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Sentinel-1-based flood mapping: a fully automated processing chain

Twele, André and Cao, Wenxi and Plank, Simon and Martinis, Sandro (2016) Sentinel-1-based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37 (13), pp. 2990-3004. Taylor & Francis. doi: 10.1080/01431161.2016.1192304. ISSN 0143-1161.

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This article presents an automated Sentinel-1-based processing chain designed for flood detection and monitoring in near-realtime (NRT). Since no user intervention is required at any stage of the flood mapping procedure, the processing chain allows derivinging time-critical disaster information in less than 45 min after a new data set is available on the Sentinel Data Hub of the European Space Agency (ESA). Due to the systematic acquisition strategy and high repetition rate of Sentinel-1, the processing chain can be set up as a web-based service that regularly informs users about the current flood conditions in a given area of interest. The thematic accuracy of the thematic processor has been assessed for two test sites of a flood situation at the border between Greece and Turkey with encouraging overall accuracies between 94.0% and 96.1% and Cohen’s kappa coefficients (κ) ranging from 0.879 to 0.910. The accuracy assessment, which was performed separately for the standard polarizations (VV/VH) of the interferometric wide swath (IW) mode of Sentinel-1, further indicates that under calm wind conditions, slightly higher thematic accuracies can be achieved by using VV instead of VH polarization data.

Item URL in elib:https://elib.dlr.de/102476/
Document Type:Article
Title:Sentinel-1-based flood mapping: a fully automated processing chain
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Twele, Andréandre.twele (at) dlr.dehttps://orcid.org/0000-0002-8035-2625
Cao, Wenxiwenxi.cao (at) dlr.dehttps://orcid.org/0000-0001-9567-3053
Plank, Simonsimon.plank (at) dlr.dehttps://orcid.org/0000-0002-5793-052X
Martinis, Sandrosandro.martinis (at) dlr.dehttps://orcid.org/0000-0002-6400-361X
Date:28 June 2016
Journal or Publication Title:International Journal of Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1080/01431161.2016.1192304
Page Range:pp. 2990-3004
Publisher:Taylor & Francis
Keywords:SAR; floods; automated; classification; Sentinel-1
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben Zivile Kriseninformation und Georisiken (old)
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Twele, Andre
Deposited On:29 Jan 2016 11:19
Last Modified:14 Jul 2016 12:31

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