## 2 Assumptions and Methods
The enhanced observational capabilities of the C-band S-1 mission [37] reduce the drawbacks of previous moderate resolution SARs and potentially enable a more accurate detection of floodwater in urban areas. Indeed, the high repeat cycle (i.e., small temporal baseline) and the relatively narrow orbit tube (i.e., small perpendicular interferometric baseline) are all characteristics that provide new momentum for the development of innovative automatic urban floodwater mapping algorithms. From the results obtained using VHR SAR data and given the current availability of systematic acquisitions of medium resolution SAR data, we hypothesize that is now possible to detect floods in urban areas using SAR medium resolution data. The \(\rho\) feature is defined as the normalized cross correlation between an interferometric pair of SAR images and it is related to the change in the spatial arrangement of the scatterers within a pixel [29], and thus to geometric changes in the scene. An interferogram and its \(\rho\) can be built using either two images taken before the event (pre-event coherence, \(\rho_{\text{pre}}\)) or with one before and one during the flood event (co-event coherence, \(\rho_{\text{co}}\)). Given that urban areas are usually coherent in time, especially with respect to the double-bounce feature, areas where \(\rho_{\text{co}}\) decreases with respect to \(\rho_{\text{pre}}\) can be considered as subject to changes. Nevertheless, a thoughtful investigation of causes triggering the drop-off must be carried out, as numerous factors may influence \(\rho\). [PERSON] & [PERSON] [36] highlighted the spatial baseline, the rotation of the target between observations and the temporal baseline as the main sources of the image decorrelation. It is obvious that the latter factor is the one enabling the detection of water appearing between buildings, while the former must be accounted to avoid false alarms. Indeed, the increase of the spatial baseline, in particular its perpendicular component, implies a decrease of \(\rho\), thus reducing the difference between \(\rho_{\text{pre}}\) and \(\rho_{\text{co}}\). The effect of the perpendicular baseline is even more important in urban areas, where the geometrical complexity of structures accentuates the effect [38]. Changing vegetation is usually another important source of false alarms. To reduce such effects, it is recommended to compute \(\rho\) using pairs of images that were acquired close in time, i.e., with a short temporal baseline. With decametric spatial resolution data, such as the 20 m S-1 IW data, the dominant scattering mechanism is still the double-bounce from buildings. However, contributions from other scatterers in the same pixel are expected and other decorrelation factors thus must be considered. Fortunately, the \(\rho\) degradation due to changes in the spatial and temporal baselines is reduced when considering S-1 SAR data. S-1 has a small and well-controlled orbital tube of 50 m, resulting in a small spatial baseline between image pairs that is, in the worst-case, equal to 100 m. The S-1 orbit cycle is 12 days and when considering just one satellite, interferometric couples are thus also available every 12 days. With the two satellites currently available, the temporal baseline is reduced to 6 days. The short revisit time is another factor that helps containing the risk of undesired \(\rho\) decrease.