1106 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 55, NO. 2, FEBRUARY 2017 Accuracy of Nearshore Bathymetry Inverted From X-Band Radar and Optical Video Data Jantien Rutten, Steven M. de Jong, and Gerben Ruessink Abstract— Shore-based remote sensing platforms are increasingly used to frequently (∼daily) obtain bathymetric information of large (∼km 2 ) nearshore regions over many years. With recorded wave frequency  and wavenumber k (and hence wave phase speed c = / k), bed elevation z b can be derived using a model that relates  and k to water depth. However, the accuracy of z b as a function of the sensor and the method of - k retrieval is not well known, especially not under low-period waves. Here, we assess the accuracy of z b , based on two sensors with their own method of phase speed retrieval, in a dynamic, kilometer-scale environment (Sand Engine, The Netherlands). Bias in z b is systematic. A fast Fourier transform (FFT) method on X-band radar imagery produced z b too shallow by 1.0 m for -15 m ≤ z b ≤-9 m, and too deep by 2.3 m for z b ≥-6 m. A cross-spectral method on optical video imagery produced z b too shallow by 0.59 m for -10 m ≤ z b ≤-5 m, and too deep by 0.92 m for z b ≥-1 m. Intermediate depths had negligible bias, -0.02 m for the radar-FFT approach and -0.01 m for the video- CS approach. The collapse of the FFT method in shallow water may be explained by the inhomogeneity of the wave field in the 960 m × 960 m analysis windows. A shoreward limit of the FFT method is proposed that depends on z b in the analysis windows. Index Terms— Optical imaging, radar imaging, remote sensing, sea coast, sea floor, signal processing. I. I NTRODUCTION T HE morphology of the nearshore zone, defined as the region between the shoreline and roughly 10 m water depth, changes on a variety of spatial and tempo- ral scales [1]. For example, kilometer-wide shoreline undu- lations can migrate hundreds of meters in several years, whereas decimeter-scale wave ripples form and move within minutes [2]. The scales involved are site-specific due to environmental boundary conditions such as geology and wave climate. Numerous measurements have been taken to study morphology and its change, for practical applications in coastal zone management, but also purely out of scientific interest in the dynamics of the nearshore zone. Morphological monitoring is becoming increasingly important in the perspective of sea Manuscript received November 13, 2015; revised April 5, 2016 and August 9, 2016; accepted October 12, 2016. Date of publication November 8, 2016; date of current version December 29, 2016. The work of J. Rutten and G. Ruessink was supported in part by the Dutch Technology Foundation STW of the Dutch Organisation for Scientific Research (NWO) and in part by the Ministry of Economic Affairs under Contract 12686 (Nature Coast: S1 Coastal Safety). The authors are with the Department of Physical Geography, Faculty of Geosciences, Utrecht University, 3508 TC Utrecht, The Netherlands (e-mail: j.rutten@uu.nl). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2016.2619481 level rise, with potentially stronger erosion and higher flooding risks [3]. Traditionally, in situ field data sets are used to study morphology. However, these measurements, gathered with boats [4], jet-skis [5], [6], and amphibious vehicles [7], [8], are time-consuming and costly for spatially extensive areas, especially when high spatial and temporal resolutions are required to follow morphological change at the scales of interest. Also, in situ surveys are limited to low-energetic conditions, implying that the role of individual high-energetic storms cannot be assessed adequately. Therefore, shore-based remote sensing platforms are increasingly used, allowing large (∼km 2 ) nearshore regions to be monitored for a long- term (years) and with short intervals (hourly to daily). Microwave X-band radar and optical Argus video cameras [9] are examples of common shore-based platforms in nearshore research. An X-band radar system transmits pulses within the X-band (wavelengths of ∼3 cm) from an antenna rotating around a vertical axis. A wavy pattern, known as sea clutter, can be recognized in the resulting circular recordings and serves as proxy for the pattern of wind-generated gravity waves. Sea clutter arises by the interaction of the emitted radar pulse with capillary waves on the sea surface and the subsequent accumulation of capillary waves on the wind-wave fronts [for details on the imaging mechanism (see [10], [11])]. X-band radar systems have typically an imaging range of a few kilometers, a temporal resolution of 1–3 s, and a spatial resolu- tion of 5–10 m. The second example, the Argus video-system, was developed at the Coastal Imaging Laboratory of Oregon State University in the 1990s [12]. It consists of multiple optical cameras mounted together on the top of a high building or tower, looking downward in different directions along the beach. A host computer stores the collected imagery and sends the data to an archive. In contrast to shore-based X-band sensors, optical sensors are passive, using solar visible radia- tion, which limits their data availability to daylight conditions. Ocean waves can be recognized in optical imagery from varia- tions in reflection due to the variations in the sea surface slope or from differences in intensity depending on whether waves break or not. Optical systems have typically a smaller range of about 1 km, but a higher sampling frequency (0.1–1 Hz) and a denser sampling grid of O(1 m) than X-band systems. Morphological information has been derived from radar and video imagery as morphometric parameters, e.g., sandbar posi- tion [13], [14], and as bed elevation [15]–[17]. Morphometric parameters are well suited to describe the behavioral dynamics of the nearshore zone [18]–[20] and have the advantage that they are easy to extract from imagery, although they provide no actual bed elevation. In contrast, the derivation of bed ele- 0196-2892 © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. 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