-event image pair is required to make the exploitation of coherence for flood detection effective. Conversely, a stack with longer temporal baseline is mostly suited to producing a prior classification of the built-up area. The range of perpendicular baselines ensured by the S-1 orbital tube does not affect the performance of the method.  
The findings of this study demonstrate that the progress achieved in SAR imaging by the S-1 constellation (large spatial coverage, short revisit time, repeat-pass observations with small perpendicular baseline) potentially enabled emergency managers to fulfill their need for gathering NRT synoptic maps of the flood extent that also included urban settlements. The algorithm proposed can represent a valuable tool to pursue this objective. Nonetheless, further verifications of its performances considering other case studies are needed and are planned for the near future.  
Moreover, advancing the capability to monitor floodwater within urban settlements paves the way for new applications to be developed in disaster risk management. For example, it becomes possible to regularly update/correct urban flood models through data assimilation procedures, as well as to estimate related variables such as river discharge and channel bathymetry. Different studies have already shown the value of satellite EO-derived floodwater extent maps for reducing the predictive uncertainty of numerical models and deriving key hydrological variables [50; 51; 52]. However, the inability to delineate floodwater in urban areas has so far restricted such approaches to open areas, e.g., agricultural land outside the city centers.  
Conceptualization, M.C.; Formal analysis, M.C. and R.P.; Investigation, M.C., R.P. and L.P.; Methodology, M.C.; Software, M.C., R.P., R.H. and P.M.; Validation, R.P.; Writing--original draft, M.C. and R.P.; Writing--review & editing, M.C., R.P., R.H., P.M., L.P. and N.P.  
This research was funded by the National Research Fund of Luxembourg (FNR) through the MOSQUITO project, grant number C15/SR/10380137.  
The authors wish to acknowledge the Digital Globe Open Data Program for publicly releasing pre- and post-event imagery and crowdsourcing data over affected areas during Harvey.  
The authors declare no conflict of interest.