Satellite-derived shoreline and nearshore bathymetry

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Traditional nearshore bathymetric surveys rely on in situ sonar measurements, typically using multibeam echosounders, which provide high accuracy but are costly, labor-intensive, and spatially limited. As a result, survey frequency is low and large areas of the global nearshore zone remain unmapped. Similarly, long-term shoreline change is often inferred from historical imagery, as systematic field monitoring is uncommon.

Remote sensing offers a scalable alternative for shoreline detection and nearshore bathymetric mapping. Fixed coastal systems (video and radar) and airborne sensors (LIDAR, photogrammetry, hyperspectral imaging) can provide accurate measurements but require significant investment in equipment and logistics and typically cover limited areas.

Satellite remote sensing complements these techniques by providing repeated observations with very large spatial coverage, much of it from freely accessible satellite missions. This article focuses on satellite-based approaches for shoreline detection and nearshore bathymetry, which can be grouped into three complementary techniques: (1) passive optical methods based on water color, (2) passive wave-based methods that infer depth from wave propagation, and (3) active space-borne LIDAR. Color-based and lidar bathymetry require sufficient water transparency, whereas wave-based bathymetry requires substantial clearly detectable waves whose propagation is influenced by water depth. Together, these techniques enable cost-effective, large-scale monitoring of coastal morphology under a wide range of environmental conditions.

Satellite-derived bathymetry is particularly useful for reconnaissance, regional mapping and monitoring in areas where conventional surveys are sparse. It does not generally replace hydrographic surveys where detection of navigational hazards and compliance with hydrographic accuracy standards are required[1].



Remote sensing reflectance

Optical remote sensing provides a measure of reflectance [math]R_{rs}(\lambda)[/math] in different wavelength bands [math]\Delta \lambda[/math]. Remote sensing reflectance is defined as the ratio of water-leaving radiance to downwelling irradiance, [math]R_{rs}=L_w/E_d[/math].

Water-leaving radiance [math]L_w[/math] is the spectral radiant power leaving the sea surface in the sensor viewing direction in a wavelength band [math]\Delta \lambda[/math], expressed per unit projected area and per unit solid angle (units are: watt per m2 sea surface, per nm wavelength and per steradian sr). Downwelling irradiance [math]E_d[/math] is the incoming light energy in a wavelength band [math]\Delta \lambda[/math] received directly and indirectly from the sun per unit time and unit sea surface (units are: watt per m2 sea surface and per nm wavelength). The reflectance [math]R_{rs}[/math] has units sr-1 and depends on wavelength, water absorption and scattering, bottom reflectance and depth, the solar zenith angle and the sensor viewing angles.

Water-leaving radiance includes light scattered by the water column, [math]L_{water}[/math] (depending on water constituents, e.g. suspended sediment, Chlorophyl, gelbstoff), and light reflected by the seabed [math]L_{bed}[/math]. The radiance received by the sensor additionally contains reflected skylight and direct solar reflection (sunglint) from the sea surface. Corrections for these contaminating surface contributions are necessary to obtain the desired water-leaving radiance signal [math]L_w[/math]. Sunglint occurs in imagery when the water surface orientation is such that the sun is directly reflected towards the sensor; and hence is a function of sea surface state, sun position and viewing angle.

Downwelling surface irradiance [math]E_d[/math] (units W per m2 sea surface and per nm wave length) is the sum of the direct and diffuse components of sunlight attenuated by reflection at the air-sea interface. Reflectance of the direct sun beam depends on the solar zenith angle and the index of refraction of seawater. Reflectance of the diffuse irradiance is related to the roughness of the sea surface. Downwelling irradiance can be estimated from analytical expressions[2] or from measurements[3]. A more detailed discussion the optical properties of coastal waters can be found in Light fields and optics in coastal waters.

Shoreline detection

The land–water interface can be detected in satellite imagery by combining information from multiple spectral bands, which exhibit distinct responses over water and sand:

Band Water Sand
Blue (490 nm) high penetration and reflectance low to moderate reflectance
Green (560 nm) moderate penetration and reflectance moderate reflectance
Red (650 nm) absorption and low reflectance moderate to high reflectance
Near-infrared (NIR, 842 nm) very low reflectance high reflectance
SWIR (SWIR1, SWIR2, 1600-2200 nm) completely absorbed, almost no reflectance very high reflectance


These bands are available from satellite sensors such as Landsat (30 m resolution), Sentinel-2 (10 m) and PlanetScope (3–4 m, no SWIR bands). The spectral bands available differ among satellite sensors; algorithms relying specifically on SWIR bands cannot be applied to sensors that do not provide these bands.

Several shoreline extraction algorithms have been developed, including CoastSat [4], CASSIE [5], SHOREX [6], HighTide-SDS [7], and Shoreliner[8].

Most approaches rely on spectral water indices to separate land and water. A commonly used index is the Modified Normalized Difference Water Index (MNDWI),

[math]MNDWI=\dfrac{Green – SWIR1}{Green + SWIR1} [/math].

Shoreliner instead applies the Subtractive Coastal Water Index (SCOWI), [math]\quad SCoWI = B + 2(G − NIR) − 0.75 SWIR1 − 0.5 SWIR2[/math],

designed to enhance coastal waterlines and enable sub-pixel shoreline detection.

Once a water index is computed, the land–water interface is extracted using image-processing techniques, such as contouring of a land/water threshold; maximum-gradient contouring methods; and soft classification techniques.[9]

The total shoreline detection error (including bias) is typically 5–15 m on microtidal coasts and approximately twice this range on macrotidal coasts. Errors arise primarily from image geolocation uncertainty and from the difficulty of distinguishing shallow water from wet sand. In addition, the instantaneous waterline position is affected by wave run-up (including wave set-up) and the tide. If beach slope information is available, tidal effects can be corrected by converting vertical water-level variations into horizontal shoreline shifts. Wave set-up can also be corrected if offshore wave conditions are known. However, the oscillatory swash component of wave run-up cannot be corrected on individual satellite images and is therefore treated as noise.

Because sand can move across the entire active coastal zone, neither the instantaneous waterline nor the high-water shoreline is a reliable indicator of medium- to long-term beach evolution (explained in the article Coastline). Furthermore, changes in sediment volume within the active coastal zone cannot be directly inferred from optical satellite imagery alone.

Satellite color-based bathymetry

Sunlight reflected from the seabed in shallow water contains information on the water depth. A simple example is the shallow vs. deep ends of a swimming pool that appear as different colors to the human eye.

Several satellites, such as Landsat 8, Sentinel 2, Planet, WorldView-2, Pleiades, QuickBird, IKONOS, SPOT, are equipped with optical sensors that measure sunlight reflected from the sea surface in different wavelength bands. Google Earth Engine is a cloud-based geospatial computing platform which offers a petabyte-scale archive of freely available optical satellite imagery. Among characteristics, it features the whole archive of Landsat, the first three Sentinel missions, and full Moderate Resolution Imaging Spectroradiometer (MODIS) data and products. Spectra recorded by these optical sensors contain information on the bathymetry of nearshore coastal waters. A plethora of factors affect the state of the atmosphere (e.g., haze, aerosols, and clouds), sea surface (e.g., sunglint, sky glint, and white caps) and water column (e.g., sedimentation, turbidity and variable optical properties) originating from either atmospheric interference or ocean surface. For instance, the top-of-atmosphere signal for blue-to-red spectral bands could consist of up to 90% of scattering due to ozone and Rayleigh effects[10]. The influence of these factors has to be removed by available preprocessing algorithms to obtain the remote sensing reflectance [math]R_{rs}[/math].

Several methods have been developed to extract the seabed bathymetry from the remote sensing reflectance. Most methods are based on models that require bathymetric data from other sources for calibration. One of the possible sources is the LIDAR camera mounted on the ICESat-2 satellite.

Optical methods to derive bathymetry from satellite imagery are restricted to shallow coastal areas. In deep water, the light in the water column is too strongly attenuated for producing a significant reflected signal from the seabed. In clear water the maximum depth is about 25 m and in turbid water less than about 5 m[11]. Under favourable conditions, vertical errors can be of order 10% of the water depth, but accuracy depends strongly on water clarity, bottom type, sensor characteristics and the availability of calibration data.

Retrieving water depth

Several methods have been developed to retrieve water depth from remote sensing reflectance spectra. A few popular methods are briefly described below.

Semi-analytical methods

Semi-analytical methods were developed by Lee et al. (1999)[12] who analyzed water’s absorption and backscatter properties (including the influence of suspended matter, phytoplankton and gelbstoff). From these properties they derived semi-empirical formulas for the contributions of absorption and backscatter to radiative transfer in seawater and a corresponding formula for the remote sensing reflectance (see Appendix Semi-Analytical Model). In shallow waters, the remote sensing reflectance depends not only on the absorption and scattering properties of dissolved and suspended material in the water column, but also on the bottom depth and the reflectivity of the bottom, or the bottom albedo. For the bottom depth to be retrieved, the water-column contributions to the reflectance have to be removed. The bathymetry of a shallow-water coastal field site then can be obtained by matching the semi-empirical formula with observed remote sensing reflectance values for a particular wavelength. The accuracy is on the order of ± 1 m for depths less than about 10 m. In principle, semi-analytical inversion can retrieve depth without local bathymetric calibration data, provided that the optical properties of the water column and seabed can be adequately represented.

Semi-empirical methods

Semi-empirical methods use relationships between reflected radiation and water depth without considering light transmission in water. In addition to depth values measured in a number of locations, semi-empirical methods require certain bands in the visible wavelength, with blue and green being the most widely used, as inputs in simple or multiple linear regressions. Two well-known methods for estimating bathymetry are the linear transform (Lyzenga et al. 2006[13]) and the ratio transform (Stumpf et al. 2003[14]), see Appendix Semi-Empirical Model.

The physical concept underlying the ability to estimate bathymetry from multi-spectral imagery is the wavelength-dependent attenuation of light in the water column. The ratio transform method with the blue and red bands may perform better than with the blue and green bands in very shallow water because the red band or bands with longer wavelengths have stronger absorption than the green band or bands with shorter wavelengths[15]. With the decrease of water depth, the sensitive wavelength band in water-leaving reflectance for the water depth varies from the shorter wavelength band to the longer wavelength band (from the green band to the red-edge band).

Empirical method

If the water depth is known at a sufficiently large number of locations, machine learning (ML) techniques can be used to predict bathymetry directly from the remote sensing reflectance [math]R_{rs}(\lambda)[/math] in different wavelength bands. Compared with the classic method, machine-learning methods do not require an explicit physical model of light attenuation, but they generally require representative training bathymetry and their performance may deteriorate when applied outside the conditions represented in the training data. The underlying assumption is that variations in spectral reflectance contain a reproducible depth-dependent signal that can be distinguished from variations related to water properties and seabed reflectance. The empirical approach is easy to apply and tools are readily available to process and analyze data, which are major advantages. Recent ML techniques facilitate efficient processing of large remote-sensing datasets. The limitations are requirement of in-situ data and the adaptation to a specific site; results usually cannot be transferred to other sites.

Machine learning techniques have also been applied to remote sensing reflectance without atmospheric correction by using the near-infrared (NIR) wavelength band. The NIR band is widely used for atmospheric correction; when training the model using the remote sensing images without atmospheric correction, including the NIR band helps to improve the training accuracy[16]. Locally trained empirical models can sometimes perform adequately on top-of-atmosphere reflectance, but this increases dependence on the atmospheric conditions represented in the training dataset and generally reduces transferability.

Popular machine learning techniques for retrieving the bathymetry directly from remote sensing reflectance are Artificial Neural Networks (ANN)[17], Random Forest Regression Trees (RF)[18] and Support Vector Regression (SVR) algorithms[19]. Statistical preprocessing techniques such as Principal Component Analysis have also been used in multispectral bathymetry retrieval[20],

Spaceborne LIDAR

Airborne lidar bathymetry provides an efficient alternative to vessel-based echo-sounding techniques, particularly in transparent shallow waters. An introduction to the use of airborne LIDAR is given in the article Use of Lidar for coastal habitat mapping.

ICESat-2 ATLAS is a space-based laser altimeter launched in September 2018. It is a photon-counting lidar with a revisit period of 91 days. ATLAS uses a green laser (532 nm) firing 10,000 pulses per second, with footprints of about 11 m diameter and an along-track sampling interval of about 0.7 m. ATLAS produces six ground tracks arranged in three beam pairs, each consisting of a strong and a weak beam. The three beam pairs are separated by about 3.3 km across-track, while the strong and weak beams within each pair are about 90 m apart. The detector is very sensitive, so the raw photon data in the ATL03 dataset are noisy, especially during the day due to solar activity[21].

The ICESat-2 geolocation photon data are available in the ATL03 product, which is disseminated through the National Snow and Ice Data Center (NSIDC). ICESat-2 was not originally designed for marine applications and it samples along widely spaced narrow tracks rather than producing continuous area coverage. However, the use of a 532-nm laser and the high-accuracy of the altimetry give it great potential in nearshore bathymetry. Bottom returns have been detected to several tens of metres depth in exceptionally clear water, but practical penetration depth varies strongly with water clarity and seabed reflectance[11]. Refraction and tide corrections are essential for nearshore bathymetry when using the ATLAS remote sensing images. Even if the spacing between the beam pairs is too wide to generate high-resolution bathymetric results, water depth profiles derived from ICESat-2 can be used to feed the (semi)empirical methods for optical satellite-derived bathymetry with calibration/control data.

Satellite wave-based bathymetry

Depth information can also be retrieved by analyzing wave propagation characteristics detected by satellite remote sensing. Satellite wave-based bathymetry does not require in situ data for calibration or training. Unlike color-based and lidar bathymetry, wave-based bathymetry does not require the seabed to be optically visible. The technique is based on a formula that expresses the wave propagation speed (wave celerity) in shallow water as a function of wavelength and water depth, see Shallow-water wave theory. This formula holds in principle for uniform depth, but is generally a reasonable approximation in situations where the depth is smoothly varying. Values of the wave celerity and the wavelength can be derived from images of the sea surface at successive times. The water depth can then be computed from the analytical formula of the wave celerity. This technique has been commonly applied for sea surface images obtained from shore-based video cameras such as ARGUS [22][23] or from images taken by drones[24][25].

Wave propagation from satellite images

The wave celerity can be derived from a time series of satellite images of the sea surface if certain requirements are met[26]

  1. the spatial coverage comprises at least one wavelength,
  2. the pixel diameter is much smaller than the wavelength (to enable wavelength estimation),
  3. the timelapse between two successive images is smaller than the wave period (to enable identification of individual waves),
  4. the ratio of pixel diameter and timelapse is smaller than the wave celerity (to enable estimating the shift in wave phase),
  5. the overlap of two successive images contains the same nearshore area.

These requirements restrict the use of satellite images, because the pixel diameter is large, O(10 m), and the timelapse between two images is often longer than the ratio of area width and satellite speed, meaning that successive images do not have sufficient overlap. In the best case 2 successive images are available, if there is a small timelapse of O(1 s) between images taken for different wavelength bands. For example, for Sentinel-2’s bands with best resolution (10 m) and time lapse of 1 s, phase shift estimation requires about 7-8 pixels, meaning that wavelengths smaller than 70–80 m are not sufficiently resolved for phase shift estimation[27].

The identification of waves in the remote sensing images is based on the analysis of variations in pixel intensity, see Appendix C. A complicating factor is the irregular character of natural waves, with varying wave height, wave period and wave incidence direction. The uncertainty in the results is largely due to the uncertainty in the estimation of the wave celerity. Currently, the accuracy margins of depths obtained from satellite-based techniques are substantially larger than those of operational shore-based video cameras, and an order of magnitude larger than those of in-situ echo-soundings[28][29].

Jurisdictional issues

In situ bathymetric surveys in territorial waters typically require permission of the host country. Often, this permission comes with the request that survey findings are not made publicly available and the host country receives a copy of the data. Many countries are reluctant to grant foreign entities access to territorial waters, due to the risk that bathymetric survey information could be used to facilitate undersea navigation by military vessels[30]. Bathymetry from space that makes no use of in-situ data circumvents host country permissions.


Appendices

Appendix A: Semi-Analytical Model

The below-surface remote reflectance [math]r_{rs}[/math] is given by[12]

[math]r_{rs} = \Large\frac{r_{up}}{r_{in}}\normalsize = \Large\frac{r_{water}}{r_{in}} + \frac{r_{bed}}{r_{in}}\normalsize , \qquad (A1)[/math]

where [math] r_{in}(\lambda)[/math] is the downwelling irradiance below the sea surface. The upwelling radiance below the sea surface, [math]r_{up}(\lambda)[/math], has contributions from water column backscattering, [math]r_{water}[/math], and from seabed reflection, [math]r_{bed}[/math]. In optically deep water the seabed contribution is negligible. The deep water reflectance is thus equal to [math]r_{rs}^{\infty} = \Large\frac{r_{deep water}}{r_{in}} .[/math]

We now make 2 assumptions:

  1. The incoming light is approximately exponentially attenuated with depth z, with a diffuse attenuation coefficient K determined by absorption and scattering in the water column.
  2. The attenuation coefficient is the same in shallow water and deep water.

Considering that downwelling and upwelling attenuation can be different, the formula for the remote sensing reflectance reads[31]:

[math]r_{rs} = r_{rs}^{\infty} \, \Big(1 - e^{- (K_D +K_U^C)h} \Big) + \dfrac{R_B}{\pi} e^{-(K_D +K_U^B)h} . \qquad (A2)[/math]

Symbols: [math]K_D=[/math] the vertically averaged diffuse attenuation coefficient for downwelling irradiance, [math]K_U^C=[/math] the vertically averaged diffuse attenuation coefficient for upwelling radiance from water-column scattering, [math]K_U^B=[/math] the vertically averaged diffuse attenuation coefficient for upwelling radiance from bottom reflectance, and [math]R_B=[/math] the bottom irradiance reflectance.

Derivation of the remote sensing signal observed by satellite [math]R_{rs}[/math] from the below-surface reflectance [math]r_{rs}[/math] requires several correction factors: the refractive index of water, the water-to-air internal reflection, the radiance transmittance from below to above the surface, the irradiance transmittance from above to below the surface and the ratio of the upwelling radiance to the upwelling irradiance. An approximate expression for the relationship between [math]r_{rs}[/math] and [math]R_{rs}[/math] for nadir view angles is[32]

[math]R_{rs} \approx \Large\frac{0.5 r_{rs}}{1 - 1.5 r_{rs}}\normalsize . \qquad (A5)[/math]

If the attenuation coefficients [math]K_D, \, K_U^C, K_U^B[/math] and the seabed reflection coefficient [math]R_B[/math] are known for a specific wavelength [math]\lambda[/math], then the water depth [math]h[/math] can be derived by comparing the formulas (A4, A5) with the reflectance [math]R_{sat}[/math] recorded by the satellite sensor for the corresponding wavelength band.

Using empirical expressions for [math]K_D, \, K_U^C, \, K_U^B, \, R_B[/math], Lee et al. (1999)[12] determined the bathymetry of Florida Bay from remote sensing reflectance data with an accuracy better than 10% for these shallow waters with a uniform, sand-type bottom.

Appendix B: Semi-Empirical Model

The first model is the one proposed by Lyzenga et al. (2006)[13], which assumes a linear relationship between the log-transformed wavelength bands and depth [math]h[/math],

[math]h = h_0 + \sum_{j=1}^N h_j \, X_j , \quad X_j = \ln \big( R_{rs}(\lambda_j) - R_{\infty} (\lambda_j) \big) , \qquad (B1)[/math]

where [math]R_{\infty} (\lambda_j)[/math] is the deep water reflectance, [math]N[/math] is the number of bands considered and [math]h_0, h_1, …, h_N[/math] are coefficients that can be estimated through linear (multiple) regression from known depth values [math]\hat{h}[/math] in a number of points of the coastal area where the bathymetry is to be determined.

The second model by Stumpf et al. (2003)[14] proposed an empirical linear relationship between the water depth [math]h[/math] and the ratio of the log-transformed green or red band ([math]\lambda_1[/math]) to the log-transformed blue band ([math]\lambda_2[/math]),

[math]h = m_0 + m_1 \Large\frac{\ln(1000 \, R_{rs}(\lambda_1))}{\ln(1000 \, R_{rs}(\lambda_2))}\normalsize . \qquad (B2)[/math]

The values of the parameters [math]m_0, \, m_1[/math] have to be adjusted by linear regression to predetermined depths in a number of points for estimating the bathymetry in other points of the coastal area[33].

When bathymetric data are unavailable, the parameters [math]m_0, \, m_1[/math] can be calibrated if nearshore wave heights have been measured in the breaking zone. At breaker locations - identifiable as white foam in satellite imagery[34] - the water depth can be estimated from the observed wave height if the breaker index is known. These depth estimates can then be used to determine [math]m_0, \, m_1[/math] through linear regression. The main source of uncertainty in this approach stems from assumptions about the relationship between water depth and the height of breaking waves.[35]

Appendix C: Analysis of wave characteristics from satellite images

According to linear wave theory, the water depth [math]h[/math] can be computed from the wave dispersion relation,

[math]h = \Large\frac{\lambda}{2 \pi }\normalsize \, \tanh^{-1} \Big( \Large\frac{2 \pi c^2}{g \lambda}\normalsize \Big), \qquad (C1)[/math]

if the wavelength [math]\lambda[/math] and the wave celerity [math]c[/math] are known. However, in deep water, [math]2 \pi h / \lambda \gt \gt 1[/math], which implies that the wave celerity does not depend on the depth and consequently the water depth cannot be computed from Eq. (C1).

In shallow water, [math]h \lt \lt \lambda / 2 \pi[/math], the relationship (C1) between water depth and wave celerity becomes [math]h \approx c^2 / g[/math]. The validity of this expression is questionable for the highly distorted bore-type waves on the upper shoreface. Applying cnoidal wave theory would give [math]c^2 \approx g (h + H)[/math], where [math]H[/math] is the wave height[36]. A more accurate depth estimate can be obtained when using a modified dispersion relation based on Boussinesq theory[37][38], see Nonlinear wave dispersion relations.

Wavelength and wave celerity can be extracted from the pixel intensity pattern [math]I(x,y)[/math], where [math]x, y[/math] are the spatial coordinates of the remote sensing image. As the pixel intensity pattern may contain a great amount of noise, a common technique to enhance linear patterns is the so-called Radon Transform (RT) [27]. Even if wave fronts are not obviously apparent, they normally will appear through the Radon Transform. Linear features coinciding with wave fronts are represented by lines [math]r=x \cos \theta + y \sin \theta[/math], where [math]\theta[/math] is the wave incidence angle and [math]r[/math] the distance of the wave front to [math]x=0, \, y=0[/math] along the wave ray. The Radon Transform is then given by the integral over the image area [math]A[/math] along possible wave fronts (depending on [math]\theta[/math]),

[math]R_I (\theta, r) = \int \int_A \, I(x,y) \, \delta(r-x \cos \theta -y \sin \theta) \, dx dy , \qquad (C2)[/math]

where [math]\delta[/math] is the Dirac distribution ([math]\delta (x) = 0[/math] for [math]x \ne 0 , \; \int_{x\lt y}^{x\gt y} f(x) \delta (x-y) dx = f(y)[/math]). Wavefronts correspond to values of [math]\theta, r[/math] where [math]R_I[/math] is maximum.

The pixel intensity pattern is discretized ([math]r \rightarrow r_0, r_1, …, r_N[/math]) by assuming periodicity ([math]R_I(\theta, r_0) = R_I(\theta, r_N)[/math]) for successive wave fronts. The discrete complex Fourier transform of [math] R_I (\theta, r)[/math], [math]\; \tilde{ R_I }(\theta, k) = \sum_{n=0}^{N-1} R_I (\theta,r_n) \exp(-i 2 \pi k n/N), \; [/math] gives the intensity and the phase of the pixel pattern in wavenumber space [math]k = 2 \pi / \lambda[/math]. From the phase difference [math]\Delta \Phi[/math] between two successive images ([math]\Delta t[/math]) the wave celerity [math]c[/math] can be derived:

[math]\Phi = \tan^{-1} \Big( \Large\frac{\Im \tilde{ R_I }(\theta, k)}{\Re \tilde{ R_I }(\theta, k)}\normalsize \Big) , \quad c = \Large\frac{\lambda}{2 \pi}\frac{\Delta \Phi}{\Delta t}\normalsize . \qquad (C3)[/math]


Other techniques can also be used for analyzing the remote sensing images, for instance wavelet analysis [26].



Related articles

Light fields and optics in coastal waters
Optical remote sensing
HyMap: Hyperspectral seafloor mapping and direct bathymetry calculation in littoral zones
Bathymetry from remote sensing wave propagation
Use of Lidar for coastal habitat mapping
Data processing and output of Lidar
Use of X-band and HF radar in marine hydrography
Tidal flats from space
Optical measurements in coastal waters
Instruments for bed level detection


References

  1. ↑ IHO, 2024. Guidance to Satellite-Derived Bathymetry. IHO Publication B-13, Edition 1.0.0, International Hydrographic Organization, Monaco
  2. ↑ Gege, P. 2012. Analytic model for the direct and diffuse components of downwelling spectral irradiance in water. Applied Optics 51: 1407-1419
  3. ↑ Ruddick, K.G., Voss, K., Boss, E., Castagna, A., Frouin, R., Gilerson, A., Hieronymi, M., Johnson, B.C., Kuusk, J., Lee, Z., Ondrusek, M., Vabson, V. and Vendt, R. 2019. A Review of Protocols for Fiducial Reference Measurements of Downwelling Irradiance for the Validation of Satellite Remote Sensing Data over Water. Remote Sens. 11, 1742
  4. ↑ Vos, K., Splinter, K. D., Harley, M. D., Simmons, J. A. and Turner, I. L. 2019. CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. Environ. Model Softw. 122, 104528
  5. ↑ Almeida, L.P., Efraim de Oliveira, I., Lyra, R., Scaranto Dazzi, R.L., Martins, V.G. and Henrique da Fontoura Klein, A. 2021. Coastal Analyst System from Space Imagery Engine (CASSIE): Shoreline management module. Environ. Model. Softw. 140, 105033
  6. ↑ Sánchez-García, E., Palomar-Vázquez, J.M., Pardo-Pascual, J.E., Almonacid-Caballer, J., Cabezas-Rabadán, C. and Gómez-Pujol L. 2020. An efficient protocol for accurate and massive shoreline definition from mid-resolution satellite imagery. Coastal Eng. 160, 103732
  7. ↑ Mao, Y., Harris, D. L., Xie, Z. and Phinn, S. 2021. Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 181: 385–399
  8. ↑ Bergsma, E.W.J., Klotz, A.N., Artigues, S., Graffin, M., Prenowitz, A., Devit, J.-M. and Almar, R. 2024. Shoreliner: A Sub-Pixel Coastal Waterline Extraction Pipeline for Multi-Spectral Satellite Optical Imagery. Remote Sens. 16, 2795
  9. ↑ Vos, K., Splinter, K.D., Palomar-Vazquez, J., Pardo-Pascual, J.E., Almonacid-Caballer, J., Cabezas-Rabadan, C., Kras, E.C., Luijendijk, A.P., Calkoen, F., Almeida, L.P., Pais, D., Klein, A. H. F., Mao, Y., Harris, D., Castelle, B., Buscombe, D. and Vitousek, S. 2023. Benchmarking satellite-derived shoreline mapping algorithms. Commun. Earth Environ. 4, 345
  10. ↑ Mishra, D. R., Narumalani, S., Rundquist, D. and Lawson, M. 2005. Characterizing the Vertical Diffuse Attenuation Coefficient for Downwelling Irradiance in Coastal Waters: Implications for Water Penetration by High Resolution Satellite Data. ISPRS Journal of Photogrammetry and Remote Sensing 60: 48–64
  11. ↑ 11.0 11.1 Cesbron, G., Melet, A., Almar, R., Lifermann, A., Tullot, D. and Crosnier, L. 2021. Pan-European Satellite-Derived Coastal Bathymetry—Review, User Needs and Future Services. Frontiers in Marine Science 8, 740830
  12. ↑ 12.0 12.1 12.2 Lee, Z., Carder, K. L., Mobley, C. D., Steward, R. G. and Patch, J. S. 1999. Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Appl. Opt. 38: 3831–3843
  13. ↑ 13.0 13.1 Lyzenga, D.R., Malinas, N.P. and Tanis, F.J. 2006. Multispectral bathymetry using a simple physically based algorithm. IEEE Trans. Geosci. Remote Sens. 44: 2251–2259
  14. ↑ 14.0 14.1 Stumpf, R.P., Holderied, K. and Sinclair, M. 2003. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol. Oceanogr. 48: 547–556
  15. ↑ Liu, Y., Tang , D., Deng, R., Cao, B., Chen, Q., Zhang, R., Qin, Y. and Zhang, S. 2020. An adaptive blended algorithm approach for deriving bathymetry from multispectral imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 14: 801–817
  16. ↑ Xie, C., Chen, P., Zhang, Z. and Pan, D. 2023. Satellite-derived bathymetry combined with Sentinel-2 and ICESat-2 datasets using machine learning. Front. Earth Sci. 11, 1111817
  17. ↑ Lumban-Gaol, Y. A., Ohori, K. A. and Peters, R. Y. 2021. Satellite-derived bathymetry using convolutional neural networks and multispectral sentinel-2 images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 43(B3-2021): 201-207
  18. ↑ Mudiyanselage, S.S.J.D., Abd-Elrahman, A., Wilkinson, B. and Lecours, V. 2022. Satellite-derived bathymetry using machine learning and optimal Sentinel-2 imagery in South-West Florida coastal waters. GIScience & Remote Sensing 59: 1143-1158
  19. ↑ Misra, A., Vojinovic, Z., Ramakrishnan, B., Luijendijk, A. and Ranasinghe, R. 2018. Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery. International journal of remote sensing 39: 4431-4450
  20. ↑ Sanchez-Carnero, N., Ojeda-Zujar, J., Rodriguez-Perez, D. and Marquez-Perez, J. 2014. Assessment of different models for bathymetry calculation using SPOT multispectral images in a high-turbidity area: The mouth of the Guadiana Estuary. International Journal of Remote Sensing 35: 493–514
  21. ↑ Zhang, X., Chen, Y., Le, Y., Zhang, D., Yan, Q., Dong, Y., Han, W. and Wang, L. 2022. Nearshore Bathymetry Based on ICESat-2 and Multispectral Images: Comparison Between Sentinel-2, Landsat-8, and Testing Gaofen-2. In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 15: 2449-2462
  22. ↑ Almar, R., Bonneton, P., Senechal, N. and Roelvink, D. 2009. Wave celerity from video imaging: A new method. In Proceedings of the 31st international conference coastal engineering. Vol. 1 :661–673
  23. ↑ Holman, R. A., Plant, N. and Holland, T. 2013. Cbathy: A robust algorithm for estimating nearshore bathymetry. Journal of Geophysical Research: Oceans 118: 2595–2609
  24. ↑ Bergsma, E. W. J., Almar, R., de Almeida, L. P. M. and Sall, M. 2019. On the operational use of uavs for video-derived bathymetry. Coastal Engineering 152, 103527
  25. ↑ Lange, A.M.Z., Fiedler, J.W., Merrifield, M.A. and Guza, R.T. 2023. UAV video-based estimates of nearshore bathymetry. Coastal Engineering 185, 104375
  26. ↑ 26.0 26.1 Poupardin, A., Idier, D., de Michele, M. and Raucoules, D. 2016. Water depth inversion from a single spot-5 dataset. IEEE Trans. Geosci. Remote Sens. 119 : 2329–2342
  27. ↑ 27.0 27.1 Bergsma, E.W.J., Almar, R. and Maisongrande, P. 2019. Radon-augmented sentinel-2 satellite imagery to derive wave-patterns and regional bathymetry. Remote Sens. 11, 16
  28. ↑ Bergsma, E.W.J., Almar, F., Rolland, A., Binet, R., Brodie, K.L. and Bak, A.S. 2021. Coastal morphology from space: A showcase of monitoring the topography-bathymetry continuum. Remote Sensing of Environment 261, 112469
  29. ↑ Almar, R., Bergsma, E.W.J., Thoumyre, G., Lemai-Chenevier, S., Loyer, S., Artigues, S., Salles, G., Garlan, T., Lifermann, A. 2024. Satellite-derived bathymetry from correlation of Sentinel-2 spectral bands to derive wave kinematics: Qualification of Sentinel-2 S2Shores estimates with hydrographic standards. Coastal Engineering 189, 104458
  30. ↑ Dickens, K. and Armstrong, A. 2019. Application of Machine Learning in Satellite Derived Bathymetry and Coastline Detection. SMU Data Science Review 2, Article 4
  31. ↑ Albert, A. and Mobley, C.D. 2003. An analytical model for subsurface irradiance and remote sensing reflectance in deep and shallow case-2 waters. Opt. Express 11: 2873–2890
  32. ↑ Lee, Z., Carder, K. L., Mobley, C. D., Steward, R. G. and Patch, J. S. 1998. Hyperspectral remote sensing for shallow waters: 1. A semi-analytical model. Appl. Opt. 37: 6329–6338
  33. ↑ Caballero, I. and Stumpf, R.P. 2020. Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity: Contribution of Sentinel-2A/B Satellites Mission. Remote Sens. 12, 451
  34. ↑ Frugier, S., Almar, R., Bergsma, E. and Granjou, A. 2025. SBI: A sandbar extraction spectral index for multi-spectral satellite optical imagery. Coast. Eng. 200, 104752
  35. ↑ Frugier, S., Almar, R., Bergsma, E.W.J. and Bak, S.A. 2026. Standalone color-based bathymetry over 10 years at Duck (NC, USA) from optical satellite imagery and wave breaking analysis. Coastal Engineering 203, 104855
  36. ↑ Thornton, E.B. and Guza, R.T. 1982. Energy saturation and phase speeds measured on a natural beach. J. Geophys. Res. 87, 9499
  37. ↑ Herbers, T. H. C., Elgar, S., Sarap, N. A. and Guza, R. T. 2002. Nonlinear dispersion of surface gravity waves in shallow water. Journal of Physical Oceanography 32: 1181–1193
  38. ↑ Martins, K., Bonneton, P., de Viron, O., Turner, I.L., Harley, M.D. and Splinter, K. 2022. New Perspectives for Nonlinear Depth-Inversion of the Nearshore Using Boussinesq Theory. Geophysical Research Letters 50, e2022GL100498


Rafaël Almar provided comments on a draft version of this article.


The main author of this article is Job Dronkers
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Citation: Job Dronkers (2026): Satellite-derived shoreline and nearshore bathymetry. Available from http://www.coastalwiki.org/wiki/Satellite-derived_shoreline_and_nearshore_bathymetry [accessed on 2-09-2026]
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