Methodology for Mapping Flood Extent on Estonian Floodplains

Sipelgas, Liis; Aavaste, Age; Uiboupin, Rivo; Rikka, Sander

doi:10.1109/igarss39084.2020.9324369

Cited by 1 publication

(3 citation statements)

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“…We evaluated the sensitivity of S1 polarizations for land and water discrimination [52,53]. The overall backscatter differences resulting from the sensitivity analysis are summarized in Table 4.…”

Section: Sentinel-1 Data Processingmentioning

confidence: 99%

“…The dataset of open water backscattering from known waterbodies was collected from 7 IW and 12 EW mode images acquired at the time of flooding in October, November, December, and April in three consecutive years (2017)(2018)(2019). The relationships between the local incidence angle (θ) and surface water backscattering (σ 0 ) for polarizations with the greatest sensitivity (Table 4 in the previous section) are shown in Figure 2a (original source of data [52]). An established relationship was used for the water mapping algorithm (Table 5) dependency.…”

Section: Sentinel-1 Data Processingmentioning

confidence: 99%

“…Summary of sensitivity analysis for land and water discrimination for different S1 polarizations[52].…”

mentioning

confidence: 99%

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Mapping Flood Extent and Frequency from Sentinel-1 Imagery during the Extremely Warm Winter of 2020 in Boreal Floodplains and Forests

2021

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The current study presents a methodology for water mapping from Sentinel-1 (S1) data and a flood extent analysis of the three largest floodplains in Estonia. The automatic processing scheme of S1 data was set up for the mapping of open-water flooding (OWF) and flooding under vegetation (FUV). The extremely mild winter of 2019/2020 resulted in several large floods at floodplains that were detected from S1 imagery with a maximal OWF extent up to 5000 ha and maximal FUV extent up to 4500 ha. A significant correlation (r2 > 0.6) between the OWF extent and the closest gauge data was obtained for inland riverbank floodplains. The outcome enabled us to define the water level at which the water exceeds the shoreline and flooding starts. However, for a coastal river delta floodplain, a lower correlation (r2 < 0.34) with gauge data was obtained, and the excess of river coastline could not be related to a certain water level. At inland riverbank floodplains, the extent of FUV was three times larger compared to that of OWF. The correlation between the water level and FUV was <0.51, indicating that the river water level at these test sites can be used as a proxy for forest floods. Relating conventional gauge data to S1 time series data contributes to flood risk mitigation.

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Section: Sentinel-1 Data Processingmentioning

confidence: 99%

Section: Sentinel-1 Data Processingmentioning

confidence: 99%