The accuracy of sequential aerial photography and SAR data for observing urban flood dynamics, a case study of the UK summer 2007 floods

In this paper we examine, for the first time, the potential of remote sensing to monitor flood dynamics in urban areas and constrain mathematical models of these processes. This is achieved through the development of a unique data set consisting of a series of eight space-borne synthetic aperture radar (SAR) and aerial photographic images of flooding of the UK town of Tewkesbury acquired over an eight day period in summer 2007. Previous observations of urban flooding have used single image and wrack mark data and have therefore been unable to adequately chart the propagation and recession of flood waves through complex urban topography. By using a combination of space-borne radar and aerial imagery we are able to show that remotely sensed imagery, particularly from the new TerraSAR-X radar, can reproduce dynamics adequately and support flood modelling in urban areas. We illustrate that image data from different remote sensing platforms reveal sufficient information to distinguish between models with varying degrees of channel–floodplain connectivity, particularly toward the end of the recession phase of the event. For this test case, our results also show that high resolution SAR imagery even when acquired from satellites can reveal important hydraulic characteristics difficult to simulate with current dynamic flood models. Hence, it is established, at least for this test case and event, that SAR imagery from as far as several hundred kilometers from the Earth's surface can deliver important information about floodplain dynamics that can be used to identify and help build suitable models, even in built-up environments.

Research highlights
► For the first time we assess remote sensing to monitor urban flood dynamics.
► Satellites deliver important information about urban floodplain dynamics.
► High resolution images reveal important hydraulic characteristics missing in models.
► This allows to distinguish between models with varying degrees of connectivity.
► It also helps improve suitable flood models where most flood risks are.