Water contributes to all aspects of economic and social development. Water supply, sanitation, and a healthy environment form the basis of successful poverty reduction and shared-growth strategies, especially in developing countries [1
]. The spatial distribution of surface water informs about an important part of freshwater resources on Earth. Surface water bodies are dynamic in nature as they shrink, expand, or change their appearance with time, owing to different natural and human-induced factors. Variations in water bodies have been known to have significant impacts on other natural resources and human assets, and further influence climate change [2
]. Under extreme conditions, drastic changes of surface water could cause serious disasters such as floods and droughts—two life-threatening and financially expensive natural disasters [3
]. Therefore, it is crucial to understand the extent of water bodies and master their dynamics over time.
Remote sensing provides an efficient way of monitoring surface water bodies. Ever since the first remote sensing satellite was launched, it has been used for water detection [4
]. There are generally two types of remote sensors that are powerful for detecting water from space, namely optical sensors and microwave sensors. The optical sensors utilize the low reflectance of water bodies in near infrared channels to achieve delineation of water and land. The principle for water detection using microwave sensors is based on the low backscatter signal of water surfaces. Both types of sensors have their own advantages and disadvantages. The microwave sensors are able to penetrate cloud and some vegetation cover, due to their much longer wavelength. Therefore, they can work under all weather conditions and be effective in detecting water bodies beneath low vegetation.
Synthetic Aperture Radar (SAR) sensors have been widely applied in detecting and monitoring water bodies due to their suitable spatial resolution, as well as the ability to penetrate cloud and vegetation cover [5
]. Until recently, SAR sensors had a relatively low temporal frequency (about 24–35 days for acquisitions in the same geometric configuration) [7
], which hinders the intensive monitoring of surface water variation. The launch of the Sentinel-1A satellite in 2014 has improved temporal frequency of acquisitions (every 3–12 days in Europe), associated with a 20 m spatial resolution. The launch of the Sentinel-1B satellite in 2016 further improved the temporal frequency to 3–6 days. Sentinel-1 data can be freely downloaded by every user from the Scientific Data Hub of the European Space Agency (ESA).
Several methods for mapping surface water bodies from SAR imagery have been developed. Visual interpretation is always one reliable and simple approach [8
] except that it can be time consuming and subjective. Other popular methods include active contour models [9
], texture-based segmentations [10
], and grey-level thresholding [11
]. Among all these methods, grey-scale thresholding is still the most commonly used approach to detect water areas using SAR imagery [12
], due to its efficiency and acceptable accuracy. All pixels with a backscatter value lower than a specific threshold in an intensity image are classified as water. This threshold can be optimized using the Otsu algorithm [13
], or through careful manual adjustment.
Bearing in mind the multiple advantages, there are also disadvantages using SAR for surface water detection. A serious and inevitable one is the terrain effect, because SAR sensors acquire data in a side-looking geometry. Therefore, mountains and hills may block the transmission and reception of microwave pulses, and thus introduce shadows and blind areas on SAR images. This usually impacts the water detection algorithms, especially when using grey-scale thresholding method, because the blocked areas also have low backscatter just as water surfaces do [14
Surface water has a close relationship with the topography because of its fluid characteristics. Terrain information is thus useful in assisting water detection. There are many studies that have employed Digital Elevation Models (DEMs), digital representations of ground surface topography or relief, in detecting water bodies from remote sensing imagery [15
]. More and more DEM data sources are becoming accessible, including Shuttle Radar Topographic Mission (SRTM) DEM with up to 1 arc-second resolution and TanDEM-X with 12 m resolution. High quality global terrain datasets, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) with 30 m resolution, are now available, which promotes the application of DEMs in assisting mapping surface water globally [18
]. However, in most of these studies, DEM data are generally used for identifying those lower areas where water is more likely to be presented, or for estimating water level. The elevation itself is not ideal for helping delineating terrain shadows from water bodies, because shadow areas could be located in low areas and water could be held in high places sometimes.
Many terrain indices [19
] have been developed in order to build up a stronger connection between water presence and topography. Most of these terrain indices can generally be categorized into two groups, valley bottom based and drainage based. A classic and popular one in the first group is the Multi-resolution Valley Bottom Flatness (MrVBF) proposed by Gallant and Dowling [23
]. It tries to identify valley bottoms using a slope classification constrained to convergent areas, which is more likely to be occupied by water than simply low places. It has been used as an important data layer for mapping water bodies from remote sensing imagery in many studies [24
]. A representative index in the drainage based group is the Height Above Nearest Drainage (HAND) index, which was presented by Rennó et al. [27
] and implemented by Nobre et al. [28
]. This index also attracted a lot of attention and has been applied in many water detection studies [5
This study aims to conduct a comprehensive comparison on MrVBF and HAND indices, especially on their ability to assist surface water mapping using Sentinel-1A dual-polarized SAR data. A number of study sites with different topographic features under different inundation scenarios will be selected to showcase this comparison. Through this study, we are trying to elaborate the appropriate usage of these indices for the purpose of better surface water mapping, especially with SAR data.
3.1. Calculation of MrVBF Index
The MrVBF index can be used for identifying valley bottoms at a range of sizes and slopes. It does not rely on a prior identification of channels and can depict unchanneled valley areas including perched swamps and lake wetlands [23
]. It is an index that integrates the flatness and lowness characteristics of valley bottoms. Flatness is measured by the inverse of the terrain slope. Lowness is measured by a ranking of elevation with respect to a circular surrounding area. In order to identify different scales of valley bottoms, the flatness and lowness calculation are carried out through a series of scales. Both the measures of flatness and lowness were scaled to the range between 0 and 1, and then multiplied to construct the MrVBF index (Figure 2
). MrVBF values range from 0 to a positive integer value. Increasing MrVBF values indicate a reduction in slope and broadening of the valley.
There are six key parameters in calculating MrVBF from DEM data, the initial threshold for slope (Ts), the lower and upper thresholds for elevation percentile (TeL, TeU, respectively), the shape parameter for slope and elevation percentile (Ss, Se, respectively), and the maximum resolution (Rm). The calculation of MrVBF has been implemented in SAGA-GIS, with an interactive interface for parameter input.
The MrVBF algorithm was originally developed using 1s resolution DEMs, but can be applied at any resolution, provided appropriate adjustments are made. The link between size and flatness of valley bottoms is incorporated into the algorithm by reducing the slope threshold by a factor of 2 at each step, and it is assumed that the relationship between slope threshold, resolution, and MrVBF value does not vary between landscapes or with DEMs of different resolutions. If the DEM resolution is substantially different from 1s, the initial slope threshold (Ts) must be adjusted to retain the relationship between slope and resolution. In this study, Ts for 1s DEM was set to be 16%, while that for 3s was set to be 8%, as suggested by Gallant and Dowling [23
]. The other parameters used the same default values.
3.2. Calculation of HAND Index
The HAND index is generated using two sets of procedures. The first one is to condition the input DEM data, fixing sinks, defining flow paths, calculating an accumulated area map, and defining the drainage networks based on the accumulation map. The second procedure uses local drain directions and the drainage network to generate the nearest drainage map. Each pixel on this map is spatially associated with all DEM pixels draining into it. Then, each DEM pixel will have an elevation difference with its associated nearest drainage pixel. This elevation difference is assigned as the HAND index value for this DEM pixel, therefore, the unit of HAND value is meter.
The HAND model programme includes embedded routines for processing the DEM, filling sinks, and generating flow direction for each cell in the DEM using the so-called D8 approach [37
], and routines for determining flow paths and the drainage network, as well as routines for calculating the height difference between each DEM pixel and its nearest drainage pixel, which is defined from the drainage network (Figure 3
). This workflow has been implemented as Topography Tools in ArcGIS. These tools were employed in this study to generate HAND images from 1s and 3s SRTM DEMs.
Theoretically, the HAND value for drainage pixels is 0, and all the other pixels should have a HAND value great than 0, with a higher value indicating more unlikely to be held by water [27
], while with a HAND value that is closer to 0, it is easier to be inundated by water. Inherited from the DEM data, the minimum difference of a HAND value is also 1 m.
3.3. Water Mapping
Selected Level-1 Sentinel-1 GRDH images were preprocessed and resampled into 10 m pixel spacing grid backscatter images using the SAR Geophysical Retrieval Toolbox (SGRT) [38
] developed by Vienna University of Technology. SGRT is written in Python programming language and includes some external software modules, in particular ESA’s Sentinel-1 toolbox (S1TBX) for SAR data geocoding, radiometric corrections, and calibration. 3s SRTM data were employed for SAR terrain correction.
Reference water maps for each of the study cases were derived by manually thresholding both VV and VH backscatter data. Referring to a high-resolution Google Earth image, Sentinel-2, and Landsat images, some masking and digitalization work was also carefully conducted to improve the thresholding results. The final results were employed as the “actual” water maps for each study case.
SAR data can be quite useful for surface water mapping, but the data also have several disadvantages. Terrain effect is one of them that especially affects water mapping due to similar image values in terrain shadow regions and over water bodies. However, elevation information directly acquired from DEM is sometimes not suitable for assisting surface water mapping for two reasons. First, shadow areas could be located in low areas and water could be held in high places sometimes, which makes simply thresholding on elevation not reliable in delineating shadows and water bodies. Therefore, we need some kind of terrain index that has stronger connections with water presence than the original elevation. The second reason is that all DEMs have errors. However, when using terrain indices such as MrVBF and HAND, the absolute height error of DEM becomes less important, since we do not threshold against height in an absolute sense but rather are concerned with the valley bottom flatness, or relative spatial change in elevation, to quantify likely water presence.
This study conducted a comprehensive comparison on MrVBF and HAND toward their ability in assisting water mapping using Sentinel-1 C-band dual-polarized data. Through several case studies with different geomorphic types and inundation scenarios, we achieved the following three findings.
Both terrain indices are able to help improve water mapping significantly. HAND performs slightly better than MrVBF in most of these cases.
Optimal thresholds for both indices are not fixed. Adjustments are required to achieve optimal results. HAND is less sensitive to different thresholds, which is a good quality when being applied to larger areas with varied topography.
MrVBF classifies low and flat areas with more details than HAND does. For example, those areas that have a unique HAND value of 0 may have quite different MrVBF values, depending on the scale of valley bottoms. This advantage makes MrVBF sometimes more effective in eliminating false water bodies in mountainous areas.
These findings are able to provide guidance for terrain index selection and usage when using DEM data for assisting surface water mapping from SAR imagery. It is suggested that for large scale or global water mapping, HAND index would be a better choice as the auxiliary data to SAR imagery. While for water mapping in complex terrain areas, MrVBF or a combination of MrVBF and HAND is recommended. Using either index, the threshold should be carefully selected to achieve optimal water mapping results. The results of this study are useful for other studies that apply SAR data for water mapping, either regionally or globally.
Though it has been proven that simply thresholding on either VV or VH polarized image with the assistance from terrain indices is able to derive water maps with high accuracy, we are aware that the results could be further improved with more advanced water detection algorithms. For example, it is believed that the rate of backscatter change with local incidence angle may provide a better means to map the water extents than a single threshold on backscatter alone [41
]. Therefore, our future work will investigate robust water mapping methodologies that involve more variables, such as local incidence angle, terrain indices, or any other useful information.