Toward improved urban flood detection using Sentinel-1: dependence of the ratio of post- to preflood double scattering cross sections on building orientation

Abstract

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 postflood image due to double scattering between water and adjacent buildings, compared with a preflood image where double scattering is between unflooded ground and buildings. For co-polarized data, if f is the angle between the building wall and the satellite direction of travel, double scattering is strongest for f = 0 deg and falls off as f 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 f = 0 deg but only small at f > 10 deg. Ostensibly, this implies that few DSs might be detected in an urban area. However, experiments on real images have called into question the veracity of the modeling. We describe an empirical study to examine the relationship between the flooded-to-unflooded DS RCS ratio and f in Sentinel-1 (S-1) C-band data. We use high-resolution light detection and ranging and aerial photographs so that f can be measured accurately and is based on S-1 images from flood events that occurred in the United Kingdom during the storms of winter 2019 to 2020. Results indicate that vertical-vertical polarization is better than vertical-horizontal at distinguishing flooded from unflooded DS; that the theoretical model used underestimates the number of DS with high RCS ratios in the f range 10 deg to 30 deg; and that sufficient DS ground heights can be determined to estimate an accurate local average flood level, although in high density housing there are less of these due to the presence of adjacent buildings.