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An improved dense dark vegetation based algorithm for aerosol optical thickness retrieval from hyperspectral data
Author(s): Yaokai Liu; Yonggang Qian; Ning Wang; Lingling Ma; Caixia Gao; Shi Qiu; Chuanrong Li; Lingli Tang
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Paper Abstract

Aerosol optical thickness is a very important parameters in the atmospheric correction of the hyperspectral data. In this study, an improved dense dark vegetation (DDV) based algorithm is introduced to estimate the AOT@550nm from hyperspectral remote sensing data. A correction relationship between TOA and land surface reflectance at short wavelength near 2.13μm was introduced in order to reduce the assumption of the traditional DDV that the TOA reflectance is equal to the land surface reflectance at short wavelength near 2.13μm. Simulated hyperspectral data of Hyperion sensor were applied to the improved DDV algorithm. The retrieved AOT @550nm show a well correlation with the actual values and the correlation coefficients is larger than 0.99.

Paper Details

Date Published: 11 April 2019
PDF: 5 pages
Proc. SPIE 11028, Optical Sensors 2019, 1102812 (11 April 2019); doi: 10.1117/12.2524488
Show Author Affiliations
Yaokai Liu, Academy of Opto-Electronics (China)
Yonggang Qian, Academy of Opto-Electronics (China)
Ning Wang, Academy of Opto-Electronics (China)
Lingling Ma, Academy of Opto-Electronics (China)
Caixia Gao, Academy of Opto-Electronics (China)
Shi Qiu, Academy of Opto-Electronics (China)
Chuanrong Li, Academy of Opto-Electronics (China)
Lingli Tang, Academy of Opto-Electronics (China)

Published in SPIE Proceedings Vol. 11028:
Optical Sensors 2019
Francesco Baldini; Jiri Homola; Robert A. Lieberman, Editor(s)

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