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The use of Sentinel-1 time-Series data to improve flood Monitoring in arid areas

Martinis, Sandro and Plank, Simon Manuel and Cwik, Kamila (2018) The use of Sentinel-1 time-Series data to improve flood Monitoring in arid areas. Remote Sensing, 10 (582), pp. 1-13. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs10040583. ISSN 2072-4292.

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Official URL: http://www.mdpi.com/2072-4292/10/4/583/pdf


Due to the similarity of the radar backscatter over open water and over sand surfaces a reliable near real-time flood mapping based on satellite radar sensors is usually not possible in arid areas. Within this study, an approach is presented to enhance the results of an automatic Sentinel-1 flood processing chain by removing overestimations of the water extent related to low-backscattering sand surfaces using a Sand Exclusion Layer (SEL) derived from time-series statistics of Sentinel-1 data sets. The methodology was tested and validated on a flood event in May 2016 at Webi Shabelle River, Somalia and Ethiopia, which has been covered by a time-series of 202 Sentinel-1 scenes within the period June 2014 to May 2017. The approach proved capable to significantly improve the classification accuracy of the Sentinel-1 flood service within this study site. The Overall Accuracy increased by ~5% to a value of 98.5%, the User’s Accuracy by 25.2% to a value of 96.0%. Experimental results have shown that the classification accuracy is influenced by several parameters such as the lengths of the time-series used for generating the SEL.

Item URL in elib:https://elib.dlr.de/119660/
Document Type:Article
Title:The use of Sentinel-1 time-Series data to improve flood Monitoring in arid areas
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Martinis, Sandrosandro.martinis (at) dlr.dehttps://orcid.org/0000-0002-6400-361X
Plank, Simon ManuelSimon.Plank (at) dlr.dehttps://orcid.org/0000-0002-5793-052X
Date:April 2018
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs10040583
Page Range:pp. 1-13
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:SAR; water bodies; inundation; flood detection; Sentinel-1; time-series; sand surfaces; arid areas
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 - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Martinis, Sandro
Deposited On:23 Apr 2018 14:06
Last Modified:14 Dec 2019 04:26

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