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dc.contributor.authorColosio, P.
dc.contributor.authorTedesco, M.
dc.contributor.authorTellman, E.
dc.date.accessioned2022-03-31T21:14:27Z
dc.date.available2022-03-31T21:14:27Z
dc.date.issued2022
dc.identifier.citationColosio, P., Tedesco, M., & Tellman, E. (2022). Flood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case over Bangladesh. Remote Sensing.
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs14051180
dc.identifier.urihttp://hdl.handle.net/10150/663858
dc.description.abstractMonitoring floods is a major issue in water resources management and risk mitigation, especially in the Global South. Optical and radar observations, even providing a fine spatial resolution, are still limited by cloud cover interaction or insufficient temporal resolution. On the other hand, passive microwave (PMW) sensors collect information on a daily frequency with minor cloud cover interaction, but they have been historically limited in terms of spatial resolution. Here, we evaluate the capability of an enhanced spatial resolution PMW dataset (3.125 km) in monitoring spatio-temporal evolution of flood events, focusing on a major flood event that occurred in October 2005 in Bangladesh. We apply an algorithm aimed to remove the seasonal variability of surface temperature from the PMW timeseries, exploiting the difference in emissivity between dry and water-covered pixels. We assess the capability of the algorithm in capturing flood evolution and extension through the comparison with quantities obtained from optical data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) and water level measurements. We also compare the enhanced product with the historical coarser resolution dataset by means of a variogram-based analysis to evaluate the improvements in terms of spatial representation. Finally, we evaluate the possibility to extract the water fraction within a single pixel by using an Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) emissivity dataset and compare the estimates with MODIS-derived water fractions. Our results show that the enhanced PMW product outperforms the coarser one when compared to flood mapped from optical data based on information content, indicating that it is possible to integrate such a product into the mapping of floods at a global scale on a daily basis. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.language.isoen
dc.publisherMDPI
dc.rightsCopyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBangladesh
dc.subjectExtreme events monitoring
dc.subjectFlood detection
dc.subjectPassive microwave
dc.titleFlood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case over Bangladesh
dc.typeArticle
dc.typetext
dc.contributor.departmentSchool of Geography, Development, and Environment, University of Arizona
dc.identifier.journalRemote Sensing
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleRemote Sensing
refterms.dateFOA2022-03-31T21:14:27Z


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Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).