Improved urban flood detection using Sentinel-1 by effective combination of elevation data with image intensity and interferometric coherence

Flooding of urban areas causes great risk to lives and property. It may be detected using high-resolution synthetic aperture radar (SAR) sensors by measuring changes in double scattering and/or interferometric coherence between pre- and postflood images. Double scattering in a postflood image usually increases compared with a preflood image, whereas coherence generally decreases. Less attention has been paid to change detection techniques that use a high-resolution digital surface model (DSM) in addition to double scattering and coherence. The availability of a DSM may enable increased spatial resolution in the flood extent delineation and allow flood depth (useful for flood damage assessment) to be measured as well as extent. We aim to investigate how best to combine double scattering, coherence, and DSM data for urban flood mapping using change detection. Four urban floods from Asia and Europe were studied. Due to the limited training data available in these floods, a shallow learning approach was adopted, using separate training and test datasets. It was found that when classifying using double scattering and coherence only, an approach using a neural network (NN) was marginally superior to an established method. The DSM was applied to the resulting NN classification in a second-stage process. The results indicated that using the NN employing only double scattering and coherence, a weighted average classification accuracy of 84% could be achieved on test data. If the DSM was also included in the classification, the accuracy increased to 91%, spatial resolution improved, and flood depth maps could generally be generated. These findings should be of use in automating the detection of urban flooding as an aid to operational flood incident management and flood forecasting.