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A comparison of global flood models using Sentinel-1 and a change detection approach
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.ORCID iD: 0009-0002-8140-6602
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering.ORCID iD: 0000-0002-7575-8989
2024 (English)In: Natural Hazards, ISSN 0921-030X, E-ISSN 1573-0840Article in journal (Refereed) Published
Abstract [en]

Advances in numerical algorithms, improvement of computational power and progress in remote sensing have led to the development of global flood models (GFMs), which promise to be a useful tool for large-scale flood risk management. However, performance and reliability of GFMs, especially in data-scarce regions, is still uncertain, as they are difficult to validate. Here we aim at contributing to develop alternative, more flexible, and consistent methods for GFM validation by applying a change detection analysis on synthetic aperture radar (CD-SAR) imagery obtained from the Sentinel-1 imagery, on a cloud-based geospatial analysis platform. The study addresses two main objectives. First, to validate four widely adopted GFMs with flood maps generated through the proposed CD-SAR approach. This exercise was conducted for eight different large river basins on four continents, to account for a diverse range of hydro-climatic environments. Second, to compare CD-SAR-derived flood maps with those obtained from alternative remote sensing sources. These comparative results offer valuable insights into the reliability of CD-SAR data as a validation tool, more specifically how it stacks up against flood maps generated by other remote sensing techniques.

Place, publisher, year, edition, pages
Springer Nature , 2024.
National Category
Other Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:kth:diva-346950DOI: 10.1007/s11069-024-06629-7ISI: 001220729900002Scopus ID: 2-s2.0-85192526255OAI: oai:DiVA.org:kth-346950DiVA, id: diva2:1861092
Funder
KTH Royal Institute of Technology
Note

QC 20240529

Available from: 2024-05-27 Created: 2024-05-27 Last updated: 2024-05-29Bibliographically approved

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Brandimarte, Luigia

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