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X-band radar in marine hydrography

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This article explains how ground-based and ship-borne microwave (X-band) radar is used in hydrography as a remote sensing technique. It describes the measurement principle, the derivation of sea state parameters (wave height, period, direction and wavelength), surface currents and bathymetry, and gives examples of applications. Radar techniques operating at much longer wavelengths are described in the companion article HF radar in marine hydrography.

Introduction

Many offshore operations are critically dependent on the prevailing sea state. To enhance the safety of crew, vessels, buildings and environment, reliable sea state measurements are required. In coastal areas, sea state measurements are needed to support weather, wave climate and ship routing services. Wave data is essential for the protection of the coastal zone to estimate the eroding forces of wind, waves, and currents. Due to climate change and its impact on coastal protection, wave and current measurements are of growing importance.

Radar remote sensing in Earth observation does not only cover the global and regional survey of geophysical parameters by satellite- or airborne radars, it also includes local observations by ground-based radar techniques. Due to their continuous measurement capabilities, the information provided by ground-based instruments installed at the coast or on offshore platforms does not suffer from the episodic character of the satellite- or airborne radar observations. The same holds for marine radars on board of ships. In contrast to the satellite systems, the incident angle of ground-based radars is only a few degrees and near to zero (grazing) at far ranges.

Microwave radars, also called X-band radars, are limited by line-of-sight propagation to the horizon. Marine X-band radar can achieve a range resolution of about 5–10 m, while the azimuthal resolution depends on antenna beamwidth and distance from the radar. With a beamwidth of 1°, the azimuthal resolution is approximately 17 m at 1 km and 87 m at 5 km. By installing an X-band radar on board a ship, the observation area can be extended along the ship track to a regional scale. Compared to traditional equipment like wave rider buoys, X-band radar offers the opportunity to monitor the wave field in larger areas at relatively low costs and with relatively little supervision and maintenance. X-band radar sensors are in widespread use for ship traffic control and navigation purposes and are installed on nearly every offshore installation and larger vessel. By adding hardware and software components (Fig. 1), the nautical radar can be used as wave sensor.

Figure 1: Components of X-band radar system.

Marine microwave radar images were first used by Ijima et al. (1964[1]) and Wright (1965[2]) for imaging ocean waves. Ziemer et al. (1983[3]) showed that Fourier analysis of digitized radar images provided spectra similar to those obtained from wave buoys, from which the directional wave spectrum can be derived. A comprehensive overview on the use of radar in general and its use in geophysics is given by Skolnik (1990[4]).

A review of the algorithms for retrieving wind and wave information from X-band marine radar is given by Huang et al. (2017[5]).

Measurement principle

Radar backscatter from the sea surface

Figure 2. Bragg scattering: constructive interference of radar waves backscattered by regularly spaced surface ripples. At low grazing angles, resonance occurs when the ripple wavelength is approximately half the radar wavelength. Image from Middleditch (2006[6]).

X-band radars operate in the frequency band 8-12.5 GHz (wavelength 2.5-4 cm). Marine X-band radars receive microwave signals backscattered mainly by centimeter-scale wind-generated surface ripples. An important scattering mechanism is Bragg resonance, which occurs when the ripple wavelength is approximately half the radar wavelength at low grazing angles. Steep and breaking waves can also produce substantial radar backscatter.

The backscattered signal results from constructive interference of the electromagnetic radar wave with the sea surface ripples in the radar footprint (see Fig. 2). Because microwave signals penetrate only a very short distance into seawater, the radar primarily receives information from the water surface. Water depth and currents can nevertheless be inferred indirectly from their influence on the propagation of surface gravity waves. This information can be retrieved by analyzing the modulation of intensity and frequency (Doppler shifts) of the backscattered radar signal.

There are two different uses of Doppler information:

  • Coherent radar: The microwave Doppler shift provides information about the radial motion of the scattering surface features[7].

Under certain conditions signatures of the sea surface are visible in the near range (< 5 km) of nautical radar images. Given a wind speed of more than approximately 3 m/s, the backscatter from the rippled sea surface becomes visible in radar images. This base signal is modulated by longer ocean waves (surface gravity waves with wavelength in the order of magnitude of some 10 m to several 100 m), generating a stripe-like pattern, the so-called 'sea clutter'. Typically, sailors suppress this signal because they consider it noise. However, it contains valuable information on the actual sea state. As the basic imaging mechanisms of ocean waves in radar images are known (Alpers et al., 1981[8]; Plant and Keller, 1990[9]), it is possible to analyse the spatial and temporal development of these patterns to gain information on wave height, wavelength, wave period, surface current and water depth.

The long surface waves modulate the backscatter in several ways. The local slope of the long wave changes the angle at which the radar beam meets the rippled surface (tilt modulation). At the low grazing angles of ground-based radar, wave crests hide part of the wave troughs behind them from the radar (shadowing). In addition, the orbital motion of the long waves compresses and stretches the ripples along the wave profile (hydrodynamic modulation). The radar image therefore shows the wave pattern, but the image intensity is not a linear measure of the surface elevation. This is the reason why wavelengths, periods, directions and propagation speeds can be derived directly from radar images, whereas the wave height requires additional steps, see the section Significant wave height.

Acquisition of radar backscatter

Figure 3: Geometry microwave radar. The radar pulse sweeps along a radial line. The size of the radar resolution cell is preset by antenna focusing [math]\displaystyle{ \Delta \phi }[/math] and pulse limitation [math]\displaystyle{ \Delta r }[/math].

The radar signal intensity that is detected at the receiving antenna is called radar backscatter. The amplitude and phase of the backscatter are instantaneously and locally modulated in time and space by hydrodynamic effects. The most important influence to the radar backscatter of an ocean surface element is the mean amplitude of the surface roughness, which is instantaneously steered by the local wind impact. The radars under discussion here are operated synchronously with a precise navigation track record to compose the backscatter values into a geo-coded radar map. While a radar pulse sweeps along a radial line, the received radar backscatter is decomposed into a series of radial bins. The radar return is allocated to corresponding scattering surface elements by measuring the microwave runtime and the antenna azimuth. The focusing in azimuth depends on the length of the antenna and in range on the duration of the transmitted radar signal (see Fig. 3). Typical values for the range resolution are 5 m or 10 m and for the antenna opening angle about 1°. Whereas the return of each individual radial beam is acquired during nanoseconds, the acquisition of the total image takes some seconds or even minutes depending on the acquisition mode. The image composition under the use of a rotating antenna takes about 2 seconds and the product is a matrix in polar coordinates (range and azimuth).

X-band radar systems thus scan the ocean surface in real time with high temporal (1 – 2 s) and spatial (5 – 10 m) resolution. The azimuthal resolution depends on the antenna beamwidth and increases with distance from the radar. A water surface of several km2 can be monitored continuously. The systems can be installed on moving vessels as well as on fixed platforms or at coastal sites. For wave measurements, the radar raw signal is digitized and transferred to an analysis computer. Results can be displayed on site or transferred to remote locations via internet.

Acquisition of radar Doppler shift

Determining the Doppler frequency shift requires coherent observation of the backscattered signal over a finite time interval. The required integration time depends on the desired velocity resolution and the radar characteristics. During this interval, the radar must maintain a suitable observation geometry for the scattering surface elements. The longer the Doppler frequency information is integrated, the greater its significance. As the frequency shift is sensitive to instantaneous local wind friction, the knowledge on the wind impact is crucial. An application of the Doppler measurements is described in the section Surface currents by Radar Doppler Current Profiler.

Analysis of radar image sequences

Figure 4: Example of a radar image obtained by X-band radar system on board a vessel.

While single radar images give the intensity distribution of the backscattered radar power attained from a kind of instantaneously frozen surface, the surface dynamics can be observed by tracking features in subsequent radar measurements. A typical wave measurement by means of X-band radar is composed of a temporal sequence of sea clutter images [math]\displaystyle{ I(x,y,t) }[/math] (see Fig. 4). To derive wave information from such data sets, several analysis steps are required.

First, the image sequence is transformed into the spectral domain by means of a discrete 3D Fourier Transform. The resulting 3D image spectrum is a function of the wave vector [math]\displaystyle{ \vec{k}=(k_x,k_y) }[/math] and the angular frequency [math]\displaystyle{ \omega }[/math]. The angular frequency and the wave vector of surface gravity waves obey the dispersion relationship

[math]\displaystyle{ \omega = \vec{k}.\vec{u}+\sqrt{gk \tanh(kd)}  ,  \qquad (1) }[/math]

where [math]\displaystyle{ g }[/math]  is the acceleration due to gravity, [math]\displaystyle{ k=|\vec{k}|=2 \pi/ \lambda }[/math] is the wave number, [math]\displaystyle{ \lambda }[/math] is the wavelength, [math]\displaystyle{ d }[/math] is the water depth and [math]\displaystyle{ \vec{u} }[/math] is the current velocity relative to the radar (encounter current). In the wave vector-frequency domain, Eq. (1) describes a surface called the dispersion shell (Fig. 6). For [math]\displaystyle{ kd }[/math] greater than about 3, the factor [math]\displaystyle{ \tanh(kd) }[/math] approaches unity and the dispersion relation becomes independent of water depth. Bathymetric inversion is therefore possible only when waves are sufficiently influenced by the seabed, typically for [math]\displaystyle{ kd }[/math] of order unity or smaller.

The dispersion relation is used as a filter function to separate the wave signal in the image spectrum from background noise and patterns not related to the wave field: only the spectral energy located close to the dispersion shell is attributed to the waves. The image spectrum can then be transformed into an energy density spectrum of the ocean surface waves by correcting for distortions due to the imaging mechanism (Young et al., 1985[10], Ziemer and Rosenthal, 1987[11]). This correction is in practice made with an empirical transfer function that depends on the wave number (Nieto Borge et al., 2004[12]).

The resulting 2D ocean wave spectrum [math]\displaystyle{ E(\omega, \theta) }[/math] (see Fig. 5) is usually expressed as a function of wave frequency [math]\displaystyle{ \omega }[/math] and wave direction [math]\displaystyle{ \theta }[/math]. It gives a comprehensive description of the sea state. Integration over all directions yields the 1D wave spectrum [math]\displaystyle{ S(\omega) }[/math]. Commonly, various statistical sea state parameters are calculated from these spectra to give a compact description of the wave climate, see Statistical description of wave parameters.

The water depth and the current vector are free parameters influencing the shape of the dispersion shell (Fig. 6). Deviations of the observed spectral energy from the undisturbed dispersion shell (no current, deep water) therefore provide information on the current and, in shallow water, on the water depth. Unlike the estimation of significant wave height from radar image intensity, retrieval of current velocity and water depth from the linear wave dispersion relation does not require amplitude calibration of the radar signal. Since both current velocity and water depth influence wave dispersion, their simultaneous estimation requires a sufficiently broad range of observed wavelengths and directions. Errors may arise when the wave field provides insufficient information to distinguish their effects.

Figure 5: 2-Dimensional Frequency-Direction Spectrum obtained by X-band radar system on board a vessel.
Figure 6: Gravity wave-dispersion shell in the wave vector-frequency domain [math]\displaystyle{ k_x, k_y, \omega }[/math]. The isotropic shell (thick line) represents the dispersion as a function of the local water depth [math]\displaystyle{ d }[/math]. The distorted shell (dashed line) represents the dispersion in the presence of a non-zero onshore current [math]\displaystyle{ \vec{u_e} }[/math].

Sea state parameters

Figure 7: Comparative time series of significant wave height measured from X-band radar system at the 2/4k Ekofisk platform in the North Sea and from a wave rider buoy.
Figure 8: Comparative time series of peak wave period and peak wave direction measured from X-band radar system and wave rider buoy at the Island Helgoland in the German Bight.
Figure 9: Comparative time series of peak wave length measured from X-band radar system and wave rider buoy at the Island Helgoland in the German Bight.

Significant wave height

In contrast to in situ sensors, the wave spectra determined from radar images are not properly scaled. Thus, the total wave energy and the significant wave height cannot be computed directly from these spectral estimates. It has been shown that the significant wave height is linearly dependent on the square root of the signal-to-noise ratio (SNR), where the signal is assumed as the total energy of the wave spectrum estimation derived by the sea clutter analysis mentioned above and the noise is computed as the energy due to the speckle caused by the sea surface roughness (Alpers and Hasselmann, 1982[13]). The significant wave height [math]\displaystyle{ H_s }[/math] is determined by using a linear regression equation,

[math]\displaystyle{ H_s = A + B \sqrt{SNR} , \qquad (2) }[/math]

which relates the square root of the signal-to-noise ratio of the radar images to the significant wave height (Nieto Borge et al., 1999[14]) (see Fig. 7). Comparisons with wave-buoy measurements have shown good correlations under favorable conditions, although performance depends on radar characteristics, sea state and environmental conditions. The coefficients [math]\displaystyle{ A }[/math] and [math]\displaystyle{ B }[/math] depend on the radar and on its installation (antenna height, range of the analysis area). They have to be determined for each installation by comparison with a reference instrument, usually a wave buoy.

Methods that can estimate significant wave height without calibration against a reference wave sensor have also been developed. One class of methods uses the fraction of the image that lies in the radar shadow of wave crests: this fraction depends on the steepness of the waves, from which the significant wave height follows when the wave period is known (Gangeskar, 2014[15]). Another possibility is the use of a coherent radar, which measures the Doppler velocity of the scatterers and hence the orbital velocity of the waves, from which the wave height can be computed without reference measurements (Carrasco et al., 2017[16]). An overview of methods is given by Huang et al. (2017[5]).

Peak wave period and direction

The peak wave period and direction can be derived from the frequency-direction spectrum [math]\displaystyle{ E(\omega, \theta) }[/math] (Young et al., 1985[10]) (see Fig. 8). They are defined as the location of the energy maximum within the spectrum. To determine the peak frequency, the centroid formula can be used, applied to the frequency interval where the spectral density exceeds 80% of its maximum. This method is recommended by IAHR/AIRH because of its high stability with respect to statistical variability of the spectrum. The wave period can also be derived from the spectral moments obtained over the full range of frequencies.

Peak wave length

Wavelength and period of ocean waves are connected by the dispersion relation (Eq. 1). Thus, it is possible to express wave spectra as a function of frequency, [math]\displaystyle{ E(\omega, \theta) }[/math], or wave number, [math]\displaystyle{ E(k, \theta) }[/math]. The peak wave length can be determined from [math]\displaystyle{ E(k, \theta) }[/math] directly in the same way as the peak period (see Fig. 9).

Wave systems

X-band radar systems can distinguish different wave systems, such as locally generated wind sea and remotely generated swell, when these systems produce sufficiently distinct peaks in the frequency-direction spectrum. Wind sea and swell generally differ in their spectral characteristics and propagation directions, although their frequency ranges may overlap.

Near-surface current from wave dispersion

The mean surface current over the analysis area is obtained by minimising the distance between the location of the spectral energy in the 3D image spectrum and its theoretical position defined by the dispersion relation (1) for linear surface gravity waves (Young et al., 1985[10]; Seemann, 1997[17]; Nieto Borge et al., 1999[18]). The result is the encounter current [math]\displaystyle{ \vec{u} }[/math], the current relative to the radar. For a fixed platform, this corresponds to the ‘true’ current, whereas for a moving vessel ship speed and course are used to derive the true mean current. The determination of the current is the basic step to calculate directional wave spectra from radar images, because it fixes the position of the dispersion filter (see also Currents).

The current derived in this way is not the current at the water surface itself. Waves are influenced by the current in a surface layer with a thickness that increases with the wavelength; the result is thus a weighted average over a layer whose thickness depends on the wavelengths used in the analysis. Short waves are mainly influenced by currents close to the surface, whereas longer waves are influenced by currents extending to greater depths. When the current is determined separately for short and long waves, information on the vertical shear of the near-surface current is obtained. Accuracies of 0.04–0.07 m/s were found in a comparison with surface drifters (Lund et al., 2020[19]; Anderson et al., 2026[20] ).

Bathymetric survey and current field observation

The methods described above assume that the wave field is homogeneous over the analysis area. In shallow coastal waters this is not the case: the waves vary their propagation (phase speed and wave number) locally and thus impress the local bathymetry and the local current into the image series. Based on this relation it is possible to compose maps of the water depth and of the current field, which can be used in monitoring sand transport. Another way of obtaining information on the seabed is the extrapolation of surface features down to the seabed in the operational model by Vogelzang et al. (1997[21]).

Dispersive Surface Classificator (DiSC)

Figure 10: Bathymetries of List West (north end of the Island Sylt in the German bight) derived by DiSC for February 20 (left) and February 28 (right). The size of the grid mesh is 42 m. Each result is averaged over a full tidal cycle. Comparison of the bathymetric maps before and after the storm suggests a net sediment accumulation of approximately 50,000 m³ in the surveyed area. The reliability of this estimate depends on the accuracy of the radar-derived depths and the correction for differences in water level. Image from Flampouris 2006[22].

A special challenge in coastal protection management is provided by the management of sandy coastlines. Here humans interact with the dynamic processes of the ocean and undertake measures for stabilization of the shoreline such as beach nourishment. The supply of sand to the coastal system requires monitoring of the sand’s residence as well as monitoring processes of erosion, transport and deposition. For this it is not enough to survey physical parameters at single observation points. It requires area covering observations to identify the response of the system and the processes involved.

To retrieve spatially varying bathymetry and currents, radar image sequences are analyzed in smaller areas where the wave field can be considered approximately homogeneous. The local wave frequencies and wavenumbers are determined from the radar images and fitted to the linear wave dispersion relation. Water depth and current velocity are then estimated from their influence on wave propagation. The results from adjacent areas are combined to produce spatial maps of bathymetry and currents. Various inversion algorithms have been developed for this purpose, including the Dispersive Surface Classificator (DiSC) of Senet et al. (2008[23]). The DISC algorithm was applied to List West, an area in the German Bight, before and after a storm. For morphological monitoring, radar-derived water depths must be converted to seabed elevations relative to a common vertical datum, taking account of water-level variations during the measurements. The result (Fig 10) shows net accretion in the area of investigation. The investigation procedure is shown in Fig. 11.  Fig. 12 illustrates an example of the near-surface current field in the same area. The wind conditions are approximately 5 Beaufort and the date of the data acquisition is the 12th July of 2001. Unrealistically high current estimates close to the shore and over the shoal in the northwestern corner indicate unreliable inversion results, possibly caused by wave breaking, nonlinear wave propagation or insufficient spectral information.

Figure 11: Main analysis steps for current field and bathymetry retrieval from X-band radar images.
Figure 12: Current field at List West during a tidal cycle.

Other examples of bathymetry retrieval methods

Several other methods have been developed for retrieving nearshore bathymetry from X-band radar observations.

Chernyshov et al. (2020[24]) used X-band radar for establishing nearshore bathymetric maps of the coastal zone off the island of Sylt in the German Bight. They used traditional Fast Fourier Transform techniques for retrieving peak frequency maps and a localized 2D Continuous Wavelet Transform for retrieving the corresponding peak wavenumbers. The accuracy obtained was in the range 2.6–6.6% root-mean-square-error (RMSE) of the maximum water depth.

Honegger et al. (2019[25]) used X-band radar for obtaining high-resolution estimates of the shoreface bathymetry at two contrasting beaches (Duck, Atlantic coast and Benson beach, Pacific coast). The algorithm used for analyzing radar spectra is similar to the one previously developed for retrieving bathymetry from optical video observations. This algorithm uses frequency dependent empirical orthogonal functions (EOF) to extract coherent spatial structures at each frequency. The performance of X-band radar was found to be comparable to optical video (RMSE of 0.35-0.5 m for water depth greater than 2 m). Results were substantially improved by Kalman filtering.

The same authors used a similar method for determining the bathymetry of an ebb-tidal shoal (New River Inlet, NC) and an estuarine river mouth (Columbia River, OR/WA)[26]. Simultaneous estimation of depth and currents via inversion of the dispersion relation including Doppler shifts was required at the deep estuary mouth, where currents affect the incident wave field. In the estuary channel with locations as deep as 27 m, Kalman-filtered depths achieve 0.92 m RMSE and 0.38 m depth bias as compared to a concurrent sounding survey (95% of estimates are within 10% of those surveyed). Independent of the Doppler compensation technique, Kalman filtered depths can be used to more tightly constrain high-resolution current velocity estimates.

Gawehn et al. (2020[27]) used X-band radar for monitoring near-shore sand nourishments on an open sandy coast (21 million m3) and an ebb-tidal delta (5 million m3). Morphologies were derived from analyzing the backscatter radar spectrum obtained by applying 3D Fast Fourier Transformation (FFT). In both cases large agreement with ground truth data was found. Depth biases were around -0.9 m (1.32 m RMSE) at the open coast nourishment and fluctuated between approximately -2 – 0 m at the ebb-tidal delta nourishment (-0.7 m mean bias, 1.2 m RMSE). By averaging and debiasing the radar-derived morphologies, it was possible to accurately quantify the growth of the ebb tidal delta nourishment during its placement in 2018 with a volumetric margin error of 7%. The combined effect of a Kalman filter and an analysis based on amplitude spectra was a reduction of the overall depth bias to -0.58 m and RMSE to 0.88 m at the open coast nourishment.

Other examples are described in the articles Bathymetry German Bight from X-band radar and Bathymetry from remote sensing wave propagation.

Surface currents by Radar Doppler Current Profiler (RDCP)

Figure 13: Ship with mounted Radar Doppler Current Profiler (RDCP). In addition to the two radars, a precise navigation (PDGPS) is mounted for the geo-coding and an Acoustic Doppler Current Profiler (ADCP) for the verification.

The radar Doppler shift from the sea surface can be used to estimate near-surface currents. A coherent X-band radar measures the Doppler shift of the backscattered microwave signal, which provides the radial velocity of the scattering surface features. This velocity includes contributions from surface currents, wave orbital motions and the propagation of the short surface waves responsible for Bragg scattering. After correcting for these contributions and for radar-platform movement, near-surface current velocities can be estimated. Combining measurements from two different viewing directions enables reconstruction of the horizontal current vector.

Figure 13 shows a ship-mounted Radar Doppler Current Profiler (RDCP) consisting of two coherent X-band radars with viewing directions at 45° forward and backward relative to the ship's course. The two radars operate alternately to avoid interference. Their Doppler measurements provide radial velocities at successive range bins (typically 7.5 m). The two velocity components are combined into georeferenced maps of the near-surface current field, using precise positioning and ship-motion measurements. Corrections are applied for the effects of wind and wave motion. Acoustic Doppler Current Profiler (ADCP) measurements are used for validation and to obtain information about the vertical current structure. An example of the resulting current field in the Lister Tief is shown in Fig. 14.

Figure 14: The two maps show the current distribution within a tidal channel in the German bight (Lister Tief) obtained with RDCP. Left panel: current speed; right panel: current direction. The measurement took place shortly before slack water at low tide. The maps are overlaid on a topographic map of Schleswig-Holstein. The ebb current from the south-east part of the tidal inlet turns west and follows the shoals on the north side. The flood comes in at the seaward side of the channel and produces a large anticlockwise circulation. The stripy structure in the current field in the central part of the channel (depth between 12 and 25 m) is caused by the interaction of the current with underwater sand dunes.

Resolution, limits and accuracy

The resolution of sea state measurements of X-band radar systems is limited by the properties of the radar sensor:

  • The spatial resolution is limited by the sampling frequency of the radar signal, the antenna aperture [math]\displaystyle{ \Phi_A }[/math] and the size of the analysis areas [math]\displaystyle{ N_x \times N_y }[/math] placed within the radar image.
  • The temporal resolution is limited by the antenna rotation time and the number [math]\displaystyle{ N_t }[/math] of images used for the wave analysis.

The following limits of the technique should be kept in mind:

  • A minimum wind speed of about 3 m/s is required to generate the ripples that produce the radar backscatter.
  • For conventional intensity-based radar measurements, low waves (significant wave height below approximately 0.5 m) may produce insufficient backscatter modulation for reliable wave analysis. The threshold depends on the radar system and environmental conditions.
  • Rain roughens the sea surface and scatters the radar signal, which degrades the wave signal in the images.
  • The strength of the wave signal decreases with distance from the radar and depends on the antenna height and on the angle between the radar look direction and the wave direction; it is weakest when looking along the wave crests.
  • The spectral analysis assumes a homogeneous wave field within the analysis area. Close to the coast and over shoals, where waves refract and break, local methods such as those described in the section Bathymetric survey and current field observation are needed.
  • Bathymetric inversion is generally based on linear wave theory, which can introduce errors in shallow water where nonlinear wave propagation becomes important. Nonlinear wave dispersion relations may improve the estimates under suitable conditions, but wave breaking, strong currents and spatially varying wave fields can also cause substantial errors. Independent validation and correction of systematic biases are important, especially when radar-derived bathymetry is used to quantify morphological changes.

Figure 15 lists the standard output parameters with corresponding resolutions, ranges, and accuracies. These values indicate typical ranges. The numbers depend on the radar hardware and the total time of measurement and can therefore vary for each individual installation.

Figure 15: Standard output parameters of X-band radar system with corresponding resolutions, ranges, and accuracy.

Related articles

External links

Examples of X-band Radar Systems:

References

  1. ↑ Ijima, T., Takahashi, T. and Sasaki, H. 1964. Application of radars to wave observations. Proc. 11th Int. Conf. Coastal Engineering 30(1): 10-22
  2. ↑ Wright, W.F. 1965. Wave observations by shipboard radar. Ocean Science and Ocean Engineering 1: 506-514
  3. ↑ Ziemer, F., Rosenthal, W. and Carlson, H. 1983. Measurements of directional wave spectra by ship radar. IAPSO Symposium, General Assembly, Int. Assoc. for Phys. Sci. of the Oceans, Hamburg
  4. ↑ Skolnik, M. 1990. Radar Handbook. McGraw-Hill Publishing Company. New York
  5. ↑ 5.0 5.1 Huang, W., Liu, X. and Gill, E.W. 2017. Ocean wind and wave measurements using X-band marine radar: A comprehensive review. Remote Sensing 9, 1261
  6. ↑ Middleditch, A. 2006. Spectral Analysis in High Frequency Radar Oceanography. PhD thesis, University of Sheffield
  7. ↑ Carrasco, R., Nieto-Borge, J.C., Seemann, J. and Horstmann, J. 2025. Monitoring ocean waves using coherent X-band radar: Surface reconstruction and short-term prediction. IEEE Transactions on Geoscience and Remote Sensing 63, 5102115
  8. ↑ Alpers, W., Ross, D.B., and Rufenach, C.L. 1981. On the Detectability of Ocean Surface Waves by Real and Synthetic Aperture Radar. Journal of Geophysical Research 86: 6481-6498
  9. ↑ Plant, W.J. and Keller, W.C. 1990. Evidence of Bragg scattering in microwave Doppler spectra of sea return. J. Geophys. Res. 95(C9): 16,299-16,310
  10. ↑ 10.0 10.1 10.2 Young, I.R., Rosenthal, W. and Ziemer, F. 1985. A Three-dimensional analysis of marine radar images for the determination of ocean wave directionality and surface currents. J. Geophys. Res. 90: 1049-1059.
  11. ↑ Ziemer F. and Rosenthal, W. 1987. On the Transfer Function of a Shipborne Radar for Imaging Ocean Waves. Proc. IGARSS'87 Symp. Ann Arbor. Michigan, May 1987, pp 1559-1564.
  12. ↑ Nieto Borge, J.C., Rodriguez, G.R., Hessner, K. and Gonzalez, P.I. 2004. Inversion of marine radar images for surface wave analysis. Journal of Atmospheric and Oceanic Technology 21: 1291-1300
  13. ↑ Alpers, W. and Hasselmann, K. 1982. Spectral signal to clutter and thermal noise properties of ocean wave imaging synthetic aperture radars. Int. J. Remote Sens. 3: 423–446
  14. ↑ Nieto Borge, J.C., Reichert, K. and Dittmer, J. 1999. Use of Nautical Radar as a Wave Monitoring Instrument. Coastal Engineering 37: 331-342
  15. ↑ Gangeskar, R. 2014. An algorithm for estimation of wave height from shadowing in X-band radar sea surface images. IEEE Transactions on Geoscience and Remote Sensing 52: 3373-3381
  16. ↑ Carrasco, R., Horstmann, J. and Seemann, J. 2017. Significant wave height measured by coherent X-band radar. IEEE Transactions on Geoscience and Remote Sensing 55: 5355-5365
  17. ↑ Seemann, J. 1997. Interpretation of the Structure of the frequency-wave number spectrum of nautical radar temporal sequences of sea states (in German). Hamburg, Germany: University of Hamburg, Ph.D. thesis.
  18. ↑ Nieto Borge, J.C., Hessner, K., and Reichert, K. 1999. Estimation of the Significant Wave Height with X-Band Nautical Radars. Proceedings of the 18th OMAE Conference, St John’s, Newfoundland, Canada.
  19. ↑ Lund, B., Haus, B.K., Graber, H.C., Horstmann, J., Carrasco, R., Novelli, G., Guigand, C.M., Mehta, S., Laxague, N.J.M. and Özgökmen, T.M. 2020. Marine X-band radar currents and bathymetry: An argument for a wave number-dependent retrieval method. Journal of Geophysical Research: Oceans 125, e2019JC015618
  20. ↑ Anderson, J., Smeltzer, B.K., Soffer, R., Toledo, Y., Grigorieva, V., Dias, F. and Støle-Hentschel, S. 2026. Advances in extracting current profiles from X-band radar images with a focus on retrieving subsurface current. International Journal of Applied Earth Observation and Geoinformation 149, 105238
  21. ↑ Vogelzang, J., Wensink, G.J., Calkoen, C.J. and Van der Kooij, M.W.E. 1997. Mapping submarine sandwaves with multi-band imaging radar 2. Experimental results and model comparison. Journal of Geophysical Research 102: 1183-1192
  22. ↑ Flampouris, S. 2006. Investigation of correlations between radar deduced bathymetries due to the outer impact of a storm in the area “Salzsand”. M.Sc. Thesis; GKSS 2006/16. Geesthacht.
  23. ↑ Senet, C.M., Seemann, J., Flampouris, S. and Ziemer, F. 2008. Determination of bathymetric and current maps by the method DiSC based on the analysis of nautical X-band radar image sequences of the sea surface. IEEE Transactions on Geoscience and Remote Sensing 46(8): 2267–2279
  24. ↑ Chernyshov, P., Vrecica, T., Stresser, M., Carrasco, R. and Toledo, Y. 2020. Rapid wavelet-based bathymetry inversion method for nearshore X-band radars. Remote Sensing of Environment 240, 111688
  25. ↑ Honegger, D.A., Haller, M.C. and Holman, R.A. 2019. High-resolution bathymetry estimates via X-band marine radar: 1. beaches. Coast. Eng. 149: 39–48
  26. ↑ Honegger, D.A., Haller, M.C. and Holman, R.A. 2020. High-resolution bathymetry estimates via X-band marine radar: 2. Effects of currents at tidal inlets. Coast. Eng. 156, 103626
  27. ↑ Gawehn, M., Van Dongeren, A., De Vries, S., Swinkels, C., Hoekstra, R., Aarninkhof, S. and Friedman, J. 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

This article has been written by: Friedwart Ziemer, Ina Traenkmann, Marius Cysewski and Stylianos Flampouris

The main author of this article is Ziemer, Friedwart
Please note that others may also have edited the contents of this article.
Citation: Ziemer, Friedwart (2026): X-band radar in marine hydrography. Available from http://www.coastalwiki.org/wiki/X-band_radar_in_marine_hydrography [accessed on 14-11-2026]
The main author of this article is Traenkmann, Ina
Please note that others may also have edited the contents of this article.
Citation: Traenkmann, Ina (2026): X-band radar in marine hydrography. Available from http://www.coastalwiki.org/wiki/X-band_radar_in_marine_hydrography [accessed on 14-11-2026]


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