Bathymetry from remote sensing wave propagation

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Surface topographic patterns

Water motion produces undulation patterns at the water surface that contain information about the seafloor bathymetry. This offers the possibility to determine the bathymetry through observation of specific features of the water surface topography. Over the past fifty years, various techniques have been developed that provide information about patterns at the water surface. These techniques use remote sensing and require much less measurement effort than traditional measurements carried out with ships. On the other hand, unlike direct ship-based measurements of water depth, remote sensing data provides indirect information that can only be interpreted using advanced analysis techniques. In addition, bathymetry determination based on wave remote sensing is inherently limited by the wavelengths and time in which propagating waves respond to a spatially varying bottom[1]. The most accurate results are obtained for a regular pattern of substantial waves.

This article discusses some of the processes that determine the relationship between bathymetry and surface undulation patterns. The focus is on the nearshore zone and surface patterns produced by propagating waves.

Several remote sensing techniques can be used to determine wave patterns at the water surface:

In this article we will not discuss the retrieval of surface wave patterns from remote sensing images; this is dealt with in the article Satellite-derived shoreline and nearshore bathymetry. Without entering into details, it is assumed that wavelength, wave period and wave height can be determined from the remote sensing images in every point of the considered nearshore area.


Depth inversion algorithms

The wave dispersion relation is the key to determining bathymetry from the wavelength [math]\lambda[/math], the wave period [math]T = 2 \pi / \omega[/math] and the wave height [math]H[/math]. According to linear wave theory, the wave dispersion relation can be written as

[math]c = u + \Large\frac{gT}{2 \pi }\normalsize \, \tanh kh , \qquad (1)[/math]

where [math]c = \lambda / T = \omega / k[/math] is the wave celerity, [math]k = 2 \pi / \lambda[/math] is the wave number (the length of the wave number vector [math]\vec{k}[/math]), [math]\omega[/math] is the radial wave frequency, [math]h[/math] is the still water depth and [math]u[/math] is the surface current velocity in the wave propagation direction. In the following we assume that the surface current velocity is nil or very small, [math]u \lt \lt c[/math]. If this condition is not satisfied but the surface current velocity is known, it can be subtracted from the measured wave celerity [math]c[/math]. In the converse case where the bathymetry is known and the surface current velocity is substantial but unknown, the component [math]u[/math] of the surface current velocity in the direction of the wave propagation velocity can be determined by observation of the wave propagation velocity [math]c[/math] [11][12].

The local water depth [math]h[/math] can be found by inversion of this formula:

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

In shallow water, [math] kh \lt \lt 1[/math], the relationship (1) between water depth and wave celerity becomes [math]c^2 \approx gh[/math]. Knowledge of the wave celerity is sufficient to determine the water depth. In deep water, [math]k h \gt 1[/math], and [math]\tanh kh \approx 1[/math]. According to Eq. (1), the wave celerity in deep water does not depend on the depth, meaning that the depth cannot be determined from inversion of the dispersion relation.

Another limitation of Eq. (1) arises from the assumptions of linear wave theory: (i) irrotational wave flow, (ii) wave height [math]H[/math] much smaller than the water depth, [math]H/h \ll 1[/math], (iii) product of wave height and wave number much smaller than one, [math]k \, H \ll 1[/math], and (iv) nearly constant water depth over a wavelength. These assumptions break down in the nearshore zone, where waves become skewed and asymmetric. In such cases, approximate analytical expressions for wave celerity can be obtained from higher-order wave theories, as described in Nonlinear wave dispersion relations.


Related articles

Use of X-band and HF radar in marine hydrography
Satellite-derived shoreline and nearshore bathymetry
Bathymetry German Bight from X-band radar
Waves and currents by X-band radar
Statistical description of wave parameters


References

  1. ↑ Almar, R., Bergsma, E. W., Thoumyre, G., Baba, M. W., Cesbron, G., Daly, C., Garlan, T. and Lifermann, A. 2021. Global satellite-based coastal bathymetry from waves. Remote Sensing 13, 4628
  2. ↑ 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
  3. ↑ 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
  4. ↑ Stockdon, H.F. and Holman, R.A. 2000. Estimation of wave phase speed and nearshore bathymetry from video imagery. Journal of Geophysical Research 105(C9): 22015–22033
  5. ↑ Aarninkhof, S.G.J., Ruessink, B.G. and Roelvink, J.A. 2005. Nearshore subtidal bathymetry from time-exposure video images. J. Geophys. Res. 110, C06011
  6. ↑ Fiedler, J.W., Kim, L., Grenzeback, R. L., Young, A.P. and Merrifield, M.A. 2021. Enhanced surf zone and wave runup observations with hovering drone-mounted lidar. Journal of Atmospheric and Oceanic Technology 38(11): 1967–1978
  7. ↑ Bell, P. S. 1999. Shallow water bathymetry derived from an analysis of X-band marine radar images. Coastal Engineering 37: 513-527
  8. ↑ Greidanus, H. 1997. The use of radar for bathymetry in shallow seas. The Hydrographic Journal 83: 13–18
  9. ↑ Gawehn, M., van Dongeren, A., de Vries, S., Swinkels, C., Hoekstra, R., Aarninkhof, S. and Friedman, L. 2020. The application of a radar-based depth inversion method to monitor near-shore nourishments on an open sandy coast and an ebb-tidal delta. Coastal Engineering 159, 103716
  10. ↑ Almar, R., Bergsma, E.W., Thoumyre, G., Baba, M.W., Cesbron, G., Daly, C., Garlan, T. and Lifermann, A. 2021. Global satellite-based coastal bathymetry from waves. Remote Sensing 13, 4628
  11. ↑ Streßer, M., Carrasco, R. and Horstmann, J. 2017. Video-based estimation of surface currents using a low-cost quadcopter. Geosci. Rem. Sens. Lett. IEEE 14: 2027–2031
  12. ↑ Fairley, I., King, N., McIlvenny, J., Lewis, M., Neill, S., Williamson, B.J., Masters, I. and Reeve, D.E. 2024. Intercomparison of surface velocimetry techniques for drone-based marine current characterization. Estuarine, Coastal and Shelf Science 299, 108682


The main author of this article is Job Dronkers
Please note that others may also have edited the contents of this article.
Citation: Job Dronkers (2026): Bathymetry from remote sensing wave propagation. Available from http://www.coastalwiki.org/wiki/Bathymetry_from_remote_sensing_wave_propagation [accessed on 10-02-2026]
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