학술논문

Estimating Ensemble Likelihoods for the Sentinel-1-Based Global Flood Monitoring Product of the Copernicus Emergency Management Service
Document Type
Periodical
Source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of. 16:6917-6930 2023
Subject
Geoscience
Signal Processing and Analysis
Power, Energy and Industry Applications
Floods
Classification algorithms
Uncertainty
Monitoring
Detection algorithms
Optical sensors
Emergency services
Copernicus Emergency Management Service (CEMS)
Earth observation
ensemble classification
flood monitoring
likelihoods
radar
uncertainties
sentinel-1
Language
ISSN
1939-1404
2151-1535
Abstract
The Global Flood Monitoring (GFM) system of the Copernicus Emergency Management Service 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.