Estimating ensemble likelihoods for the Sentinel-1 based Global Flood Monitoring product of the Copernicus Emergency Management Service [* Cross-Reference *]

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Christian Krullikowski; Candace Chow; Marc Wieland; Sandro Martinis; Bernhard Bauer-Marschallinger; Florian Roth; Patrick Matgen; Marco Chini; Yu Li; Peter Salamon, 2026, "Estimating ensemble likelihoods for the Sentinel-1 based Global Flood Monitoring product of the Copernicus Emergency Management Service [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.UH1ANL, LIST Open Repository, V1

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Description	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. (2023-04-28) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.36227/techrxiv.22688101.v1 for the original and latest version of the publication* (2026-07-01)
Subject	Astronomy and Astrophysics; Earth and Environmental Sciences; Physics
Keyword	Flood myth, Computer science, Robustness (evolution), Synthetic aperture radar, Flooding (psychology), Ensemble forecasting, Data mining, Environmental science, Remote sensing, Geography, Machine learning, Artificial intelligence
License/Data Use Agreement	CC BY 4.0


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Persistent Identifier	perma:LIST.UH1ANL
Publication Date	2026-07-06
Title	Estimating ensemble likelihoods for the Sentinel-1 based Global Flood Monitoring product of the Copernicus Emergency Management Service [* Cross-Reference *]
Other Identifier	https://doi.org/10.36227/techrxiv.22688101.v1 OpenAlex ID: https://openalex.org/W4367307730
Author	Christian Krullikowski (Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)) - ORCID: https://orcid.org/0000-0001-8717-692X Candace Chow (Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)) - ORCID: https://orcid.org/0000-0003-3716-0924 Marc Wieland (Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)) - ORCID: https://orcid.org/0000-0002-1155-723X Sandro Martinis (Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)) - ORCID: https://orcid.org/0000-0002-6400-361X Bernhard Bauer-Marschallinger (TU Wien, Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)) - ORCID: https://orcid.org/0000-0001-7356-7516 Florian Roth (TU Wien, Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)) - ORCID: https://orcid.org/0000-0002-8589-0182 Patrick Matgen (Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0001-6668-4693 Marco Chini (Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0002-9094-0367 Yu Li (Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Luxembourg Institute of Science and Technology) - ORCID: https://orcid.org/0000-0003-1818-6643 Peter Salamon (Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), Joint Research Centre) - ORCID: https://orcid.org/0000-0002-5419-5398
Point of Contact	Use email button above to contact. LIST QDKM (LIST)
Description	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. (2023-04-28) *This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.36227/techrxiv.22688101.v1 for the original and latest version of the publication* (2026-07-01)
Subject	Astronomy and Astrophysics; Earth and Environmental Sciences; Physics
Keyword	Flood myth Computer science Robustness (evolution) Synthetic aperture radar Flooding (psychology) Ensemble forecasting Data mining Environmental science Remote sensing Geography Machine learning Artificial intelligence
Topic Classification	Flood Risk Assessment and Management Precipitation Measurement and Analysis Meteorological Phenomena and Simulations
Deposit Date	2023-04-28
Data Type	Preprint

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