Rapid flood mapping: Fusion of Synthetic Aperture Radar flood extents with flood hazard maps

Rigorous flood monitoring by ICEYE is enabled by the large-scale and systematic availability of synthetic aperture radar (SAR) data from the satellite constellation deployed and operated by ICEYE [1, 2]. However, in dense urban areas and under tree canopy cover, using single X-band based SAR images directly for rapid flood detection inherits large uncertainties due to its complex backscattering mechanisms. This study addresses this gap by proposing an approach to rapidly detect flooding in urban areas by merging real-time SAR flood extents from surrounding rural areas with hydrodynamically modeled flood hazard maps. If a flood is fully contained within an urban area, other auxiliary flood evidences are merged with JBA’s high resolution global flood hazard maps at 5 and 30m resolution.  The precomputed simulation library approach used in Mason et al. 2021 appeared as a challenge, as floods are dynamic in nature [3], they suggested the benefits of using assimilation to integrate SAR data and model outputs in dynamic situations. Thus, the proposed approach builds upon Mason et al. 2021[3] and the framework for improved near real-time flood mapping [2], wherein SAR data is assimilated to enhance future flood predictions and improve the quality of flood hazard maps. This process, in turn, enhances further real-time rapid flood mapping aiding governments, NGOs and disaster responder to make accurate timely decisions in the immediate aftermath of an event.  References:[1] Dupeyrat, A., Almaksour, A., Vinholi, J., and Friberg, T.: Deep learning for automatic flood mapping from high resolution SAR images, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6790, https://doi.org/10.5194/egusphere-egu23-6790, 2023.[2] Friberg, T., Khadka, A., and Dupeyrat, A.: A framework for improved near real-time flood mapping, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8520, https://doi.org/10.5194/egusphere-egu23-8520, 2023.[3] Mason, D.C., Bevington, J., Dance, S.L., Revilla-Romero, B., Smith, R., Vetra-Carvalho, S., Cloke, H.L.: Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps, Water 2021, 13, 1577, https://doi.org/10.3390/w13111577