Combining SAR images with land cover products for rapid urban flood mapping

Synthetic Aperture Radar (SAR) is an indispensable source of data for mapping and monitoring flood hazards, thanks to its ability to image the Earth's surface in all weather conditions and at all times. Through cloud computing platforms such as Google Earth Engine (GEE), SAR imagery can be used in near-real time for rapid flood mapping. This has facilitated the disaster response community to make informed decisions in flood hazard interventions and management plans. However, rapid urban flood mapping using SAR is challenging, due to the complex land cover configuration in urban environments, coupled with complicated backscattering mechanisms. Here, we propose a novel method to utilise SAR imagery and land use-land cover (LULC) products for rapid urban flood mapping. Our approach uses a Land Cover Product to segment the study area into LULC types and differentiate each type with respect to whether double bounce is expected to occur during the flooding events. The normalised difference index was derived using a multi-temporal SAR image stack, and the threshold segmentation method was adopted for flood mapping. In addition, DEM and Surface Water datasets were employed to refine the flood extraction results using a morphological correction approach. We assessed the method quantitatively using two use cases: the 2017 Houston and 2022 Coraki flood events. Based on fine spatial resolution optical imagery, the proposed method achieved an accuracy of 92.7% for the August 2017 Houston flood mapping task and 89% for the March 2022 Coraki flood mapping task, which not only represents at least 13% in accuracy compared to non-LCP based flood extraction method, but also provides strong capability for rapid flood mapping in urban settings.

Automated summary

Synthetic Aperture Radar (SAR) is essential for mapping and monitoring flood hazards. A novel method using SAR imagery and land use-land cover (LULC) products for rapid urban flood mapping is proposed. The method achieved high accuracy in two use cases and provides strong capability for rapid flood mapping in urban settings.