High Resolution Sentinel-2 Images For Improved Bathymetric Mapping of Coastal and Lake Environments
High Resolution Sentinel-2 Images For Improved Bathymetric Mapping of Coastal and Lake Environments
High Resolution Sentinel-2 Images For Improved Bathymetric Mapping of Coastal and Lake Environments
Article
3 Center for Spatial Information Science, The University of Tokyo, Japan; songxuan@csis.u-tokyo.ac.jp
Abstract: Bathymetry of nearshore coastal environments and lakes are constantly reworking
because of the change in the patterns of energy dispersal and related sediment transport pathways.
Therefore, updated and accurate bathymetric models are a crucial component in providing basic
information for scientific, managerial, and geographical studies. Recent advances in satellite
technology have revolutionized the acquisition of bathymetric profiles, offering new vistas in
mapping. This contribution analyzed the suitability of high resolution Sentinel-2 images for
bathymetric mapping of coastal and lake environments. The bathymetric algorithm for satellite
imageries was developed based on the available high resolution bathymetric data for Mobile Bay,
Tampa Bay and Lake Huron regions obtained from National Oceanic and Atmospheric
Administration (NOAA) National Geophysical Data Center (NGDC). The results demonstrate that
the satellite derived bathymetry is efficient for retrieving depths up to 10 m for coastal regions and
up to 30 m for lake environment. The root mean square error (RMSE) varies between 1.99 m and
2.80 m for the three regions. A comparison of Sentinel-2 derived bathymetry is also carried with the
Landsat 8 OLI derived bathymetry. The results suggest Sentinel-2 images are capable of producing
much accurate bathymetric maps than those from the Landsat 8 OLI images. Our work
demonstrated that the freely available Sentinel-2 imagery proved to be a reliable method for
acquiring updated high resolution bathymetric information for large areas in short span of time.
1. Introduction
Aquatic environments are some of the dynamic regions of the earth. Among the aquatic systems,
bathymetry or the depths of underwater terrain is one of the most important parameter that is
constantly reworked and changing both in space and time. The rapid reworks in bathymetry are
because of the change in the patterns of energy dispersal and related sediment transport pathways
[1]. Clarke [2] studies indicated that huge turbidity currents cause’s bedform migration within a few
hours. Simons and Richardson, [3] studies observed positive correlation between bathymetric
changes and measured stream power in fluvial systems. Sea level rise, shoreline morphology
dynamics, beach nourishment, coastal erosion and accretion are other relevant forcing factors behind
bathymetric changes. In shallower waters, updated and detailed coastal topography and bathymetry
are critical for navigational purpose, pipeline constriction, exploration, defense and research
applications and other management and spatial planning developmental projects [4,5]. However, due
to the constant rework of bathymetry, mapping and measuring of these alterations requires a shift
from static management measures to near real-time management procedures [6].
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Traditional or static methods for monitoring and measuring bathymetry rely on field surveys
utilizing echo sounding and mapping using multi-beam and side scan sonar. However, such
approaches are characterized as being costly, labor-intensive and time-consuming techniques. In
single beam echo sounders, a sound pulse from the vessel carrying the echo sounder instrument is
sending underneath and listening until the echo from the bottom is heard, thus end up in providing
depth at a single point. The water depth is then estimated by dividing the speed of sound by half of
the time it takes for the echo to be heard. The multi-beam and side scan sonars transmits multiple
beam of sound which represent the intensity and amplitude of reflected acoustic signals from the sea
floor resulting in an image of its physical reflectance and scattering characteristics. Although multi-
beam echo sounding (MBES) surveys produce accurate bathymetric information of surveyed area,
this method is constrained by spatial and temporal scale, expensive to operate, inability to survey in
shallow seas, and marine protected areas [7,8]. It is estimated that at the best resolution of MBES,
more than 200 ship-years and billions of dollars would take to complete a swath survey of the sea
floor [9]. Thus, the current bathymetric charts from ship-based surveys are inadequate for site specific
application because only a small fraction of the aquatic environments has been surveyed.
An increasing body of contemporary literature shows the potential of remotely sensed data in
bathymetric studies [10–13]. The major motivation for the usage of satellite in bathymetric surveys is
that their uniform and comprehensive global coverage that can contribute to a better understanding
of the topographic changes instantaneously and spatially. Radar altimeters abroad the spacecraft
ERS-1 and Geosat have surveyed over global seas to obtain a high accuracy and moderate spatial
resolution bathymetric information [14]. Dixon and Naraghi, [15] work summarizes the principles of
satellite altimeter measurements for predicting seafloor topography. The gravity anomalies estimated
from geoid undulations are highly correlated with seafloor topography, and these anomalies helps
in mapping bathymetry with a radar altimeter. ERS-1 completed its near global mapping of sea
surface topography in 1995, which was then used to reproduce the seafloor topography for data
constrained and deeper oceans [16]. Because radar altimeter uses gravity anomaly to correlate
bathymetry, this method is largely applicable for deep sea regions for mapping large sea mounts and
guyots [17]. Furthermore, the estimation of bathymetry from gravity anomaly includes a number of
mathematical models and thus is a complicated approach. On other hand, airborne LiDAR
bathymetry (ALB) is a useful technique for measuring the moderately to shallower deep coastal
waters and lakes (30 m – 50 m depth) from a low-altitude aircraft using a scanning, pulsed laser beam
[18]. LiDAR offers about 70% reduction in operating costs when compared with standard ship
surveys [18], however is again having spatial and temporal constraints. Satellite LiDAR (e.g., ICESat)
have also been used to estimate water depth in clear waters with high accuracy in conjunction with
Spectro-Radiometers and other remote sensing data [19,20].
Multispectral remote sensing datasets that are characterized with high spatial and temporal
resolutions is the most frequently used method to estimate bathymetry on shallow water bodies such
as coastal areas, estuaries, rivers and lakes [5,21]. Optical data approximates the radiative transfer in
water using an empirical approach to model reflectance and measured bathymetry via least square
regression analysis [22]. The present work explores the suitability of Sentinel-2 for retrieval of
bathymetry of shallow coastal areas and lakes, and comparison with Landsat 8 OLI aiming to provide
a new algorithm for obtaining and updating bathymetry which is under continual reworking
processes.
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with confidence. Based on availability of high resolution bathymetric dataset, we chose three study
area in the conterminous United States. They are: i) Mobile Bay, Mississippi, ii) area adjoining Tamba
Bay and iii) Lake Huron (Figure 1).
Figure 1. Location of study area: i) Mobile Bay, Mississippi, ii) area adjoining Tampa Bay, and
iii) Lake Huron.
2.2. Dataset
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Worldwide Reference System (WRS-2) path/row coordinate system, is used in this study. Cloud free
dataset one each for the three study sites are downloaded for both the Sentinel-2 MSI and Landsat-8
OLI sensor. Date of acquisition and path, row/tiles details are also presented in Table 1.
Table 1. Key summary of Landsat 8 Operational Land Imager (OLI) and Sentinal-2A Multi-
Spectral Imager (MSI) spectral bands, and date of acquisition, path, row / tiles details of images used
in this study.
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In this study, we utilized the DN values of band 2 (blue) and band 3 (green) of both Landsat 8
OLI and Sentinal 2A MSI products (Landsat: band 2 = 482 nm and band 3 =561 nm; Sentinal: band 2
= 490 nm and band 3 = 560 nm). For this, the Top of Atmosphere (ToA) OLI and MSI products are
first atmospherically corrected for the effects of atmospheric gases and aerosols to yield surface
spectral reflectance using dark object subtraction (DOS) method. In DOS method, we assume that the
dark objects in an image reflect no light, and any value captured by satellite sensor are due to the
atmospheric scattering [16]. The atmospheric scattering effect is then removed by subtracting the
value captured in dark object pixel from every pixel in the band. The corrected surface reflectance
data are then used for estimating the water depth.
Following Pacheco et al. [5], the band ratio of blue by green can provide satellite derived water
depth by linear solution of water reflectance and bathymetric depth. This spectral band-ratio method
employs an empirically derived formula to relate lake depths to the ratio of the reflectance of two
spectral bands. To apply the multiple linear regression, the depth data points were extracted from
bathymetric LiDAR for three different sites at exactly the same points as were the data retrieved by
the Landsat 8 image and Sentinel OLCI images. The points for constructing linear model are
randomly selected based on the size of the available images and DEM; 6000 for Mobile Bay, 3000 for
Tampa Bay, and 1000 for Lake Huron. A limitation of this comparison is the fact that the bathymetric
depth dataset used are dated much older than the L8 and OLCI scenes. Therefore, a perfect agreement
between SDB and surveyed maps is not expected, given that morphological differences are likely to
occur in a moderately energetic nearshore system comprising barrier islands and tidal inlets exposed
to dynamic oceanographic conditions.
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Figure 2. Flowchart for deriving Bathymetric maps from Sentinel-2 MSI and Landsat 8 OLI images.
The equation to derive depth from Sentinel-2 MSI for Mobile Bay area is follows:
𝑆𝑑𝐵 = −52.51 × 𝑅𝑟𝑠 + 42.97 (1)
The equation to derive depth from Landsat 8 OLI for Mobile Bay area is follows:4
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Figure 3. Scatter plot of blue/green (B/G) band versus surveyed depth for Mobile Bay; a) Sentinel
2 MSI B/G vs surveyed bathymetry up to 30 m depth, b) Landsat 8 OLI B/G vs surveyed bathymetry
up to 30 m depth, c) Sentinel 2 MSI B/G vs surveyed bathymetry up to 10 m depth and d) Landsat 8
OLI B/G vs surveyed bathymetry up to 10 m depth.
The equation to derive depth from Landsat 8 OLI for Tampa Bay area is follows:
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Figure 4. Scatter plot of blue/green (B/G) band versus surveyed depth for Tampa Bay; a) Sentinel
2 MSI B/G vs surveyed bathymetry up to 30 m depth, b) Landsat 8 OLI B/G vs surveyed bathymetry
up to 30 m depth, c) Sentinel 2 MSI B/G vs surveyed bathymetry up to 10 m depth and d) Landsat 8
OLI B/G vs surveyed bathymetry up to 10 m depth.
The equation to derive depth from Sentinel-2 MSI for Lake Huron is follows:
𝑆𝑑𝐵 = −31.14 × 𝑅𝑟𝑠 + 17.49 (5)
The equation to derive depth from Landsat 8 OLI for Lake Huron is follows:
𝑆𝑑𝐵 = −36.29 × 𝑅𝑟𝑠 + 41.49 (6)
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Figure 5. Scatter plot of blue/green (B/G) band versus surveyed depth for Lake Huron; a)
Sentinel 2 MSI B/G vs surveyed bathymetry up to 30 m depth, b) Landsat 8 OLI B/G vs surveyed
bathymetry up to 30 m depth, c) Sentinel 2 MSI B/G vs surveyed bathymetry up to 10 m depth and
d) Landsat 8 OLI B/G vs surveyed bathymetry up to 10 m depth.
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Figure 6. Scatter plot of blue/green (B/G) band versus surveyed depth for all three study area; a)
Sentinel 2 MSI B/G vs surveyed bathymetry up to 30 m depth, b) Landsat 8 OLI B/G vs surveyed
bathymetry up to 30 m depth.
The RMSEs estimated from the site-specific algorithm (SSA) for Mobile Bay using 2000 random
depth values are 2.26, and 2.54 respectively for S2 and L8 images; whereas the same for integrated
model (IM) are 4.84 and 5.18 respectively (Table 2). Since the RMSE estimated for integrated model
for both the sensors are larger (4.18 and 5.18), they are not shown in maps. As observed, large
differences occur in areas with depths of 2–4 m and 6-8 m (Fig. 8a). Up to 6 m, S2 SSA algorithm
overestimate the depth values and for depths more than 6 m, vice versa ensues. Nevertheless, the
satellite derived bathymetric maps are effective for representing the nearshore isobaths as well as the
shapes of the bottom morphologies (Figure 7a).
For Tampa Bay the RMSEs estimated are 2.80, 2.62, 2.50 and 5.67 respectively for S2 SSA, S2 IM,
L8 SSA and L8 IM. Two thousand random depth points are used for performing statistical analysis.
As observed from the Figure 9a – 9c and the statistical analysis, L8 SSA derived bathymetry have an
edge over S2 images for this particular region (bias 0.24). Nevertheless, S2 images also produced a
good representation of bottom topography with a bias of only 0.58 m. This can also been seen from
Figure 8b.
The bathymetric map produced for Lake Huron using S2 SSA found to be a close approximation
of the actual bottom topography (Figure 10a – 10c). The bias and RMSE estimated are 0.07 and 1.99
respectively for this model. The differences are concentrated in the 0 – 3 m class, near the south-
western region (Figure 7c). The other models show a RMSEs of 3.30, 4.74 and 5.07 respectively for S2
IM, L8 SSA and L8 IM. Again because of larger bias and RMSEs, the integrated model results are not
shown in the maps.
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Mobile Bay
Surveyed S2 SSA S2 IM L8SSA L8 IM
RMSE 2.26 4.84 2.54 5.18
MAX -10.00 -13.32 -14.48 -10. 0 -13.94
MIN 0.00 3.92 6.25 -0.40 5.45
MEAN -4.58 -5.51 -9.21 -5.39 -9.28
STD 2.20 2.79 1.78 2.71 2.25
BIAS 0.93 4.63 0.81 4.70
Tampa Bay
Surveyed S2 SSA S2 IM L8SSA L8 IM
RMSE 2.80 2.62 2.50 5.67
MAX -29.63 -32.63 -14.80 -19.87 -7.58
MIN 0.00 10.36 3.93 4.09 2.68
MEAN -7.46 -6.88 -6.37 -7.22 -2.16
STD 2.82 3.25 1.06 3.73 1.59
BIAS 0.58 1.09 0.24 5.30
Lake Huron
Surveyed S2 SSA S2 IM L8SSA L8 IM
RMSE 1.99 3.30 4.74 5.07
MAX -30.00 -30.20 -29.68 -22.21 -20.50
MIN 0.00 6.42 9.87 10.59 5.06
MEAN -7.15 -7.22 -4.93 -7.94 -9.38
STD 5.95 6.28 6.78 5.45 4.25
BIAS 0.07 2.22 0.79 2.23
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Figure 8. Histogram of depth classes for comparison between different bathymetric maps.
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Figure 10. Bathymetric map of Lake Huron. a) surveyed bathymetry, b) Sentinel-2 derived
bathymetry and c) Landsat 8 OLI derived bathymetry from site specific algorithm.
Figure 11 reports the six-representative bathymetric profile for Mobile Bay. Three of the studied
bathymetric longitudinal profiles exhibit a decent match with the observed profile (Figure 11a, 11b
and 11f). This is a good first indication that the Sentinel-2 can provide a reasonable estimate of the
real bathymetry. These profiles have at worst 2 m differences in depth. The mean differences for these
three profiles range from 0.25 m to 1 m. Nevertheless, the other profiles shown in Figure 11c, 11d and
11e also follows the general trend of observed bathymetry at places but with a worst difference of
6.94 m. Note that the mean differences for these three profiles is less than 1 m (Table 1A) indicating
reasonable quantitative match. The differences observed for L8 derived profiles are found larger than
that observed for S2 profiles. The worst case for L8 is 14.32 m difference with observed and mean
varies from 0.64 to 4.26 m for all six profiles.
Bathymetric profiles derived for Tampa Bay are shown in Figure 12. Visual interpretation
suggests there exists reasonable match of surveyed bathymetry with S2 SSA in profile 3 (Figure 12c),
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profile 5 (Figure 12d) and profile 6 (Figure 12). Quantification shows at worst case the difference is
2.95 m and mean difference of less than 1.28 m for these three profiles. Other profiles analysed shows
higher difference with the surveyed one (3.7m to 6.21 m) however with a mean difference ranging
between 0.13 m and 1.75 m (Table 1B ). Similar to the Mobile Bay profiles, L8 SSA bathymetry for
Tampa Bay also shows higher difference with original indicating improved retrieval from Sentinel
sensors.
Accordance to what is observed in the RMSE analysis, visual comparison reveals all the six
profiles for S2 SSA in Lake Huron shows a close match with the observed bathymetry (Figure 13).
Quantification shows the mean difference is between 0.19 to 0.63 m except for profile 5 shown in
figure 13e. The maximum difference exceeds 6 m at places but attributed to the larger depth retrieval
and proportion of area it estimated. Difference from L8 SSA are slightly larger, a fact that can be
explained simply by the RMSE and bias estimation.
Figure 11. Longitudinal profile analysis of Mobile Bay bathymetry derived from Sentinel-2 MSI
and Landsat 8 OLI images and comparison with the surveyed bathymetry obtained from NOAA’s
NGDC.
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Figure 12. Longitudinal profile analysis of Tampa Bay bathymetry derived from Sentinel-2 MSI
and Landsat 8 OLI images and comparison with the surveyed bathymetry obtained from NOAA’s
NGDC.
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Figure 13. Longitudinal profile analysis of Lake Huron bathymetry derived from Sentinel-2 MSI
and Landsat 8 OLI images and comparison with the surveyed bathymetry obtained from NOAA’s
NGDC.
4. Conclusion
The bathymetric map generated by S2 SSA and L8 SSA algorithms is by large effective in
mapping bottom topography of Mobile Bay, Tampa Bay and Lake Huron, despite distinct differences
in the morphometry and location. Inherent errors, smoothening and morphological variation
happened for the time difference between the surveyed bathymetry and this study is not considered
in our analysis. Therefore, it is reasonable to assume that if outliers were removed, the SSA algorithm
can retrieve depths between 0 and 10 m for coastal areas, and up to 30 m for lake regions in optically
clear waters. The value of RMSE ranges between 1.99 m (Lake Huron) and 2.80 m (Tampa Bay) for
S2 SSA whereas the RMSE estimated from L8 SSA is much higher and ranges between 2.50 m (Tampa
Bay) and 4.74 m (Lake Huron). Although there is no exact match with the observed profiles, it is clear
that even if the SSA algorithm is used, the worst-case scenario is 3 m difference for coastal areas from
Sentinel sensor. In order to validate the applicability of this method to other areas and development
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of regional bathymetric models, we plan to investigate ways of improving the current model by
analyzing more images from different time periods, in particular to examine methods for addressing
suspended sediment particles. Result of this study is a good first indication that the Sentinel-2 can be
utilized for remotely sensed bathymetry extraction for coastal and lake areas and complement the
data from survey sources.
Author Contributions: Conceptualization, Y.A. and D.J.; methodology, Y.A.; software, R.A.; validation, Y.A.,
D.J. and R.A.; formal analysis, Y.A.; investigation, Y.A.; resources, S.X.; writing—original draft preparation, Y.A.,
D.J., and S.X.; writing—review and editing, R.A.; supervision, R.A.; project administration, S.X.
Acknowledgments: Authors are thankful to NOAA’s NGDC for providing freely available bathymetric DEMS;
and NASA, USGS and ESA for access to the Landsat 8 and Sentinel-2 satellite imageries.
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