An empirical algorithm to estimate spectral average cosine of underwater light field from remote sensing data in coastal oceanic waters.
IR@NIO: CSIR-National Institute Of Oceanography, Goa
View Archive Info| Field | Value | |
| Creator |
Talaulika, M.
Suresh, T. Desa, E.S. Inamdar, A. |
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| Date |
2014-05-06T11:32:11Z
2014-05-06T11:32:11Z 2014 |
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| Identifier |
Limnology and Oceanography: Methods, vol.12; 2014; 74–85.
yes http://drs.nio.org/drs/handle/2264/4519 |
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| Description |
The underwater average cosine is an apparent optical property of water that describes the angular distribution of radiance at a given point in water. Here, we present a simple empirical algorithm to estimate spectral underwater average cosine mu (lambda) where the wavelength lambda ranges from 400 nm to 700 nm, based only on the apparent optical property, remote sensing reflectance, R<sub>rs</sub> (lambda), and solar zenith angle. The algorithm has been developed using the measured optical parameters from the coastal waters off Goa, India, and eastern Arabian Sea and the optical parameters derived using the radiative transfer code using these measured data. The algorithm was compared with two earlier reported empirical algorithms of Haltrin (1998, 2000), and the performance of the algorithm was found to be better than these two empirical algorithms. The algorithm is based on single optical parameter; remote sensing reflectance, which can be easily measured in-situ, and is available from the ocean color satellite sensors; hence this algorithm will find applications in the ocean color remote sensing.
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| Language |
en
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| Publisher |
Association for the Sciences of Limnology and Oceanography
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| Relation |
Limnol_Oceanogr-Meth_12_74.jpg
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| Rights |
An edited version of this paper was published by American Society of Limnology and Oceanography. Copyright [2014] American Society of Limnology and Oceanography
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| Subject |
remote sensing
ocean color |
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| Title |
An empirical algorithm to estimate spectral average cosine of underwater light field from remote sensing data in coastal oceanic waters.
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| Type |
Journal Article
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