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Proceedings Paper

Polarization discrimination technique to maximize lidar signal to noise ratio
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Paper Abstract

Range and sensitivities of lidar measurements are limited in daytime use by sky background noise (BGN). We report on a polarization technique to reduce the detected sky BGN by making use of the fact that daylight sky noise is polarized by atmospheric scattering to a greater or lesser degree, depending on the solar zenith angle. With suitable rotation of a beam polarizer at the entrance of the detector, the sky noise reaching the detector is minimized. At the same time, the polarization of the outgoing lidar signal is rotated to align the polarizations of both the outgoing and elastically scattered return signals with that of the polarizer in front of the detector. This ensures unhindered passage of lidar returns to the detector while at the same time minimizing detected BGN. Daylight measurements with a vertically oriented elastic backscatter lidar at 532 nanometers show that as much as tenfold improvements in signal to noise ratio (SNR), and consequent improvements in effective lidar range, can be obtained for high solar zenith angles (less for angles closer to the zenith) in typical urban atmospheric conditions. The variations in SNR as a function of different solar angles were found to be consistent with theoretical estimates of the variation of the polarization factor for incoming vertical skylight for an urban atmosphere based on adjacent CIMEL measurements of the atmospheric optical depth over the same period.

Paper Details

Date Published: 18 August 2005
PDF: 9 pages
Proc. SPIE 5888, Polarization Science and Remote Sensing II, 58880C (18 August 2005); doi: 10.1117/12.615205
Show Author Affiliations
Yasser Y. Hassebo, City College, CUNY (United States)
B. Gross, City College, CUNY (United States)
F. Moshary, City College, CUNY (United States)
Y. Zhao, City College, CUNY (United States)
S. Ahmed, City College, CUNY (United States)

Published in SPIE Proceedings Vol. 5888:
Polarization Science and Remote Sensing II
Joseph A. Shaw; J. Scott Tyo, Editor(s)

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