Consistent monitoring of surface water dynamics is essential for water resources, flood risk management, and addressing the challenges posed by climate change, urbanization. Nhat Le River Basin witnesses significant and noticeable dynamics in surface water on a yearly basis due to water-related disasters like floods and droughts. This article presents the first comprehensive study to systematically map and analyse the long-term (2016–2022) spatiotemporal dynamics of surface water in the Nhat Le River Basin of Vietnam, utilizing Sentinel-1 data. The results reveal that the optimal threshold for separating water from non-water pixels is −19 dB, with an overall accuracy of 0.93–0.94 and a Kappa coefficient of 0.77–0.82. Through quantitative analysis, the study characterizes seasonal and interannual variations in the surface water extent, contributing to an enhanced understanding of flood patterns and associated risks in a data-scarce region. Our analysis reveals the Kien Giang river delta as the most flooding-vulnerable sub-region, underscoring the importance of targeted risk management and adaptation planning in this area. A Google Earth Engine Tool is developed for automatic detecting, monitoring, and accessing the spatiotemporal dynamics of surface water in Nhat Le River Basin over the period 2016–2022 and is available on GitHub (https://github.com/MinhVu25/Surface_Water_Dynamics_2023).

  • Optimal threshold value for surface water extraction from Sentinel-1 Synthetic Aperture Radar data.

  • Systematical mapping and long-term analysis of spatiotemporal surface water dynamics in the Nhat Le river basin using radar image.

  • Fully automatic processing chain for detecting and mapping seasonal and inter-annual variations in surface water extent in data-scarce region.

  • Free Google Earth Engine tool for dynamics monitoring of surface water area.

The increasing frequency and severity of water-related disasters worldwide, exacerbated by the effects of climate change, highlights the urgent need for effective water resources management. Central Vietnam has more than 1,500 km of coastline and is the most vulnerable area in Vietnam, where 70% of country experiences damage caused by storms and floods in the past 20 years (Asian Disaster Reduction Center 2000). Nhat Le River Basin, a concave terrain and low-lying area in Quang Binh province (Figure 1), often witnesses tropical storms causing heavy rainfall, widespread flooding, and detrimental damage to local people regarding human life, property, and livelihood (Thuy 2019). Particularly, during a seasonal monsoon and tropical depression in October 2020, a series of four storms struck Quang Binh province, where the first two typhoons caused 25 deaths, 197 injuries, and more than 3,000 trillion VND of economic losses (People's Committee of Quang Binh province 2020). In contrast, the influence of the hot and dry weather during the Southwest monsoon (also known as Laos wind), combined with low precipitation, significantly contributes to droughts in this region. This phenomenon leads to serious consequences, particularly for agricultural and livestock productivity (Thuy & Thu 2021). The contrasting patterns of floods and droughts during different seasons contribute to distinguishable dynamics of surface water bodies in Nhat Le River Basin. Managing and adapting to these dynamics is essential for ensuring water availability, protecting communities, and preserving ecosystems in the face of changing climatic conditions (Poortinga et al. 2017). Therefore, monitoring and evaluating the spatiotemporal changes of surface water dynamics are of paramount importance for the economic, agricultural, and social development of Quang Binh province and lessen the severity of a disaster's impact.
Figure 1

Nhat Le River Basin in Quang Binh province, Vietnam.

Figure 1

Nhat Le River Basin in Quang Binh province, Vietnam.

Close modal

Nowadays, remote sensing has become a powerful and widely used technique for a wide range of applications (from environmental monitoring to disaster management, agriculture, forestry, urban planning, and beyond) due to its numerous advantages, i.e., wide coverage, high revisit frequency, valuable and good historical data, clear and stable images, rich information with low cost and sometimes free access, and real-time monitoring. Satellite sensors can be broadly categorized into two main types, i.e., active sensors and passive sensors. Optical images from passive sensors are indeed well-suited for specific studies and conditions, where cloud cover and daylight availability are less of an issue. On the other hand, radar sensors have the ability to penetrate cloud cover, completely neglect the cloud shadow effect, and provide data in all weather conditions. Recently, Sentinel-1, a part of the European Space Agency's Copernicus program, has become a prominent source of Synthetic Aperture Radar (SAR) data for water body mapping. With a spatial resolution of 10 m and a repeat cycle of 6 days for most of Europe and 12 days for other parts of the world, Sentinel-1 offers the creation of time series data, enabling long-term monitoring and analysis of surface water changes (Schlund & Erasmi 2020). Sentinel-1 data have been widely used for various studies, from flood extent detection (Bioresita et al. 2018; Uddin et al. 2019; Huang & Jin 2020) to temporal dynamics of surface water (Gulácsi & Kovács 2020), groundwater change monitoring (Liu et al. 2019), and soil moisture mapping (Paloscia et al. 2013).

The automatic extraction methods of remote sensing water body information are often categorized into two main approaches: threshold segmentation and image classification (Li et al. 2022). While the first approach uses the spectrum, texture, and spatial characteristics of both images and the ground for water extraction, the latter approach primarily extracts water based on its particular spectral characteristics. Although image classification methods generally produce superior results for water detection and extraction, they would be overly complex and time consuming for detecting surface water over a long study period due to difficulties in developing classification criteria and recurrent collection of training samples. On the contrary, the threshold segmentation technique is well known for its ease of use in extracting water by simply applying a threshold value to separate water pixels from other land cover categories (Atta et al. 2018). For optical images, the threshold value is typically based on the reflectance properties of the water surface and surrounding terrain. Therefore, the threshold segmentation approaches are often integrated with water indices such as normalized difference water index (NDWI) (McFeeters 1996) and modified normalized difference water index (mNDWI) (Xu 2006), which help to highlight the presence of water in an image from other categories by taking the normalized difference between the near-infrared and green bands. Various researchers have adopted these indices for their studies on water body detection, wetland management, landcover change, river and coastal morphological change, and disaster management (Eid et al. 2020; Quang et al. 2021a, 2021b; Shashikant et al. 2021). In contrast, for SAR images, the threshold value is usually determined by analysing the histogram of SAR backscatter intensity and estimating the probability distributions of water and non-water pixels. Thresholding-based methods can be categorized into two main groups: global thresholding and local thresholding (Liang & Liu 2020). In fact, the first method is more common since it employs a single threshold for all the images, making them well-suited for processing images with varying grey ranges between objects and the background. Otsu's method is one of the most widely used approaches in global thresholding, which selects an optimum threshold by maximizing the between-class variance (Huang & Wang 2009). This approach assumes the histogram of pixel values is bimodal and then selects the optimal threshold at the separated points of two classes in the histogram. Hence, Otsu's method can provide a dynamic threshold for each image to separate water and non-water areas with a fairly good accuracy compared to other methods (Bangira et al. 2019). The bimodal distribution in the pixel value histogram mainly focuses on the flood season when the water presence significantly increases, leading to lower backscatter submerged vegetation cover area, creating a second histogram peak. In contrast, the ground surface in the dry season is less affected by flooding and likely has a unimodal distribution in the histogram, reducing the accuracy of Otsu's method. Moreover, applying Otsu's method for a large study area of more than 2,600 km2 can require relatively high computation and, hence, be less effective in real-time applications or high-resolution images. Therefore, this study is interested in investigating the annual variation of surface water, and then a unique threshold for the whole year is ideally sufficient. Furthermore, the method of using a single threshold can reduce time and effort for data processing and simply apply in other case studies.

Long-term studies with huge data collection require a high demand for data storage and processing time, which can be an obstacle for conventional Geographic Information System software, e.g., QGIS, ArcGIS, or ENVI. Fortunately, cloud computing has significantly transformed the landscape of big data analysis, including remote sensing data, by offering scalability, accessibility, and cost-effectiveness. Google Earth Engine (GEE), a cloud-based computing platform, typically makes use of high-performance parallel computing capabilities, a wealth of ready-to-use geospatial products, machine learning algorithms, and a library of Application Programming Interfaces with development environments that support well-known coding languages like JavaScript and Python (Tamiminia et al. 2020). GEE has been successfully utilized in problems related to large-scale and long-term surface water monitoring (Pekel et al. 2016; Pickens et al. 2020).

The purposes of this study are as follows: (1) to identify the optimal threshold value for surface water extraction in Nhat Le river basin from Sentinel-1 SAR image; (2) to develop a free GEE Tool for dynamic monitoring of surface water area; and (3) to assess the spatial and temporal dynamics of surface water area in Nhat Le river basin over the period 2016–2022.

Study area

Nhat Le is the second largest basin in Quang Binh province with an area of 2,622 km2 (Figure 1). Two main tributaries, called the Long Dai and Kien Giang rivers, meet at Tran Xa village to form the Nhat Le River, which flows to the sea through the Nhat Le estuary. The topography of the Nhat Le River Basin is quite complex and gradually lowers from the west to the east. Separated by mountainous terrain in the west and high sand strips running along the coast in the east, Nhat Le delta is known as a flood-prone area with the lowest elevation of 0.7 m below sea level. Tung & Quang (2022) pointed out four main reasons for prolonged flooding in Nhat Le River Basin during the historic flood in 2020: (i) the low-lying and concave basin; (ii) the deposition of Nhat Le estuary – the unique drainage outlet of the basin; (iii) the construction of structures and infrastructures along the river and within the basin; and (iv) abnormally heavy rainfall coincides with the occurrence of high tide.

The area experiences an average annual rainfall ranging from 2,000 to 2,300 mm. The majority of the annual rainfall, specifically 80%, occurs during the rainy season, which lasts from September to March of the following year. The Box-whisker plots of daily water level at Kien Giang and Le Thuy hydrological stations over the period 1976–2020 show significant seasonal variations in the water level of Kien Giang river, especially from September to December (Figure 2). This characteristic leads to the high dynamism of surface water bodies since the water resources in Nhat Le River Basin are diverse with sea, rivers, lakes, and reservoirs (Figure 1). Effective management and sustainable use of these diverse water resources are crucial for meeting the needs of growing populations, ensuring food security, supporting ecosystems, and addressing the challenges posed by climate change and water scarcity.
Figure 2

Box-whisker plots of daily water level at (a) Kien Giang station and (b) Le Thuy station with their three-stage alarm levels over the period 1976–2020.

Figure 2

Box-whisker plots of daily water level at (a) Kien Giang station and (b) Le Thuy station with their three-stage alarm levels over the period 1976–2020.

Close modal

Several studies on surface water mapping using a threshold segmentation method recommended a selection of sub-case study, e.g., a lake or a reservoir, to represent the whole study areas like a basin, a province, and even a country (Feyisa et al. 2014; Zhou et al. 2020; Jiang et al. 2021). In this study, Phu Hoa reservoir, which was constructed in 2000 with a total capacity of 8.64 million m3 and a maximum water level of 30.86 m (Figure 1), is chosen as a sub-case study to identify the most suitable threshold range for surface water extraction in Nhat Le River Basin.

Materials

Satellite data

As mentioned previously, radar satellites can provide data in all weather conditions, so the data accessibility for radar images is more preeminent than optical images. Figure 3 shows the collected images from Sentinel-1 and other popular optical image sources (Landsat 7–9 and Sentinel-2) for the study area and in the same period. It is worth noting that only optical images with less than 20% cloud cover percentage are chosen for qualified water extraction (Yang et al. 2020). Figure 3(b) reveals that the collected images from Sentinel-1 are dominant compared to the optical images. Specifically, 380 collected Sentinel-1 images belonging to two relative orbits 128 and 18 over the period 2016–2022 are acquired for mapping water bodies (Figure 3(a)). Among the two polarizations of SAR data, the vertical transmit/horizontal receive (VH) is more sensitive to variations in vegetation cover and is preferable for mapping floods, shallow water bodies, and swampy areas (Kavats et al. 2022). Since Nhat Le River Basin is a flood-prone region, only VH polarization is chosen for surface water extraction.
Figure 3

(a) Sentinel-1 footprint and (b) collected images from 2016 to 2022.

Figure 3

(a) Sentinel-1 footprint and (b) collected images from 2016 to 2022.

Close modal

Optical images have been widely applied in water detection and delineation in previous studies (Huang et al. 2018); therefore, it is necessary to compare the performance of surface water extraction between radar and optical images. In this study, three images of Landsat 8, Sentinel-2, and Sentinel-1 are carefully chosen to represent the surface water conditions at Phu Hoa reservoir, the sub-case study, as closely as possible to the available date and approximate water level for the fair comparison (Table 1).

Table 1

Collected optical and radar images for comparing the performance of surface water extraction

SatelliteSensing dateWater level (m)
Landsat 8 25/02/2021 30.02 
Sentinel-2 20/01/2021 30.39 
Sentinel-1 27/02/2021 30.02 
SatelliteSensing dateWater level (m)
Landsat 8 25/02/2021 30.02 
Sentinel-2 20/01/2021 30.39 
Sentinel-1 27/02/2021 30.02 

Topographic map, water level of Phu Hoa reservoir, and flood marks in Nhat Le River Basin

Being constructed in 2000, the bathymetry of Phu Hoa reservoir has been significantly altered due to sedimentation after 23 years of operation. Therefore, its updated topographic map has been collected from the survey campaign in 2019 to reconstruct the new water level – water surface area curve. Besides surveyed bathymetry, the water level of Phu Hoa reservoir for the period 2016–2022 is also collected to identify its surface water body, which will be used later to evaluate the accuracy of extracted results.

In addition, 216 flood marks in Nhat Le River Basin during the historical flood event in October 2020 are also collected from the survey campaigns by Tung (2021) and Institute for Hydropower and Renewable Energy (2021) (Figure 4). These marks are also used as ground truth records to verify the reliability of extracted surface water extent.
Figure 4

Flood extent boundary and flood marks of the historical flood event in October 2020.

Figure 4

Flood extent boundary and flood marks of the historical flood event in October 2020.

Close modal

Selection of optimal threshold range for surface water extraction

Identification of the most suitable threshold range – the case study of Phu Hoa reservoir

The threshold segmentation method is adopted in this study to detect the water body, in which the threshold value should be carefully selected based on the specific characteristics of the imagery and the objectives of the analysis. In the first stage, the most suitable threshold range for Phu Hoa reservoir, the sub-case study of this research, will be identified using the GEE platform (Figure 5). Firstly, the raw Sentinel-1 images covering the Phu Hoa reservoir are collected and processed on GEE platform. Normally, Sentinel-1 images need to be preprocessed before computation to ensure the accuracy of water extraction output. Fortunately, five basic preprocessing steps for Sentinel-1 images described by Markert et al. (2020) were performed by Sentinel-1 Toolbox before publishing on the GEE platform, including applying orbit file, ground range detected border noise removal, radiation noise removal, thermal noise removal, application of radiometric calibration values, and terrain correction. Therefore, only one preprocessing step, which is the application of a focal median speckle filter to smooth the image surface, is implemented in this study. After pre-processing steps, the value of the radar backscatter is converted to decibel (dB) units (Mayer et al. 2021).
Figure 5

Flowchart for identifying the most suitable threshold range for water body extraction.

Figure 5

Flowchart for identifying the most suitable threshold range for water body extraction.

Close modal

Several studies have pointed out that the optimal threshold value for separating water and non-water pixels may vary depending on the specific dataset, the time of acquisition, atmospheric conditions, and the nature of the water bodies being analysed. Jiang et al. (2021) stated that the suitable threshold values for surface water extraction in the spring, summer, fall, and winter are −14.45, −13.97, −14.78, and −16.32 dB, respectively. Tran et al. (2022) also suggested the values of −20 dB in the autumn, −24 dB in the winter, and −22 dB in the flood season for VH polarization images. Therefore, the collected images should cover the whole calendar year to propose the most suitable threshold range. Hence, in the second stage, 12 Sentinel-1 images from September 2020 to August 2021 that correspond to 12 months of a year are gathered (Table 2). The duration of collected images coincides with the rainy season in 2020 and the dry season in 2021, as well as the historic flood in October 2020. It is significant to convert the sensing time of collected images into Indochina Time, i.e., GMT + 7, to acquire the observed water level of Phu Hoa reservoir at the same time with the captured satellite image. By combining this water level with reservoir bathymetry, the ‘actual’ surface water can be derived and then will be used to validate the extracted results.

Table 2

Information on collected Sentinel-1 images for Phu Hoa reservoir and its corresponding water levels

NoSensing dateSensing time (GMT + 0)Local dateLocal time (GMT + 7)Observed water level (m)
2020/09/28 22:43:41 2020/09/29 25.96 
2020/10/18 11:05:07 2020/10/18 18 26.93 
2020/11/27 22:43:40 2020/11/28 30.08 
2020/12/17 11:05:05 2020/12/17 18 30.05 
2021/01/26 22:43:38 2021/01/27 30.05 
2021/02/27 11:05:03 2021/02/27 18 30.02 
2021/03/03 22:43:37 2021/03/04 28.92 
2021/04/08 22:43:38 2021/04/09 28.93 
2021/05/14 22:43:40 2021/05/15 28.05 
10 2021/06/07 22:43:41 2021/06/08 28.01 
11 2021/07/25 22:43:44 2021/07/26 27.52 
12 2021/08/06 22:43:45 2021/08/07 27.8 
NoSensing dateSensing time (GMT + 0)Local dateLocal time (GMT + 7)Observed water level (m)
2020/09/28 22:43:41 2020/09/29 25.96 
2020/10/18 11:05:07 2020/10/18 18 26.93 
2020/11/27 22:43:40 2020/11/28 30.08 
2020/12/17 11:05:05 2020/12/17 18 30.05 
2021/01/26 22:43:38 2021/01/27 30.05 
2021/02/27 11:05:03 2021/02/27 18 30.02 
2021/03/03 22:43:37 2021/03/04 28.92 
2021/04/08 22:43:38 2021/04/09 28.93 
2021/05/14 22:43:40 2021/05/15 28.05 
10 2021/06/07 22:43:41 2021/06/08 28.01 
11 2021/07/25 22:43:44 2021/07/26 27.52 
12 2021/08/06 22:43:45 2021/08/07 27.8 

The water pixels of Sentinel-1 images usually have VH and VV backscatter coefficients in the range of −25 to −10 dB, depending on various factors such as the incidence angle, polarization, and water surface conditions. Many researchers have proposed water segmentation thresholds within this value range (Huang et al. 2017; Pham-Duc et al. 2017; Hu et al. 2020; Tran et al. 2022). Moreover, the raw Sentinel-1 images from 2016 to 2022 also reveal that the potential value to separate water and non-water pixels is also within this range (Figure 6). Therefore, in this study, the trial-and-error method is applied for pixel values ranging from −25 to −10 dB with a 1 dB increment to find out the most suitable threshold range for surface water extraction.
Figure 6

(a) Raw Sentinel-1 image at Phu Hoa reservoir and (b) its pixel value statistic.

Figure 6

(a) Raw Sentinel-1 image at Phu Hoa reservoir and (b) its pixel value statistic.

Close modal

After applying the threshold, two land cover features named Water and Non-Water are included in the classified output with assigned values equal to 1 and 0, respectively. The accuracy assessment is then performed based on two accuracy metrics, i.e., overall accuracy (OA) and Cohen's Kappa coefficient (Kappa), to determine the best threshold range. Finally, the optimal threshold for surface water extraction for the study area is investigated based on surveyed flood marks and flood extent during the historical flood event on 18 October 2020 as follows:

  • (i)

    Sentinel-1 image captured on 18 October 2020 is collected to extract surface water using different thresholds within the proposed threshold range.

  • (ii)

    Munich-Re (Munich-Re 1997) defined flood as a temporary condition of surface water, in which the water level and/or discharge exceeds a certain value, thereby escaping from its normal confines. Therefore, the permanent water bodies such as rivers and lakes should be removed from extracted surface flooded area. In this research, the permanent water is obtained from a free global surface water data source called JRC global surface water mapping (Pekel et al. 2016).

  • (iii)

    Removing steep areas is also significant to avoid misunderstanding natural drainage flow and noises from permanent mountain shadows with flood layers (Lin et al. 2019). In this study, digital elevation model data obtained from HydroSHEDS is used to remove areas with slopes above 5% over the basin.

  • (iv)

    To eliminate small inundations that confound the final floodplain results, flood areas with coverage below eight connected pixels are also excluded.

  • (v)

    Performance of extracted results is finally assessed based on surveyed flood marks and its boundary using OA and Kappa to identify the optimal threshold.

Accuracy assessment

The validation of surface water extent derived from the Sentinel-1 images is based on the ‘actual’ surface extent that is derived from Phu Hoa reservoir's observed water level and its bathymetry. More precisely, the surface water shape with a corresponding contour level for each water level is generated using AutoCAD software. These shapes are then converted to Shapefile format in ArcMap and identified as water with the ‘Landcover property’ set to 1. Similarly, another shapefile is marked as non-water, which is simply the outer part of the water shapefile, with the ‘Landcover property’ labelled as 0 (Figure 7). This approach is also applied for flood validation data of the flood event in 2020, including flood boundary in polygon and flood marks in points. Finally, these two shapefiles are imported to GEE to calculate OA and Kappa.
Figure 7

(a) Reservoir surface water area validation data and (b) flood boundary validation data with flood marks.

Figure 7

(a) Reservoir surface water area validation data and (b) flood boundary validation data with flood marks.

Close modal
Table 3 shows the confusion matrix-based approach adopted in this study to estimate the OA and Kappa of classified images with reference data. The OA and Kappa coefficient are calculated with formulas (1) and (2). The OA is the probability that a pixel will be correctly classified by a test ranging from 0 to 1. The Kappa is used to gauge the degree of agreement between the reference data and the classification that places pixels into groups that are mutually exclusive. Kappa coefficient values vary from −1 to 1. The ideal value for both OA and Kappa should be close to 1, indicating a better likelihood of accurate pixel prediction and the unification of classified and reference data.
formula
(1)
formula
(2)
where ∑ is the chance accuracy represented by (TP+FP)(TP+FN)+(FN+TN)(FP+TN), and T is the total number of pixels in the accuracy assessment.
Table 3

Confusion matrix

Reference data
WaterNon-water
Classified data Water TP FP 
Non-water FN TN 
Reference data
WaterNon-water
Classified data Water TP FP 
Non-water FN TN 

Note: True positive (TP), false negative (FN), false positive (FP), and true negative (TN) are the number of correctly extracted water pixels, the number of undetected water pixels, the number of incorrectly extracted water pixels, and the number of correctly rejected non-water pixels, respectively.

Assessment of spatiotemporal dynamics of surface water in Nhat Le River Basin

This section aims to assess the temporal dynamics and trends of water surface area as well as construct a water occurrence map, which shows the frequency of the pixel presented as water for Nhat Le River Basin over the studied period. The first task is done by adopting the chosen optimal threshold to delineate surface water from 380 collected Sentinel-1 images using GEE. The second aim can be obtained by evaluating the spatial distribution of the intra- and inter-annual dynamics of surface water. More specifically, the pixel value of all classified images collected from the same month is summed and averaged for the total number of images in that month to generate a map called the monthly surface water occurrence map. For instance, the pixel value of classified images in January 2016 is summed up with the pixel value of classified images in January 2017 and so on before averaging for the total number of collected images in January from 2016 to 2022. Finally, the pixel value of all 12 monthly surface water occurrence maps is averaged to obtain the final surface water occurrence map. The occurrence map is presented in the unit of percentage (%) and is classified into five categories: 0–10, 10–30, 30–50, 50–80, and 80–100% represent permanent not water, low seasonal, medium seasonal, high seasonal, and permanent water, respectively, as the suggestion of Pekel et al. (2016).

Optimal threshold and surface water extraction

To determine the optimal threshold for surface water extraction, the threshold range is initially proposed based on the values of OA and Kappa by applying the trial-and-error method for 12 collected images presented in Table 2. Table 4 and Figure 8 show the values of OA and Kappa with corresponding threshold values applied for the Sentinel-1 image captured on 17 December 2020.
Table 4

Accuracy assessment for Phu Hoa reservoir on 17 December 2020

Threshold (dB)OAKappaThreshold (dB)OAKappa
−25 0.892 0.700 −17 0.927 0.826 
−24 0.909 0.752 −16 0.903 0.778 
−23 0.922 0.790 −15 0.856 0.690 
−22 0.931 0.818 −14 0.813 0.616 
−21 0.939 0.841 −13 0.791 0.579 
−20 0.944 0.856 −12 0.781 0.563 
−19 0.948 0.867 −11 0.778 0.559 
−18 0.943 0.852 −10 0.775 0.554 
Threshold (dB)OAKappaThreshold (dB)OAKappa
−25 0.892 0.700 −17 0.927 0.826 
−24 0.909 0.752 −16 0.903 0.778 
−23 0.922 0.790 −15 0.856 0.690 
−22 0.931 0.818 −14 0.813 0.616 
−21 0.939 0.841 −13 0.791 0.579 
−20 0.944 0.856 −12 0.781 0.563 
−19 0.948 0.867 −11 0.778 0.559 
−18 0.943 0.852 −10 0.775 0.554 
Figure 8

(a) Plots of OA value and (b) Kappa value with corresponding thresholds on 17 December 2020 for Phu Hoa reservoir.

Figure 8

(a) Plots of OA value and (b) Kappa value with corresponding thresholds on 17 December 2020 for Phu Hoa reservoir.

Close modal
The results reveal that the optimal threshold for extracting the surface of Phu Hoa reservoir on 17 December 2020 is −19 dB. However, as mentioned in Section 3.1, this threshold determination depends on specific dataset, time of acquisition, atmospheric conditions, and the nature of the water bodies; thus, it is crucial to evaluate all 12 selected images that correspond to 12 months of a year (Table 2). Figure 9 points out that the extracted results for Phu Hoa reservoir particularly and Nhat Le River Basin generally are optimal when a threshold ranges from −20 to −19 dB, with the corresponding values of OA and Kappa greater than 0.94 and 0.82, respectively. This finding is utterly reliable since it is consistent with the pixel statistical for Phu Hoa reservoir shown in Figure 6.
Figure 9

(a) Plots of OA value and (b) Kappa value with corresponding thresholds for 12 collected images for Phu Hoa reservoir.

Figure 9

(a) Plots of OA value and (b) Kappa value with corresponding thresholds for 12 collected images for Phu Hoa reservoir.

Close modal

After narrowing the range of threshold, the trial-and-error method is applied again for the values ranging from −20 to −19 dB with a 0.25 dB increment to select the optimal threshold using the validation data in Nhat Le River Basin from the flood event on 18 October 2020. Table 5 reveals that the optimal threshold for surface water extraction in Nhat Le River Basin is −19 dB with 183/216 corrected flood marks, and the OA and Kappa values are 0.934 and 0.777, respectively.

Table 5

Accuracy assessment for historical flood event on 18 October 2020 in Nhat Le River Basin

Threshold (dB)Corrected flood marksOAKappa
20 170 0.932 0.766 
−19.75 174 0.932 0.770 
−19.5 176 0.933 0.772 
−19.25 179 0.933 0.774 
−19 183 0.934 0.777 
Threshold (dB)Corrected flood marksOAKappa
20 170 0.932 0.766 
−19.75 174 0.932 0.770 
−19.5 176 0.933 0.772 
−19.25 179 0.933 0.774 
−19 183 0.934 0.777 

The performance of extracted results from Sentinel-1 images is compared with the ones obtained from optical images since these images have been widely applied in delineating water bodies. More specifically, the surface water of Phu Hoa reservoir is derived from two optical images (Landsat 8 captured on 25 February 2021 and Sentinel-2 captured on 20 January 2021; Table 1), by using the two most common water indices, i.e., NDWI and mNDWI, with conventional thresholds that were suggested by Minh et al. (2022). Table 6 shows the performance of extracted results for Phu Hoa reservoir for both optical and radar images. Apparently, the chosen optimal threshold significantly improves the quality of surface water extraction from Sentinel-1 images with the values of OA and Kappa up to 0.944 and 0.859, respectively.

Table 6

Performance of extracted results for Phu Hoa reservoir for both optical and radar images

SatelliteWater indicesThresholdOAKappa
  −0.05 0.817 0.640 
 NDWI 0.745 0.502 
Landsat 8  0.05 0.475 0.005 
  −0.05 0.918 0.836 
 mNDWI 0.908 0.818 
  0.05 0.900 0.802 
  −0.05 0.855 0.712 
 NDWI 0.826 0.658 
Sentinel-2  0.05 0.808 0.624 
  −0.05 0.860 0.720 
 mNDWI 0.866 0.732 
  0.05 0.858 0.718 
Sentinel-1 ‒ −19dB 0.944 0.859 
SatelliteWater indicesThresholdOAKappa
  −0.05 0.817 0.640 
 NDWI 0.745 0.502 
Landsat 8  0.05 0.475 0.005 
  −0.05 0.918 0.836 
 mNDWI 0.908 0.818 
  0.05 0.900 0.802 
  −0.05 0.855 0.712 
 NDWI 0.826 0.658 
Sentinel-2  0.05 0.808 0.624 
  −0.05 0.860 0.720 
 mNDWI 0.866 0.732 
  0.05 0.858 0.718 
Sentinel-1 ‒ −19dB 0.944 0.859 

Figure 10 shows the extracted surface water of Phu Hoa reservoir using the chosen optimal threshold with three stages of water levels, i.e., low, medium, and high. The surface water extents corresponding to medium and high water levels (Figure 10(b) and 10(c)) are relatively fit for the ‘actual’ water body, while the result under low water level condition slightly overestimates the reference data (Figure 10(a)). This can be explained by the misclassification of the concrete dam, which is revealed after the water level decreases because the backscatter coefficient of concrete might be similar to water. Moreover, the body of Phu Hoa reservoir at the low water level is moderately small; hence, the spatial resolution of Sentinel-1 is not fine enough to precisely detect the surface water boundary. Nevertheless, this problem can be neglected, when the whole Nhat Le River Basin with an area of 2,622 km2 is analysed.
Figure 10

Extracted surface water of Phu Hoa reservoir with three stages of water levels: (a) 24 m, (b) 28 m, and (c) 30 m.

Figure 10

Extracted surface water of Phu Hoa reservoir with three stages of water levels: (a) 24 m, (b) 28 m, and (c) 30 m.

Close modal
Figure 11 shows the flood extent (blue colour) in Nhat Le River Basin that is delineated from the Sentinel-1 image on 18 October 2020 using the optimal threshold. The extracted flood extent is not only compatible with the flood boundary collected by Tung (2021) and Institute for Hydropower and Renewable Energy (2021) (Figure 11(a)) but also in good agreement with collected data at specified locations such as the captured image by Flycam at the confluence of Kien Giang river (Figure 11(c)) and the very high-resolution Pléiades imagery at Gia Ninh commune in Quang Ninh district (Figure 11(b)). This infers that the chosen optimal threshold is suitable for surface water extraction in Nhat Le River Basin and will be adopted for the spatial dynamics monitoring of surface water bodies in this basin.
Figure 11

(a) Extracted flood extent in Nhat Le River Basin; (b) Gia Ninh commune in Quang Ninh district on 18 October 2020; and (c) confluence of Kien Giang River.

Figure 11

(a) Extracted flood extent in Nhat Le River Basin; (b) Gia Ninh commune in Quang Ninh district on 18 October 2020; and (c) confluence of Kien Giang River.

Close modal

Water dynamics in Nhat Le River Basin

The fluctuation in annual surface water area in Nhat Le River Basin over the period 2016–2022 is presented in Figure 12. All plotted lines of 7 years show two typical trends: (i) the surface water area is relatively low during the dry season lasting from April to August; and (ii) the area starts rising from the beginning of the flood season (vertical red line) until the next January before gradually decreasing in February and March. The surface water area in the dry season frequently stays below 100 km2, while during flood season, the figure often fluctuates around 150–200 km2. Exceptionally, the surface water area in the flood season in 2020 is significantly higher than in other years due to a series of severe flood events, especially the historic one on 18 October 2020.
Figure 12

Temporal dynamics of surface water area in Nhat Le River Basin from 2016 to 2022.

Figure 12

Temporal dynamics of surface water area in Nhat Le River Basin from 2016 to 2022.

Close modal
Similarly, the variation of surface water dynamics for monthly change within a year presents the same trend as year-on-year change. Figures 13 and 14 clearly show the discrete dynamics of surface water area between flood and dry seasons in 2016 and 2020, and the time floods are most likely to occur is from October to December. In the dry season in 2016, the area of surface water is relatively low and frequently below 100 km2 and only accounts for 15% of the total basin area. This area is merely 5–10% higher than the permanent water area of the basin (including rivers, reservoirs, and other open water bodies). This means that the study area is highly vulnerable to drought, even though 2016 is a flood year. In contrast, the monthly surface water area during the flood season noticeably increases, i.e., up to approximately 25–30% of the total basin area. Remarkably, the difference in surface water area between August and September is about 80 km2 (accounting for 10% of the basin area), which indicates the significant dynamics of surface water at the time of seasonal change and high risk of flooding. In 2020, the surface water area in the dry season is remarkably higher than the ones in other years due to the remaining amount of flood water from January to March and water storage in reservoirs. In addition, the surface water area from October to December is slightly higher than the ones in other years due to the series of flood events in October and November.
Figure 13

Monthly surface water area in Nhat Le River Basin in 2016.

Figure 13

Monthly surface water area in Nhat Le River Basin in 2016.

Close modal
Figure 14

Monthly surface water area in Nhat Le River Basin in 2020.

Figure 14

Monthly surface water area in Nhat Le River Basin in 2020.

Close modal

This study analyses not only temporal patterns of surface water distribution within the target basin but also the spatial distribution patterns are being evaluated. A spatiotemporal approach is employed to:

  • Elucidate temporal variation in surface water levels and transitions between wet and dry periods.

  • Evaluate spatial distribution and areal extent of surface water across the landscape.

  • Identify regions vulnerable to water-related hazards by integrating temporal flow dynamics with spatial water distribution patterns.

By considering both temporal variation and spatial distribution of surface water simultaneously, this study aims to improve understanding of hydrological processes governing water movement within the basin. Insights gained can help inform hazard mitigation and water resource management strategies.

Figure 15 shows the time series of surface water extraction in the Nhat Le River Basin from 2016 to 2022. The images are selected in October when floods are most likely to occur in the study area and have the highest area of surface water extraction. The visual inspection shows the annual variation in the region's surface water during the peak time of flood season, at which the difference between drought years (2017 and 2018) and flood years (2016, 2020, and 2022) is evident at the river confluence and Nhat Le River upstream. The flood years occur when there are numerous heavy rainfalls lasting for several consecutive days, influenced by tropical low-pressure systems, cold air, and storms. The flooding period often prolongs due to the phenomenon of overlapping floods happening within the same timeframe. In addition, floodwaters are often impeded due to the narrowing of the Nhat Le estuary caused by sedimentation, the impact of hydraulic structures, and the aquaculture activities, combined with the lower concave terrain of the basin compared to the sea level. On the other hand, the drought years occur when the precipitation is scared, and the main sources of water for this region coming from upstream rivers in the vicinity are stored in reservoirs and hydro power plants. Noticeably, the water extent in 2021 is relatively high compared to flood years though it is not considered as a flood year. This happens as the frequency of flood events in 2021 is low, but the selected image for surface water extraction in 2021 coincides with the time of the flood event on 17 October 2021, which is no less severe than historical floods that occurred earlier. Table 7 presents the information of selected images for the surface water time series, including sensing date, water level at Le Thuy station, and area of surface water extraction. Furthermore, an animation GIF was added to the supplementary to illustrate the evolution of surface water in the Nhat Le River Basin from 2016 to 2022.
Table 7

Selected images for surface water time series from 2016 to 2022

YearSensing dateWater level (m)Area (km2)
2016 2016/10/31 1.48 188.23 
2017 2017/11/07 1.46 187.32 
2018 2018/10/09 0.86 144.71 
2019 2019/10/28 1.26 172.10 
2020 2020/10/18 3.95 242.78 
2021 2021/10/17 2.98 225.04 
2022 2022/10/20 1.51 195.37 
YearSensing dateWater level (m)Area (km2)
2016 2016/10/31 1.48 188.23 
2017 2017/11/07 1.46 187.32 
2018 2018/10/09 0.86 144.71 
2019 2019/10/28 1.26 172.10 
2020 2020/10/18 3.95 242.78 
2021 2021/10/17 2.98 225.04 
2022 2022/10/20 1.51 195.37 
Figure 15

Surface water time series of Nhat Le River Basin from 2016 to 2022.

Figure 15

Surface water time series of Nhat Le River Basin from 2016 to 2022.

Close modal
Figure 16 displays the surface water occurrence map, which is generated from 380 collected Sentinel-1 images from 2016 to 2022 and is classified into five categories. The ‘permanent not water’ layer is mostly located in the mountainous area in the west and partially in the urban area of Dong Hoi city and Kien Giang town. This is reasonable because the western region of the basin is mainly covered by high-elevation areas and steep mountains; hence, water is normally not retained but quickly drains downstream. Similarly, urban areas with a high density of impervious land cover and effectively operated drainage systems rapidly drain water to rivers. On the other hand, the ‘permanent water’ layer clearly shows three main rivers (Kien Giang, Long Dai, and Nhat Le), large reservoirs, and several small lakes. Noticeably, the seasonal water area mostly focuses on the Kien Giang River delta, which is responsible for rice production in Central Vietnam. The concentration of seasonal water area indicates that this region experienced the high dynamics of surface water in Nhat Le River Basin over the period 2016–2022. This is completely compatible with the terrain map shown in Figure 1, where Kien Giang River delta is low lying with the lowest elevation of 0.7 m below the sea. The delta is mainly covered by ‘high seasonal water’ layer (light orange and light blue layer), which implies a frequent inundation for more than 50% of the time Sentinel-1 images were captured throughout the study period and hence highly vulnerable to flooding.
Figure 16

Water occurrence map of Nhat Le River Basin from 2016 to 2022.

Figure 16

Water occurrence map of Nhat Le River Basin from 2016 to 2022.

Close modal

Discussion

Our study provides a fully automatic processing chain for monitoring surface water and mapping water dynamics towards a well management of surface water in Nhat Le River Basin. We emphasize that the temporal dynamics follow two typical trends corresponding to dry and flood seasons throughout the study period, which can be influenced in specific years with abnormal natural hazards. Furthermore, the spatial dynamics show the transition of surface water in Nhat Le River Basin throughout the study period, and the most flooding-vulnerable area is Kien Giang River delta. The analysis of seasonal and inter-annual variation of surface water in this study states the temporal and spatial risk in the data-scarce region and improves the understanding of flood pattern, which highly contributes to water-related disaster risk management. In addition, our findings emphasize the importance of determining an optimal threshold for distinguished study areas to enhance the accuracy of surface water extraction. This study also provides a free-access tool for publicity to increase the feasibility of replication in further studies. Aiming to mitigate the challenges of water-related disasters, it is recommended that policymakers and stakeholders integrate our study's results to monitor surface water and prepare response plans.

Not only contributing to a clearer understanding of surface water dynamics in Nhat Le River Basin in both temporal and spatial scopes, but the study's method also has certain advantages compared to conventional methods in water-related disaster risk management. Firstly, regarding flood mapping, this method can determine potential flooding areas using free satellite data, while hydraulic models require multiple data such as elevation data, rainfall data, river flow data, and physical characteristic parameters. Furthermore, satellite data are globally covered and continuously updated on the GEE platform; hence, the effort to collect new data for different study areas or periods is mostly negligible. Secondly, thanks to the global scope of satellite data, the study approach can generate flood extent in a large study area. Moreover, the integration with GEE contributes to increasing time saving and reduces a lot of effort in model building and processing. Thirdly, this user-friendly approach allows users to map surface water dynamics at any time and region by simply changing input parameters, including the study area and the period. Finally, the water occurrence map is totally feasible to be used as an input to build a flood hazard map.

The study outcomes are also potentially applied in proposing a strategic regional plan and cultivation plan. According to the spatial analysis of the water occurrence map, Kien Giang river delta is the most vulnerable area to flooding in Nhat Le River Basin. Therefore, authorities and decision-makers should focus on this region in the strategic regional plan. For instance, the local government should invest more in applying structural and non-structural solutions for flood mitigation, such as anti-flood structures at the upstream Kien Giang River or low-impact development solutions. Not only focusing on proposing solutions for regional flooding situations but also the enhancement of local people's understanding and capacity plays a significant role in flood resilience. Particularly, local authorities can issue policies to support people living in Kien Giang, such as sending financial support to build typhoon-resilient low-income houses and raising human capacity building for resilience by holding special workshops or trainings. Furthermore, the spatiotemporal analysis has previously mentioned that floods might most likely occur in Kien Giang River delta, lasting from October to December. As Kien Giang River delta is the rice granary of the Central part of Vietnam, where the main source of income for most of the local people is from agricultural product trading, the impact of flooding can decrease rice productivity and threaten people's likelihood. Hence, the cultivation plan should be adjusted based on the study's results to reduce the negative impact of flooding on the locality. According to study outcomes, the most suitable time for starting the growing season should be at the beginning of February, when the flood season is going to end, and the surface water amount in the study area is abundant. Furthermore, shifting to plant short-duration rice varieties or floods and droughts-tolerant rice varieties might overcome the natural hazards in the study area.

As mentioned previously, this study's method is a fully automatic processing chain for detecting, monitoring surface water, and mapping water dynamics, which provides potentially rapid impact assessment of water-related disasters, i.e., floods and droughts, to study area. However, there are certain difficulties when replicating this approach in other study areas due to differences in regional characteristics, which require researchers to find a new optimal threshold for surface water detection. Therefore, it is necessary to gather sufficient data for processing and validating within at least one calendar year to ensure the objectivity of studies. Excluding the optimal threshold, the following stages of creating water dynamics mapping can be replicated without further adjustment.

The study aimed to automatically detect and monitor surface water dynamics in Nhat Le River Basin using Sentinel-1 satellite images. The threshold segmentation method is commonly used in surface water extraction from satellite data, and the proposal of an optimal threshold value of −19 dB for accurate surface water extraction in Nhat Le River Basin shows high reliability for both small and large areas. Comparisons with reference data from Phu Hoa reservoir confirmed the extracted surface water's accuracy, with OA and Kappa values surpassing 0.94 and 0.82, respectively. Applying the threshold on 18 October 2020 for flood extent mapping resulted in an OA of 0.93 and Kappa of 0.78.

Temporal analysis of water surface area in Nhat Le River Basin from 2016 to 2022 revealed two trends in surface water area: low levels during dry seasons, covering only 15% of the basin, and high levels during flood seasons, affecting nearly one-third of the basin. Floods were more likely between October and December, while severe droughts threatened the region from May to July.

Spatial analysis using a water occurrence map identified various water layers, including permanent not water, low seasonal, medium seasonal, high seasonal, and permanent water. The Kien Giang River delta emerged as the most vulnerable area to flooding, mainly covered by medium and high seasonal water layers.

The study's approach offered advantages over conventional hydraulic models for flood extent mapping, utilizing free and up-to-date data, saving time and effort in data processing, and being applicable to different study areas and periods. The surface water occurrence map proved valuable for flood hazard mapping and strategic regional planning by local authorities. However, the study acknowledged limitations, such as the need to improve accuracy and flood damage estimation. Future research should incorporate additional datasets like Digital Surface Model, Land Use/Land Cover data, and population density data to address these shortcomings and enhance the overall findings.

This research was supported by a Ministerial-level Research Project, namely, ‘Research on Digital Transformation in the Flood Warning Methods for the Community: An Experimental Flood Warning System for Nhat Le river basin, Quang Binh Province,’ funded by Ministry of Agricultural and Rural Development, Vietnam.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Asian Disaster Reduction Center
2000
ADRC Annual Report 1999. Asian Disaster Reduction Center. Kobe, Japan. https://www.adrc.asia/publications/annual/99/1999_contents.php
.
Atta
A. M.
,
Imtiaz
M.
,
Arfa
H.
&
Shazia
S.
2018
Image segmentation by using threshold techniques
.
Lahore Garrison University Research Journal of Computer Science and Information Technology
2
(
2
),
1
6
.
https://doi.org/10.54692/lgurjcsit.2018.020231
.
Bangira
T.
,
Alfieri
S. M.
,
Menenti
M.
&
van Niekerk
A.
2019
Comparing thresholding with machine learning classifiers for mapping complex water
.
Remote Sensing
11
(
11
),
1351
.
https://doi.org/10.3390/rs11111351
.
Bioresita
F.
,
Puissant
A.
,
Stumpf
A.
&
Malet
J.-P.
2018
A method for automatic and rapid mapping of water surfaces from Sentinel-1 imagery
.
Remote Sensing
10
(
2
),
217
.
https://doi.org/10.3390/rs10020217
.
Eid
A. N. M.
,
Olatubara
C. O.
,
Ewemoje
T. A.
,
El-Hennawy
M. T.
&
Farouk
H.
2020
Inland wetland time-series digital change detection based on SAVI and NDWI indices: Wadi El-Rayan lakes, Egypt
.
Remote Sensing Applications: Society and Environment
19
,
100347
.
https://doi.org/10.1016/j.rsase.2020.100347
.
Feyisa
G. L.
,
Meilby
H.
,
Fensholt
R.
&
Proud
S. R.
2014
Automated water extraction index: A new technique for surface water mapping using Landsat imagery
.
Remote Sensing of Environment
140
,
23
35
.
https://doi.org/10.1016/j.rse.2013.08.029
.
Gulácsi
A.
&
Kovács
F.
2020
Sentinel-1-imagery-based high-resolution water cover detection on wetlands, aided by Google Earth Engine
.
Remote Sensing
12
(
10
),
1614
.
https://doi.org/10.3390/rs12101614
.
Hu
S.
,
Qin
J.
,
Ren
J.
,
Zhao
H.
,
Ren
J.
&
Hong
H.
2020
Automatic extraction of water inundation areas using Sentinel-1 data for large plain areas
.
Remote Sensing
12
(
2
).
https://doi.org/10.3390/rs12020243
.
Huang
D.-Y.
&
Wang
C.-H.
2009
Optimal multi-level thresholding using a two-stage Otsu optimization approach
.
Pattern Recognition Letters
30
(
3
),
275
284
.
https://doi.org/10.1016/j.patrec.2008.10.003
.
Huang
C.
,
Nguyen
B. D.
,
Zhang
S.
,
Cao
S.
&
Wagner
W.
2017
A comparison of terrain indices toward their ability in assisting surface water mapping from Sentinel-1 data
.
ISPRS International Journal of Geo-Information
6
(
5
),
1
16
.
https://doi.org/10.3390/ijgi6050140
.
Huang
C.
,
Chen
Y.
,
Zhang
S.
&
Wu
J.
2018
Detecting, extracting, and monitoring surface water from space using optical sensors: A review
.
Reviews of Geophysics
56
(
2
),
333
360
.
https://doi.org/10.1029/2018RG000598
.
Institute for Hydropower and Renewable energy
2021
Investigate, Locate Flood Marks for Historical Floods in 2020 and Construct Flood Maps for 4 Large River Basins in Quang Binh Province
.
Vietnam Academy for Water Resources
.
Kavats
O.
,
Khramov
D.
&
Sergieieva
K.
2022
Surface water mapping from SAR images using optimal threshold selection method and reference water mask
.
Water
14
(
24
),
4030
.
https://doi.org/10.3390/w14244030
.
Li
J.
,
Ma
R.
,
Cao
Z.
,
Xue
K.
,
Xiong
J.
,
Hu
M.
&
Feng
X.
2022
Satellite detection of surface water extent: A review of methodology
.
Water (Switzerland)
14
(
7
),
1
18
.
https://doi.org/10.3390/w14071148
.
Liang
J.
&
Liu
D.
2020
A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery
.
ISPRS Journal of Photogrammetry and Remote Sensing
159
,
53
62
.
https://doi.org/10.1016/J.ISPRSJPRS.2019.10.017
.
Lin
L.
,
Wu
Z.
&
Liang
Q.
2019
Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework
.
Natural Hazards
97
(
2
),
455
475
.
https://doi.org/10.1007/s11069-019-03615-2
.
Liu
Z.
,
Liu
P.-W.
,
Massoud
E.
,
Farr
T. G.
,
Lundgren
P.
&
Famiglietti
J. S.
2019
‘Monitoring groundwater change in California's central valley using Sentinel-1 and GRACE observations’
.
Geosciences
9
(
10
),
436
.
https://doi.org/10.3390/geosciences9100436
.
Markert
K. N.
,
Markert
A. M.
,
Mayer
T.
,
Nauman
C.
,
Haag
A.
,
Poortinga
A.
,
Bhandari
B.
,
Thwal
N. S.
,
Kunlamai
T.
,
Chishtie
F.
,
Kwant
M.
,
Phongsapan
K.
,
Clinton
N.
,
Towashiraporn
P.
&
Saah
D.
2020
'Comparing Sentinel-1 Surface Water Mapping Algorithms and Radiometric Terrain Correction Processing in Southeast Asia Utilizing Google Earth Engine'
.
Remote Sensing
12
(
15
), p.
2469
.
Available at: https://doi.org/10.3390/rs12152469
.
Mayer
T.
,
Poortinga
A.
,
Bhandari
B.
,
Nicolau
A. P.
,
Markert
K.
,
Thwal
N. S.
,
Markert
A.
,
Haag
A.
,
Kilbride
J.
,
Chishtie
F.
,
Wadhwa
A.
,
Clinton
N.
&
Saah
D.
2021
Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine
.
ISPRS Open Journal of Photogrammetry and Remote Sensing
2
,
100005
.
https://doi.org/10.1016/j.ophoto.2021.100005
.
McFeeters
S. K.
1996
The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features
.
International Journal of Remote Sensing
17
(
7
),
1425
1432
.
https://doi.org/10.1080/01431169608948714
.
Minh
V. A.
,
Quang
D. N.
&
Dung
N. P.
2022
Evaluation of water indices for dynamic monitoring reservoir surface water using Landsat 8 data
. In:
International Conference on Flood and Sediment Management in River Basins for Sustainable Development (FSMaRT)
.
Da Nang University of Science and Technology
.
Munich-Re
.
1997
Flooding and Insurance
.
Munich Reinsurance Company
,
Munich, Germany
.
Paloscia
S.
,
Pettinato
S.
,
Santi
E.
,
Notarnicola
C.
,
Pasolli
L.
&
Reppucci
A.
2013
Soil moisture mapping using sentinel-1 images: Algorithm and preliminary validation
.
Remote Sensing of Environment
134
,
234
248
.
https://doi.org/10.1016/j.rse.2013.02.027
.
Pekel
J. F.
,
Cottam
A.
,
Gorelick
N.
&
Belward
A. S.
2016
High-resolution mapping of global surface water and its long-term changes
.
Nature
540
(
7633
),
418
422
.
https://doi.org/10.1038/nature20584
.
People's Committee of Quang Binh Province
2020
Report on Damage Caused by Rain and Flood From October 7 to 22, 2020
.
Quang Binh, Vietnam. People's Committee of Quang Binh Province
.
Pham-Duc
B.
,
Prigent
C.
&
Aires
F.
2017
Surface water monitoring within Cambodia and the Vietnamese Mekong delta over a year, with Sentinel-1 SAR observations
.
Water (Switzerland)
9
(
6
),
1
21
.
https://doi.org/10.3390/w9060366
.
Pickens
A. H.
,
Hansen
M. C.
,
Hancher
M.
,
Stehman
S. V.
,
Tyukavina
A.
,
Potapov
P.
,
Marroquin
B.
&
Sherani
Z.
2020
Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series
.
Remote Sensing of Environment
243
,
111792
.
https://doi.org/10.1016/j.rse.2020.111792
.
Poortinga
A.
,
Bastiaanssen
W.
,
Simons
G.
,
Saah
D.
,
Senay
G.
,
Fenn
M.
,
Bean
B.
&
Kadyszewski
J.
2017
A self-calibrating runoff and streamflow remote sensing model for ungauged basins using open-access earth observation data
.
Remote Sensing
9
(
1
),
86
.
https://doi.org/10.3390/rs9010086
.
Quang
D. N.
,
Ngan
V. H.
,
Tam
H. S.
,
Viet
N. T.
,
Tinh
N. X.
&
Tanaka
H.
2021a
Long-term shoreline evolution using DSAS technique: A case study of Quang Nam Province, Vietnam
.
Journal of Marine Science and Engineering
9
(
10
),
1124
.
https://doi.org/10.3390/jmse9101124
.
Quang
D. N.
,
Linh
N. K.
,
Tam
H. S.
&
Viet
N. T.
2021b
Remote sensing applications for reservoir water level monitoring, sustainable water surface management, and environmental risks in Quang Nam province, Vietnam
.
Journal of Water and Climate Change
12
(
7
),
3045
3063
.
https://doi.org/10.2166/wcc.2021.347
.
Schlund
M.
&
Erasmi
S.
2020
Sentinel-1 time series data for monitoring the phenology of winter wheat
.
Remote Sensing of Environment
246
,
111814
.
https://doi.org/10.1016/j.rse.2020.111814
.
Shashikant
V.
,
Shariff
A. R. M.
,
Wayayok
A.
,
Kamal
R.
,
Lee
Y. P.
&
Takeuchi
W.
2021
Utilizing TVDI and NDWI to classify severity of agricultural drought in Chuping, Malaysia
.
Agronomy
11
(
6
),
1243
.
https://doi.org/10.3390/AGRONOMY11061243
.
Tamiminia
H.
,
Salehi
B.
,
Mahdianpari
M.
,
Quackenbush
L.
,
Adeli
S.
&
Brisco
B.
2020
Google Earth Engine for geo-big data applications: A meta-analysis and systematic review
.
ISPRS Journal of Photogrammetry and Remote Sensing
164
,
152
170
.
https://doi.org/10.1016/J.ISPRSJPRS.2020.04.001
.
Thuy
N. B.
2019
The risk of typhoon and storm surge along the coast of Vietnam
.
Journal of Marine Science and Technology
19
(
3
),
327
336
.
https://doi.org/10.15625/1859-3097/19/3/13899
.
Thuy
D. T. N.
&
Thu
V. T. H.
2021
Applying the Livelihood Vulnerability Index (LVI) to Assess Livelihood Vulnerability to Climate Change: A Case Study in Quang Binh Province
.
National Economics University Publishing House
,
Hanoi
, pp.
213
230
.
Tran
K. H.
,
Menenti
M.
&
Jia
L.
2022
Surface water mapping and flood monitoring in the Mekong delta using Sentinel-1 SAR time series and Otsu threshold
.
Remote Sensing
14
(
22
).
https://doi.org/10.3390/rs14225721
.
Tung
T. T.
2021
Causes Assessment of Prolonged Flooding due to Floods and Orientation of Solutions for Flood Drainage for Le Thuy and Quang Ninh Districts, Quang Binh Province
.
Hanoi, Vietnam
.
Tung
T. T.
&
Quang
D. N.
2022
Analysing the causes of prolonged flooding in the Nhat Le river basin due to the historical flood in 2020
.
Journal of Water Resources & Environmental Engineering
81
,
82
90
.
Uddin
K.
,
Matin
M. A.
&
Meyer
F. J.
2019
Operational flood mapping using multi-temporal Sentinel-1 SAR images: A case study from Bangladesh
.
Remote Sensing
11
(
13
),
1581
.
https://doi.org/10.3390/rs11131581
.
Xu
H.
2006
Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery
.
International Journal of Remote Sensing
27
(
14
),
3025
3033
.
https://doi.org/10.1080/01431160600589179
.
Yang
X.
,
Qin
Q.
,
Yésou
H.
,
Ledauphin
T.
,
Koehl
M.
,
Grussenmeyer
P.
&
Zhu
Z.
2020
Monthly estimation of the surface water extent in France at a 10-m resolution using Sentinel-2 data
.
Remote Sensing of Environment
244
,
111803
.
https://doi.org/10.1016/j.rse.2020.111803
.
Zhou
S.
,
Kan
P.
,
Silbernagel
J.
&
Jin
J.
2020
Application of image segmentation in surface water extraction of freshwater lakes using Radar Data
.
ISPRS international journal of Geo-information
9
(
7
).
https://doi.org/10.3390/ijgi9070424
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).