東北地域災害科学研究

第58巻（2022）

99

Estimation of floodwater depth in Quang Tri province using Sentinel-1 data
Vuong Tai Chi1, Dinh Nhat Quang2,*, Nguyen Xuan Tinh3, Hitoshi Tanaka3

Abstract
Floodwater depth is an essential parameter to assess the flood damages and release the prompt
decisions to mitigate severe consequences. In the context of climate change, water-related disaster becomes
more frequent and severe; therefore, rapid estimation of floodwater depth plays a vital role in disaster
response and damage mitigation. In recent years, the approach of integrating Sentinel-1 data that isindependent of weather conditions and GEE, a cloud-based platform attracts many researchers in flood extent
and floodwater depth assessment. In this paper, the authors apply a Google Earth Engine implementation of
the Floodwater Depth Estimation Tool (FwDET-GEE) algorithm on the Sentinel-1 images to detect the floodextent extraction and DEM accuracy improvement for floodwater depth estimation. The authors also use GEE
to conduct further analysis about flood damages based on the estimated floodwater level dataset and thecloud-based datasets of population, cropland, and urban area. The results reveal that 0.5m floodwater depth
has covered an area of 19,000 ha, and 3m floodwater depth covered an area of 800 ha in the Hai Lang district.
Furthermore, the 2020 flood event has respectively impacted 9,100 ha of cropland and 360 ha of the urbanarea.
1. Introduction
Flood is a phenomenon causing massive catastrophic impacts on humans and nature. In recent years,
extreme floods have been more frequent and severe on the Central Coast of Vietnam. Quang Tri province
has diverse water resources with a dense river network, composed of 3 main river basins with a total
catchment area of more than 4,000 km2 and more than 600,000 residents (Quang Tri Province, n.d.).
Therefore, rapid floodwater depth assessment is crucial for mitigating flood risk on people andinfrastructures.
Recently, satellite-based Remote Sensing Technology has been shown as a valuable dataset for floodrisk and depth assessment (Abrishamchi et al., 2012; Anusha and Bharathi, 2020; Do et al., 2019; Quang etal., 2021; Vuong Tai Chi et al., 2020). Among available satellite data, Sentinel-1 Synthetic Aperture Radar(SAR) data has become a more attractive data resource for flood mapping and damage assessment (Huangand Jin, 2020). This is because Sentinel-1 SAR provides Level-1 Ground Range Detected products
independent of weather conditions, especially clouds. For this reason, Sentinel-1 data can even be used toanalyze under cloudy conditions during flood events, which is a huge difference compared to the Optical
Satellite data. Moreover, Google Earth Engine (GEE), a cloud-based image processing platform, appears asa high-performance tool to access numerous satellite images and process a large scale of geospatial data
(Gorelick et al., 2017). This approach helps reduce significant time intervals for data processing compared todesktop-based tools, which require a considerable duration for data downloading and processing (Tiwari etal., 2020).
The FwDET-GEE tool, developed by Peter et al.(2020), appears to be an effective and reliable tool
for floodwater depth estimation implemented on the GEE platform. It uses flood extent layer and DEM data
1Former student of Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam
Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam
3Tohoku University, 6-6-06 Aoba, Sendai 980-8579, Japan*Corresponding author2100as its main inputs. Peter et al.(2020) evaluated the results from FwDET-GEE with iRIC-FaSTMETCH
hydrodynamics model. They achieved an RMSE up to 0.7m and sufficient good to apply for floodwater depthestimation.
This paper aims to integrate FwDET-GEE with Sentinel-1 data for floodwater depth estimation on
the GEE platform. Further assessment about exposed people, affected cropland, and urban areas are also
conducted to obtain an overview of flood damages in Quang Tri province.
2. Case study and data collection
2.1. Case study
Quang Tri province is located on the North
Central coast of Vietnam with a total area of 3,469 km2
and 75km of coastline (Quang Tri Statistics Office,2015). The province has diverse and complex terrain,comprising wide sand dunes, a deep delta area along thecoast, and an increasingly high elevation towards theWest. The high mountainous area is about 100km awayfrom the sea. Quang Tri province has a dense river
network with 17 primary rivers and 13 secondary ones,
supplying water for more than 4000km2 river basin(NewsTech and SWS Consulting Engineering S.r.l(Italy), 2019). These topography characteristics makethe province vulnerable to storms with very highlandslide risk in the West and heavy floods in the East.
Figure 1. Administrative map of Quang Tri
Quang Tri province is a very attractive tourist
province
destination with many famous historical andrevolutionary sites. Moreover, the province is a part ofthe economic center of the region and the East-West Economic Corridor (EWEC), offering multipleopportunities for economic development and international economic integration (Vietnam Chamber ofCommerce and Industry (VCCI), 2017). However, these economic potentials are annually being threatenedby tropical storms. For instance, Quang Tri has directly suffered at least 15 tropical storms in the last threedecades and three severe storms in 2020. Especially, the Molave tropical storm, hitting Quang Tri province
from 26 October 2020, has caused extreme floods and landslides and forced thousands of people to excavate
their homes.
2.2. Data collection
Sentinel-1 data provides Level-1 Ground Range Detected (GRD) products, consisting of the SAR
data, with the advantage of operating at wavelengths without being affected by cloud cover or lack ofillumination (European Space Agency, n.d.). Sentinel-1 data provides multiple polarizations, including VH(Vertical transmit – Horizontal receive) and VV (Vertical transmit – Vertical receive) polarizations, which
provide more information on inundated areas than that in single-polarized radar. In this study, two Sentinel1 GRD scenes with a resolution of 10m are used, focusing on the flood event on 28 October 2020 caused by
the tropical storm Molave (Table 1).
Table 1. List of Sentinel-1 images used in the study
ProductDatePolarization
S1_GRD/S1B_IW_GRDH_1SDV_20201028T224307_20201028T2243
28-Oct-20
VH + VV
32_024019_02DA78_5453
S1_GRD/S1B_IW_GRDH_1SDV_20201028T224332_20201028T2243
28-Oct-20
VH + VV
57_024019_02DA78_486D

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Digital elevation model (DEM) represents elevation data to represent the Earth’s topographic surface.
In this study, the 30m-resolution SRTM (Shuttle Radar Topography Mission) is used as an important part ofthefloodwater
depthestimation
process(https://developers.google.com/earthengine/datasets/catalog/USGS_SRTMGL1_003).
In addition, the assessment of flood damages are estimated based on the 100m-resolution dataset of
population statistics, and 500m-resolution dataset of cropland and urban area in Quang Tri province in 2020
from WorldPop (https://www.worldpop.org/) and Moderate Resolution Imaging Spectroradiometer(MODIS) image, respectively.
3. Methodology
The procedure for floodwater depth estimation follows three main steps: i) flood extent extraction;ii) DEM accuracy improvement; iii) floodwater depth estimation (Figure 2).
For flood extent extraction, the authors based on the ratio between pixel values of the during-floodevent image and the before one to produce the temporal water layer. The generated temporary water layer
includes floodwater and the water resulting from radar error on mountainous areas. In GEE, this error isassigned to low values, corresponding to the values of water. In this context, the areas having a slope ofgreater than 5% are removed based on the DEM layer from the HydroSHEDS data, also available in GEE.
This procedure helps to remove the non-correct pixels and retrieve only the floodwater layer. Within theprocess of floodwater extraction, the removal of permanent water is not conducted since the permanent water
is included in both before and during-flood-event images.

Figure 2. Flowchart of the methodology for floodwater depth estimation
Two filtering processes are used to improve the accuracy of DEM data. The first process is applied
for the entire DEM layer using a low-pass filter with a 3×3 square kernel and the modified z-score. Eachpixel with an outlier value is detected and replaced by the median of the surrounding eight pixels. The second
process is similar but applied for DEM pixels nearest the floodwater surface boundary pixels.
After the DEM improvement, the boundary pixel elevations of flood extent are extracted based oncorresponding pixels of the DEM layer. These values are assigned to the nearest pixels to the flood extent
boundary by the focal statistic method with the condition of choosing the smallest neighborhood value. This
process is conducted until assigned to all pixels within the floodwater surface. For floodwater depth
estimation, the elevation of floodwater surface is subtracted from corresponding pixel elevations of DEM.
After using a low-pass filter to calculate the average value for each pixel based on its 3×3 neighborhood, thefinal floodwater depth layer is retrieved. The estimated floodwater depth is validated using the 2Dhydrodynamic model – MIKE 21.

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4. Results and discussion
4.1. Flood extent and floodwater depth of the flood event on 28 October 2020
Figure 3 shows the results of flood extent extraction with a total area of more than 19,000 ha of
Quang Tri province during the flood event on 28 October 2020. From Figure 3a, floodwater was mainly inthe low-elevation region along the coast and mountainous area. Hai Lang district has the largest inundated
area with nearly 8,000 ha and accounts for 50% of the whole inundated area. The Quang Tri floodwater depth
results in Figure 3b reveal mainly less than 0.5m and covered around 10,500 ha. A 3m floodwater depth
covered more than 400ha of the Hai Lang district.

a) Flood extent mapb) Inundation map
Figure 3. Flood extent and floodwater depth of the flood event on 28 October 2020 in Quang Tri province
4.2. Flood damage assessment of flood event on 28 October 2020
Based on the flood extent boundary, the layers of the population, cropland, and urban area are
extracted on GEE to assess the flood damages during the target flood event. The estimation reveals that about
54,000 people in Quang Tri province were exposed to floods, in which Vinh Linh and Dong Ha were two
districts with the highest affected population density (Figure 4a). In addition, the region with the deepest
floodwater level in Hai Lang district had the lowest population density exposed to the flood event on 28October 2020.
The results in Figure 4b highlight that cropland area was impacted by less-than-0.5m floodwater levelwith more than 5,000 ha. In addition, the most affected cropland area focused on Trieu Phong, Hai Lang, andVinh Linh districts with a total area of 3,450 ha, 2,000 ha and 1,881 ha, respectively. On the other hand, theaffected urban area was mainly in Hai Lang district with around 200 ha, in which about 7.3 ha was inundatedby 2m floodwater depth..

103a) Exposed peopleb) Affected Cropland and Urban area
Figure 4. Damage assessment due to the flood event on 28 October 2020 in Quang Tri province
5. Conclusion
In this study, the authors introduce the FwDET-GEE algorithm for rapidly estimating floodwater
depth on GEE with integration with Sentinel-1 data. This approach helps reduce a significant time interval
and requires minimal computer capability. After achieving the floodwater depth result, the authors integratewith the population, cropland, and urban area dataset recorded in 2020 to give the detailed assessment offlood damages to Quang Tri province in the flood event of 28 October 2020. The results reveal that thedominant floodwater depth was less than 0.5m covered about 10,500 ha; in addition, greater-than-3m
floodwater depth covered the area of 800 ha and mainly appeared in the Hai Lang district. Furthermore, theflood event on 28 October 2020 impacted about 54,000 people, 9,100 ha and 360 ha of cropland and urbanarea, respectively. In the future, since natural flood disaster becomes more frequent and severe, integrating
Sentinel-1 data and GEE will help achieve reliable assessment rapidly, hence obtaining better disaster
response and management.
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