OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 50, Iss. 19 — Jul. 1, 2011
  • pp: 3168–3186

Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS

Ioannis Ioannou, Alexander Gilerson, Barry Gross, Fred Moshary, and Samir Ahmed  »View Author Affiliations


Applied Optics, Vol. 50, Issue 19, pp. 3168-3186 (2011)
http://dx.doi.org/10.1364/AO.50.003168


View Full Text Article

Enhanced HTML    Acrobat PDF (2481 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Retrieving the inherent optical properties of water from remote sensing multispectral reflectance measurements is difficult due to both the complex nature of the forward modeling and the inherent nonlinearity of the inverse problem. In such cases, neural network (NN) techniques have a long history in inverting complex nonlinear systems. The process we adopt utilizes two NNs in parallel. The first NN is used to relate the remote sensing reflectance at available MODIS-visible wavelengths (except the 678 nm fluorescence channel) to the absorption and backscatter coefficients at 442 nm (peak of chlorophyll absorption). The second NN separates algal and nonalgal absorption components, outputting the ratio of algal-to-nonalgal absorption. The resulting synthetically trained algorithm is tested using both the NASA Bio-Optical Marine Algorithm Data Set (NOMAD), as well as our own field datasets from the Chesapeake Bay and Long Island Sound, New York. Very good agreement is obtained, with R 2 values of 93.75%, 90.67%, and 86.43% for the total, algal, and nonalgal absorption, respectively, for the NOMAD. For our field data, which cover absorbing waters up to about 6 m 1 , R 2 is 91.87% for the total measured absorption.

© 2011 Optical Society of America

OCIS Codes
(010.4450) Atmospheric and oceanic optics : Oceanic optics
(280.0280) Remote sensing and sensors : Remote sensing and sensors
(010.4455) Atmospheric and oceanic optics : Oceanic propagation
(010.4458) Atmospheric and oceanic optics : Oceanic scattering
(010.5620) Atmospheric and oceanic optics : Radiative transfer
(010.0280) Atmospheric and oceanic optics : Remote sensing and sensors

ToC Category:
Atmospheric and Oceanic Optics

History
Original Manuscript: December 3, 2010
Revised Manuscript: February 10, 2011
Manuscript Accepted: March 19, 2011
Published: June 23, 2011

Virtual Issues
Vol. 6, Iss. 8 Virtual Journal for Biomedical Optics

Citation
Ioannis Ioannou, Alexander Gilerson, Barry Gross, Fred Moshary, and Samir Ahmed, "Neural network approach to retrieve the inherent optical properties of the ocean from observations of MODIS," Appl. Opt. 50, 3168-3186 (2011)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-50-19-3168

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Log in to access OSA Member Subscription

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited