An exploratory study of Sentinel-1 SAR for rapid urban flood mapping on Google Earth Engine

Abstract

Real-time, near-real-time, and accurate flood extent information is critical for emergency response during disaster events such as floods. Accurate extents are critical for disaster management and relief efforts. Despite multiple efforts, there are still many challenges in automated processing of Sentinel-1 SAR to generate reliable inundation maps. The major advantage of SAR compared to optical imagery is its data collection capability despite any weather conditions even thick cloud situation. Currently, there is a knowledge gap of employing different polarization combinations of SAR imagery for flooding research. First, ten different combinations of the two original VH and VV polarizations are designed for rapid and accurate urban flood mapping. To examine the significant potentials of the polarization combinations for flood mapping, four flood mapping methods namely threshold, change detection, unsupervised and supervised classification, in combination with a zero-depth flood method, are designed and used to map flood extents. Among different polarization combinations, the multiplication, squared multiplication, addition, and squared addition combinations have resulted in good results for flood extent mapping. In addition, a flood depth estimation approach has been used to address the overestimation of urban flooded areas. In all four methods, the deduction of overestimated flooded areas using the threshold of zero flood depth has improved the overall accuracy on average 7 % for all methods. The results show that all four methods implemented on Google Earth Engine are good using different combinations to identify flooded areas but change detection method requires little user involvement, and this can be applied to new study areas without estimating flooding depth for the affected areas. Whereas the supervised classification will need more user’s involvement to collect sample points. Among all the combinations, squared addition of polarizations has been consistently performed well for all methods. All the analysis has been done on the Google Earth Engine platform, and this strategy can be used to map flood in any urban environment. The finding of this study will enhance local governments and federal agencies rapid assessment of flooding disasters and making accurate decisions.