DocumentCode
894035
Title
Geometrical optics prediction of surface scattering statistics
Author
McDaniel, Suzanne T.
Author_Institution
Appl. Res. Lab., Pennsylvania State Univ., University Park, PA, USA
Volume
42
Issue
2
fYear
2004
Firstpage
361
Lastpage
366
Abstract
A composite-roughness formulation of the geometrical optics approximation is applied to study the statistics of near-nadir electromagnetic scattering from the sea surface. For scattering from Gaussian random surfaces, the scattering cross section is dependent only on the probability density of surface slopes. The statistical distribution of the scattered intensity depends on both the slope probability density function and <|Ω|> $the mean absolute value of the surface curvature. The curvature is of interest because it provides a measure of capillary wave spectra. Numerical results are obtained for scattering from isotropic surfaces for a fixed number N of specular scatterers and for N Poisson distributed. Obtaining viable estimates of <|Ω|>, and hence of capillary wave spectra, from backscatter data at microwave frequencies may not be practical. Optical measurements for which individual point scatterers can be identified may, however, yield estimates of the surface curvature.
Keywords
Gaussian distribution; Poisson distribution; backscatter; electromagnetic wave scattering; oceanographic techniques; radar cross-sections; remote sensing by radar; Gaussian random surfaces; Poisson distribution; backscatter data; capillary wave spectra; composite-roughness formulation; geometrical optics prediction; isotropic surfaces; mean absolute value; microwave frequencies; near-nadir electromagnetic scattering; optical measurements; point scatterers; scattered intensity; scattering cross section; sea surface; slope probability density function; specular scatterers; statistical distribution; surface curvature; surface scattering statistics; Electromagnetic scattering; Frequency estimation; Geometrical optics; Optical scattering; Optical surface waves; Probability density function; Sea measurements; Sea surface; Statistical distributions; Statistics;
fLanguage
English
Journal_Title
Geoscience and Remote Sensing, IEEE Transactions on
Publisher
ieee
ISSN
0196-2892
Type
jour
DOI
10.1109/TGRS.2003.817687
Filename
1266725
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