The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Multi‐temporal synthetic aperture radar flood mapping using change detection
Open accessAbstract
A change detection and thresholding methodology has been adapted from previous studies to determine the extent of flooding for 13 Sentinel‐1 synthetic aperture radar images captured during the floods of winter 2015–2016 in Yorkshire, UK. Both available polarisations, VH and VV, have been processed to allow for a comparison of their respective accuracy for delineating surface water. Peak flood extents are found on 29 December 2015 during the aftermath of storms Eva and Frank. Results have been validated against a Sentinel‐2 optical image, with both polarisations producing a total accuracy of 97%. Of the two polarisations, VV produces fewer misclassifications, mirroring the similar results reported in previous research. Mapped results are compared to the Environment Agency Flood Maps for Planning (EA FMP), with good correlation observed for inundation on the floodplains. Differences occur away from the floodplains, with the satellite data identifying pluvial flooding not highlighted by the EA FMP.
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Cites
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Cited by
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
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Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation mapping using deep learning
Konapala, G., Kumar, S. V., & Khalique Ahmad, S. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.08.016 · Cited by 209
Psomiadis, E., Diakakis, M., & Soulis, K. X. (2020) · Remote Sensing · doi:10.3390/rs12233980 · Cited by 61
Metadata
- Authors
- Clement, M. A., Kilsby, C. G., & Moore, P.
- Venue
- Journal of Flood Risk Management
- Publisher
- Wiley
- Published
- 2017-04-27
- Volume
- 11
- Issue
- 2
- Pages
- 152-168
- ISSN
- 1753-318X (ISSN-L)
- OpenAlex
- W2604199742
- Cited by
- 403
- References
- 24
- FWCI
- 20.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
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