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Volume 34 Issue 3
Jun 2023
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Article Contents
Limei Wang, Guowang Jin, Xin Xiong. Flood Duration Estimation Based on Multisensor, Multitemporal Remote Sensing: The Sardoba Reservoir Flood. Journal of Earth Science, 2023, 34(3): 868-878. doi: 10.1007/s12583-022-1670-9
Citation: Limei Wang, Guowang Jin, Xin Xiong. Flood Duration Estimation Based on Multisensor, Multitemporal Remote Sensing: The Sardoba Reservoir Flood. Journal of Earth Science, 2023, 34(3): 868-878. doi: 10.1007/s12583-022-1670-9

Flood Duration Estimation Based on Multisensor, Multitemporal Remote Sensing: The Sardoba Reservoir Flood

doi: 10.1007/s12583-022-1670-9
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  • Corresponding author: Guowang Jin, guowang_jin@163.com
  • Received Date: 21 Dec 2021
  • Accepted Date: 09 Apr 2022
  • Issue Publish Date: 30 Jun 2023
  • Single-sensor monitoring of flood events at high spatial and temporal resolutions is difficult because of the lack of data owing to instrument defects, cloud contamination, imaging geometry. However, combining multisensor data provides an impressive solution to this problem. In this study, 11 synthetic aperture radar (SAR) images and 13 optical images were collected from the Google Earth Engine (GEE) platform during the Sardoba Reservoir flood event to constitute a time series dataset. Threshold-based and indices-based methods were used for SAR and optical data, respectively, to extract the water extent. The final sequential flood water maps were obtained by fusing the results from multisensor time series imagery. Experiments show that, when compare with the Global Surface Water Dynamic (GSWD) dataset, the overall accuracy and Kappa coefficient of the water body extent extracted by our methods range from 98.8% to 99.1% and 0.839 to 0.900, respectively. The flooded extent and area increased sharply to a maximum between May 1 and May 4, and then experienced a sustained decline over time. The flood lasted for more than a month in the lowland areas in the north, indicating that the northern region is severely affected. Land cover changes could be detected using the temporal spectrum analysis, which indicated that detailed temporal information benefiting from the multisensor data is highly important for time series analyses.

     

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