All-Weather Flood Mapping Using a Synergistic Multi-Sensor Downscaling Framework: Case Study for Brisbane, Australia

Highlights:
What are the main findings?
- High temporal resolution, low spatial resolution space-based remote sensors can provide valuable insights during densely clouded flood events.
- Data sources of disparate spatial resolutions can be harmonized and integrated using topography.

What are the implications of the main findings?
- By successfully harmonizing free, public sensor data in a multi-sensor framework, this method offers a viable, cost-effective alternative for rapid flood mapping in resource-constrained scenarios.

Abstract: Despite a growing number of Earth Observation satellites, a critical observational gap persists for timely, high-resolution flood mapping, primarily due to infrequent satellite revisits and persistent cloud cover. To address this issue, we propose a novel framework that synergistically fuses complementary data from three public sensor types. Our methodology harmonizes these disparate data sources by using surface water fraction as a common variable and downscaling them with flood susceptibility and topography information. This allows for the integration of sub-daily observations from the Visible Infrared Imaging Radiometer Suite and the Advanced Himawari Imager with the cloud-penetrating capabilities of the Advanced Microwave Scanning Radiometer 2. We evaluated this approach on the February 2022 flood in Brisbane, Australia using an independent ground truth dataset. The framework successfully compensates for the limitations of individual sensors, enabling the consistent generation of detailed, high-resolution flood maps. The proposed method outperformed the flood extent derived from commercial high-resolution optical imagery, scoring 77% higher than the Copernicus Emergency Management Service (CEMS) map in the Critical Success Index. Furthermore, the True Positive Rate was twice as high as the CEMS map, confirming that the proposed method successfully overcame the cloud cover issue. This approach provides valuable, actionable insights into inundation dynamics, particularly when other public data sources are unavailable.