Retrieval of slope spectrum of sea roughness by Snell’s window imagery: theory and numerical experiment (one-dimensional sea roughness)

Aleksandr A. Molkov

doi:10.1117/12.2573949

20 September 2020 Retrieval of slope spectrum of sea roughness by Snell’s window imagery: theory and numerical experiment (one-dimensional sea roughness)

Aleksandr A. Molkov

Author Affiliations +

Proceedings Volume 11529, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2020; 115290C (2020) https://doi.org/10.1117/12.2573949
Event: SPIE Remote Sensing, 2020, Online Only

Abstract

The new mathematical models of the statistical moments of the Snell's window image are proposed. These models are based on the use of a binary representation of the Snell’s window image and a Gaussian slope distribution function. It leads to express statistical image moments in terms of the error function. Using its well-known properties, it was possible to establish analytical relationships between the AI differentiated by the zenith angle with the slope variance, as well as between the twice-differentiated ACF of the image with the ACF of the surface slopes. The obtained expressions can already be used in practice to solve inverse problems by a numerical method. At the same time, the results of numerical simulation show that the Snell’s window image is an object very sensitive to changes of the roughness of the sea surface. With an increase of wind speed or in the presence of a surfactant film, the number and sizes of patterns near the Snell’s window border significantly change that manifests in the statistical moments of the image. This result indicate that the presented method can be used to study the variability of the spectra of wind waves in the field of near-surface hydrophysical processes, as well as in the presence of surface pollutants.

Conference Presentation

Citation Download Citation

Aleksandr A. Molkov "Retrieval of slope spectrum of sea roughness by Snell’s window imagery: theory and numerical experiment (one-dimensional sea roughness)", Proc. SPIE 11529, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2020, 115290C (20 September 2020); https://doi.org/10.1117/12.2573949

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