Abstract/Summary

High resolution Synthetic Aperture Radar (SAR) sensors are now commonly used for flood detection. Automated detection tends to be limited to rural areas owing to the complicated backscattering mechanisms occurring in urban areas. Flooding can be identified in urban areas by searching for increased SAR backscatter in a post-flood image due to double scattering between water and adjacent buildings, compared to that in a pre-flood image where double scattering is between unflooded ground and buildings. For co-polarized data, if φ is the angle between the building wall and the satellite direction of travel, double scattering is strongest for φ = 0° and falls off as φ increases. Theoretical studies estimating the ratio of flooded-to-unflooded double scatterer (DS) radar cross section (RCS) using X-band SAR data, found that the ratio was high at φ = 0° but only small at φ > 10°. Ostensibly this implies that few double scatterers might be detected in an urban area. However, experiments on real images have called into question the veracity of the modelling. This paper describes an empirical study to examine the relationship between the flooded-to-unflooded DS RCS ratio and φ in Sentinel-1 C-band data. The study uses high resolution LiDAR and aerial photographs so that φ can be measured accurately and is based upon Sentinel-1 images from flood events that occurred in the UK during the storms of Winter 2019-20. Results indicate that VV polarization is better than VH at distinguishing flooded from unflooded DS; that the theoretical model used underestimates the number of DS with high RCS ratios in the φ range 10 - 30°; and that sufficient DS ground heights can be determined to estimate an accurate local average flood level, though in high density housing there are less of these due to the presence of adjacent buildings.