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Article

Flood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case over Bangladesh

1
Department of Civil, Environmental, Architectural Engineering and Mathematics, University of Brescia, Via Branze 43, 25123 Brescia, Italy
2
Lamont-Doherty Earth Observatory, Columbia University, 61 Rte 9W, Palisades, NY 10964, USA
3
NASA Goddard Institute of Space Studies, 2880 Broadway, New York, NY 10027, USA
4
School of Geography, Development, and Environment, University of Arizona, 1064 E. Lowell Street, Tucson, AZ 85721, USA
5
Cloud to Street, New York, NY 11201, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Renaud Hostache
Remote Sens. 2022, 14(5), 1180; https://doi.org/10.3390/rs14051180
Received: 18 January 2022 / Revised: 21 February 2022 / Accepted: 22 February 2022 / Published: 27 February 2022
Monitoring 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. View Full-Text
Keywords: flood detection; passive microwave; Bangladesh; extreme events monitoring flood detection; passive microwave; Bangladesh; extreme events monitoring
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MDPI and ACS Style

Colosio, P.; Tedesco, M.; Tellman, E. Flood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case over Bangladesh. Remote Sens. 2022, 14, 1180. https://doi.org/10.3390/rs14051180

AMA Style

Colosio P, Tedesco M, Tellman E. Flood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case over Bangladesh. Remote Sensing. 2022; 14(5):1180. https://doi.org/10.3390/rs14051180

Chicago/Turabian Style

Colosio, Paolo, Marco Tedesco, and Elizabeth Tellman. 2022. "Flood Monitoring Using Enhanced Resolution Passive Microwave Data: A Test Case over Bangladesh" Remote Sensing 14, no. 5: 1180. https://doi.org/10.3390/rs14051180

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