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Ocean wavenumber estimation from wave-resolving time series imagery

IEEE Transactions on Geoscience and Remote Sensing

By:
, , and
DOI: 10.1109/TGRS.2008.919821

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Abstract

We review several approaches that have been used to estimate ocean surface gravity wavenumbers from wave-resolving remotely sensed image sequences. Two fundamentally different approaches that utilize these data exist. A power spectral density approach identifies wavenumbers where image intensity variance is maximized. Alternatively, a cross-spectral correlation approach identifies wavenumbers where intensity coherence is maximized. We develop a solution to the latter approach based on a tomographic analysis that utilizes a nonlinear inverse method. The solution is tolerant to noise and other forms of sampling deficiency and can be applied to arbitrary sampling patterns, as well as to full-frame imagery. The solution includes error predictions that can be used for data retrieval quality control and for evaluating sample designs. A quantitative analysis of the intrinsic resolution of the method indicates that the cross-spectral correlation fitting improves resolution by a factor of about ten times as compared to the power spectral density fitting approach. The resolution analysis also provides a rule of thumb for nearshore bathymetry retrievals-short-scale cross-shore patterns may be resolved if they are about ten times longer than the average water depth over the pattern. This guidance can be applied to sample design to constrain both the sensor array (image resolution) and the analysis array (tomographic resolution). ?? 2008 IEEE.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Ocean wavenumber estimation from wave-resolving time series imagery
Series title:
IEEE Transactions on Geoscience and Remote Sensing
DOI:
10.1109/TGRS.2008.919821
Volume
46
Issue:
9
Year Published:
2008
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
IEEE Transactions on Geoscience and Remote Sensing
First page:
2644
Last page:
2658