Accuracy Assessment of Extracting Flooded Areas Caused by Typhoon No. 19 of 2019 Using Sentinel-1 Data

Typhoon 19 of 2019 hit Koriyama City in Fukushima Prefecture, Japan, on October 13, 2019. The outflow of the rivers caused flood damage to built-up areas in the city center, and rice production was also damaged because rice paddies were flooded just before harvest time. This study applied a learning-based method to detect flooding in both built-up and rice paddy areas by using changes in backscattering coefficients before and during the flood based on Sentinel-1 synthetic aperture radar (SAR) data. Both built-up areas and rice paddies damaged by Typhoon 19 were used for training and test data. We used changes in these SAR data for training and used a support vector machine (SVM) as a classifier to detect flood damaged areas. The combination of changes in backscattering coefficients and texture (entropy) information improved the accuracy of flood detection by a kappa coefficient of 0.15, compared with backscattering-only input. In addition, a comparison of F values in each category in the results of dual and single polarization demonstrated that VV polarization improved the accuracy of extracting data on flooded built-up areas, while VH polarization improved data extraction for flooded rice paddy areas.