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Improving reliability in flood mapping by generating a global seasonal reference water mask using Sentinel-1/2 time-series data

Martinis, Sandro und Groth, Sandro und Wieland, Marc und Rättich, Michaela und Knopp, Lisa (2022) Improving reliability in flood mapping by generating a global seasonal reference water mask using Sentinel-1/2 time-series data. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Seiten 1127-1132. ISPRS Congress 2022, 2022-06-06 - 2022-06-11, Nizza, Frankreich. doi: 10.5194/isprs-archives-XLIII-B3-2022-1127-2022. ISSN 1682-1750.

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Offizielle URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/1127/2022/isprs-archives-XLIII-B3-2022-1127-2022.pdf

Kurzfassung

Variable intra-annual climatic and hydrologic conditions result in many regions of the world in a strong seasonality of the water extent throughout the year. This behaviour, however, is usually not reflected in satellite-based flood emergency mapping. This may lead to non-reliable representations of the flood extent and to misleading information within disaster management activities. In order to be able to separate flooding from normally present seasonal water coverage, up-to-date, high-resolution information on the seasonal water cover is crucial. In this work, we present an automatic methodology to generate a global and consistent permanent and seasonal reference water product based on high resolution Earth Observation data, specifically designed for the use within flood mapping activities. The water masks are primarily based on the time-series analysis of optical Sentinel-2 imagery, which are complemented by Sentinel-1 Synthetic Aperture Radar-based information in data scarce regions. The methodology has been developed based on data of five globally distributed study areas (Australia, Germany, India, Mozambique, and Sudan). Within this work results for Australia and India are demonstrated and are systematically compared with external reference water products. Results show, that by using the proposed product it is possible to give a more reliable picture on flood-affected areas in the frame of disaster response.

elib-URL des Eintrags:https://elib.dlr.de/187276/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Improving reliability in flood mapping by generating a global seasonal reference water mask using Sentinel-1/2 time-series data
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Martinis, Sandrosandro.martinis (at) dlr.dehttps://orcid.org/0000-0002-6400-361XNICHT SPEZIFIZIERT
Groth, SandroSandro.Groth (at) dlr.dehttps://orcid.org/0000-0002-0499-9072NICHT SPEZIFIZIERT
Wieland, MarcMarc.Wieland (at) dlr.dehttps://orcid.org/0000-0002-1155-723XNICHT SPEZIFIZIERT
Rättich, Michaelamichaela.raettich (at) dlr.dehttps://orcid.org/0009-0006-6631-5496NICHT SPEZIFIZIERT
Knopp, LisaLisa.Knopp (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:2022
Erschienen in:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Nein
DOI:10.5194/isprs-archives-XLIII-B3-2022-1127-2022
Seitenbereich:Seiten 1127-1132
ISSN:1682-1750
Status:veröffentlicht
Stichwörter:Flood, Sentinel-1, Sentinel-2, reference water, seasonality
Veranstaltungstitel:ISPRS Congress 2022
Veranstaltungsort:Nizza, Frankreich
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:6 Juni 2022
Veranstaltungsende:11 Juni 2022
Veranstalter :International Society for Photogrammetry and Remote Sensing
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Fernerkundung u. Geoforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Hinterlegt von: Martinis, Sandro
Hinterlegt am:27 Jul 2022 09:56
Letzte Änderung:24 Apr 2024 20:48

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