Journal of Oceanography, Vol. 62 (No. 3), pp. 235-248, 2006

Estimating the Underwater Light Field from Remote Sensing of Ocean Color

Cheng-Chien Liu¹*, Richard L. Miller², Kendall L. Carder³, Zhongping Lee⁴, Eurico J. D'Sa⁵ and James E. Ivey³

¹Department of Earth Sciences, National Cheng Kung University, Tainan, Taiwan 701, R.O.C.also at Disaster Prevention Research Center and Earth Dynamic System Research Center, National Cheng Kung University, Tainan, Taiwan 701, R.O.C.
²NASA, Earth Science Applications Directorate, Stennis Space Center, MS 39529, U.S.A.
³College of Marine Science, University of South Florida, St. Petersburg, FL 33701, U.S.A.
⁴Naval Research Laboratory, Code 7340, Stennis Space Center, MS 39529, U.S.A.
⁵Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, U.S.A.

(Received 15 September 2005; in revised form 12 December 2005; accepted 12 December 2005)

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 series of analytical, semi-analytical, and empirical algorithms to retrieve the spectrum of inherent optical properties (IOPs), including the absorption and the backscatter coefficients, from the spectrum of remote sensing reflectance. The second model computes the profile of photosynthetically available radiation E_0,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 equation, 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 E_0,PAR(z) can be obtained routinely from ocean-color data and may have significant implications for the estimation of global heat and carbon budget.