Estimating ensemble likelihoods for the Sentinel-1 based Global Flood
Monitoring product of the Copernicus Emergency Management Service
Abstract
The Global Flood Monitoring (GFM) system of the Copernicus Emergency
Management Service (CEMS) addresses the challenges and impacts that are
caused by flooding. The GFM system provides global, near-real time flood
extent masks for each newly acquired Sentinel-1 Interferometric Wide
Swath Synthetic Aperture Radar (SAR) image, as well as flood information
from the whole Sentinel-1 archive from 2015 on. The GFM flood extent is
an ensemble product based on a combination of three independently
developed flood mapping algorithms that individually derive the flood
information from Sentinel-1 data. Each flood algorithm also provides
classification uncertainty information that is aggregated into the GFM
ensemble likelihood product as the mean of the individual classification
likelihoods. As the flood detection algorithms derive uncertainty
information with different methods, the value range of the three input
likelihoods must be harmonized to a range from low [0] to high
[100] flood likelihood. The ensemble likelihood is evaluated on two
test sites in Myanmar and Somalia, showcasing the performance during an
actual flood event and an area with challenging conditions for SAR-based
flood detection. The Myanmar use case demonstrates the robustness if
flood detections in the ensemble step disagree and how that information
is communicated to the end-user. The Somalia use case demonstrates a
setting where misclassifications are likely, how the ensemble process
mitigates false detections and how the flood likelihoods can be
interpreted to use such results with adequate caution.