Towards High-Precision Flood Mapping: Multi-Temporal Sar/Insar Data, Bayesian Inference, And Hydrologic Modeling

High-resolution flood mapping is an essential step in the monitoring and prevention of inundation hazard, both to gain insight into the processes involved in the generation of flooding events, and from the practical point of view of the precise assessment of inundated areas, useful e.g. in the case of post-event recovery and insurance indemnity assessments. Synthetic Aperture Radar (SAR) data present several favourable characteristics for flood mapping, such as their relative insensitivity to the meteorological conditions during acquisitions, thanks to the use of microwaves as sensing radiation, as well as the possibility of acquiring imagery independently of solar illumination, thanks to the active nature of the radar sensors. The Italian COSMO-SkyMed (CSK) SAR constellation is particularly useful in this respect, because it allows image sequences of flooding events to be built up with short revisit times. The acquisition of several images before, during and after the event often allow a reconstruction of the flooding dynamics. Moreover, they help in interpreting the backscatter signatures of different land cover types, reducing uncertainties about the actual presence of water, which can be seriously misleading, especially over agricultural areas [1, 2]. Finally, when acquisitions are made from the same geometry, with short repeat intervals, SAR interferometry (InSAR) observables, such as the coherence or the differential InSAR phase can be exploited as additional information layers. The favorable characteristics of these next-generation sensors have been exploited by a number of researchers worldwide [3, 4, 5] to improve performances of flood mapping approaches. Recently, our group [6, 2] has used high-resolution CSK radar images for flood mapping exploiting both the intensity and the interferometric coherence, with promising results. Nevertheless, additional information can be used to improve flood detection. In case of flooding, distance from the river, terrain elevation, hydrologic information or some combination of these data can add useful information that leads to a better performance in flood detection.