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Time-series analysis of Sentinel-1/2 data for flood detection using a discrete global grid system and seasonal decomposition

Fichtner, Florian Willy and Mandery, Nico and Wieland, Marc and Groth, Sandro and Martinis, Sandro and Riedlinger, Torsten (2023) Time-series analysis of Sentinel-1/2 data for flood detection using a discrete global grid system and seasonal decomposition. International Journal of Applied Earth Observation and Geoinformation, 119, pp. 1-12. Elsevier. doi: 10.1016/j.jag.2023.103329. ISSN 1569-8432.

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Official URL: https://www.sciencedirect.com/science/article/pii/S1569843223001516


Automated flood detection using earth observation data is a crucial task for efficient flood disaster management. Current solutions to identify flooded areas usually rely on calculating the difference between new observations and static, pre-calculated water extents derived by either single acquisitions or timely aggregated products. Such pre-calculated datasets, however, lack representation of real-world seasonality and short-term changes in trend. In this paper we present a complete workflow to automatically detect hydrological extreme events and their spatial extent, which automatically adapts to local seasonality and trend. For that we rely on a novel combination of well-established algorithms and tools to detect anomalies in time-series of water extent across large study areas. The data is binned into a discrete global grid system H3, which greatly simplifies aggregation across spatial and temporal resolutions. For each grid cell of an H3 resolution we perform a time-series decomposition using Seasonal and Trend decomposition using Loess (STL) of the cell’s proportion which is covered with surface water. All cells receive an anomaly score, calculated with extended isolation forest (EIF) on the residuals for each step in time. A burst of anomalies represents a hydrological extreme event like a flood or low water level. The presented methodology is applied on Sentinel-1/2 data for two study areas, one near Sukkur, Pakistan and the other one in Mozambique. The detected anomalies correlate with reported floods and seasonal variations of the study areas. The performance of the process and the possibility to use different H3 resolutions make the proposed methodology suitable for large scale monitoring.

Item URL in elib:https://elib.dlr.de/194951/
Document Type:Article
Title:Time-series analysis of Sentinel-1/2 data for flood detection using a discrete global grid system and seasonal decomposition
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Fichtner, Florian WillyUNSPECIFIEDhttps://orcid.org/0000-0003-2122-1163137131999
Mandery, NicoUNSPECIFIEDhttps://orcid.org/0000-0001-8388-3635137132000
Wieland, MarcUNSPECIFIEDhttps://orcid.org/0000-0002-1155-723XUNSPECIFIED
Groth, SandroUNSPECIFIEDhttps://orcid.org/0000-0002-0499-9072UNSPECIFIED
Martinis, SandroUNSPECIFIEDhttps://orcid.org/0000-0002-6400-361XUNSPECIFIED
Riedlinger, TorstenUNSPECIFIEDhttps://orcid.org/0000-0003-3836-614XUNSPECIFIED
Date:May 2023
Journal or Publication Title:International Journal of Applied Earth Observation and Geoinformation
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Page Range:pp. 1-12
Keywords:Flood detection, H3, Time-series analysis, Seasonal decomposition, Anomaly detection, Sentinel-1, Sentinel-2, Copernicus EMS
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: Fichtner, Florian W.
Deposited On:19 Jun 2023 09:24
Last Modified:19 Oct 2023 10:02

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