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Published VersionCC BY V4.0, Open Access
Journal article
Flood Modeling and Prediction Using Earth Observation Data
Surveys in Geophysics, Vol.44, pp.1553-1578
2023
Abstract
The ability to map floods from satellites has been known for over 40 years. Early images of floods were rather difficult to obtain, and flood mapping from satellites was thus rather opportunistic and limited to only a few case studies. However, over the last decade, with a proliferation of open-access EO data, there has been much progress in the development of Earth Observation products and services tailored to various end-user needs, as well as its integration with flood modeling and prediction efforts. This article provides an overview of the use of satellite remote sensing of floods and outlines recent advances in its application for flood mapping, monitoring and its integration with flood models. Strengths and limitations are discussed throughput, and the article concludes by looking at new developments.
Details
- Title
- Flood Modeling and Prediction Using Earth Observation Data
- Authors
- Guy Schumann (Corresponding Author) - University of BristolLaura Giustarini (Author) - RSS-HydroAngelica Tarpanelli (Author) - National Research CouncilBen Jarihani (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringSandro Martinis (Author) - German Remote Sensing Data Centre
- Publication details
- Surveys in Geophysics, Vol.44, pp.1553-1578
- Publisher
- Springer Netherlands
- Date published
- 2023
- DOI
- 10.1007/s10712-022-09751-y
- ISSN
- 1573-0956; 0169-3298
- Copyright note
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99697898202621
- Output Type
- Journal article
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