Estimating the Underwater Light Field from Remote Sensing of Ocean Color

Abstract

We present a new approach that incorporates two models to estimate the underwater light field from remote sensing of ocean color. The first employs a sense of analytical. semi-analytical, and empirical algorithms to retrieve the spectrum of inherent optical properties (IOPs), including the absorption and backscatter coefficients, from the spectrum of remote sensing reflectance. The second model computes the profile of photosynthetically available radiation EO, PAR(z) for a vertically homogeneous water column using the information of the retrieved IOPs and the ambient optical environment. This computation is based on an improved look-up table technology that possesses high accuracy, comparable with the full solution of the radiative transfer equations, and meets the computational requirement of remote sensing application. This new approach was validated by in situ measurements and an extensive model-to-model comparison with a wide range of IOPs. We successfully mapped the compensation depth by applying this new approach to process the SeaWiFS imagery. This research suggests that E0, PAR(z) can be obtained routinely from ocean-color data and may have significant implications for the estimation of global heat and carbon budget.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA470241

Entities

People

  • Cheng-chien Liu
  • Eurico J. D'sa
  • James E. Ivey
  • Kendall L. Carder
  • Richard L. Miller
  • Zhongping Lee

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Ground and Sea Platforms
  • Space

DTIC Thesaurus Topics

  • Absorption
  • Absorption Coefficients
  • Accuracy
  • Algorithms
  • Backscattering
  • Climate Change
  • Environment
  • Equations
  • Measurement
  • Oceanography
  • Optical Properties
  • Radiation
  • Radiative Transfer
  • Regression Analysis
  • Remote Sensing
  • Scattering
  • Sorption

Readers

  • Atmospheric Remote Sensing.
  • Computational Modeling and Simulation