A spectrum matching method for estimating the inherent optical properties from remote sensing of ocean color

Abstract

Light emerging from the sea surface carries information of the water constituents. General empirical methods to derive in-water ocean color algorithms use measurements near the sea surface to relate the emerging radiative signals to the water contents. Problems with existing algorithms are frequently reported and there is no single algorithm adopted for Case 2 waters. There is a general trend in investigators moving from pure empirical methods to model-based techniques to solve the inverse problem. Among these techniques, the non-linear optimization approach (NOA) offers the highest accuracy without any dependency on the simulated or training data, but generally requires substantial computation time. Our research presents an approach to substantially decrease the computation time of the NOA by using a look-up-table (LUT) technique to correct the effects of inelastic scattering. A series of sensitivity tests was made to determine the critical factors required to accurately simulate the remote sensing reflectance. Results show that the inherent optical properties (IOPs) and inelastic scattering play a significant role, while variations of the ambient optical environment and surface wind speed are negligible. A LUT was then derived from numerous forward simulations using the Hydrolight radiative transfer model. All processes of inelastic scattering were considered and a set of three-variable (chlorophyll concentration, CDOM ratio and backscattering fraction) biooptical models, was used to yield a flexible parameterization of IOPs. This new approach was validated against in situ measurements. To examine its application to a large variety of water types, an extensive model-to-model comparison was made for a wide range of combinations of IOPs. Results show that our model provides both fast and accurate retrievals of chlorophyll concentration, CDOM ratio and backscattering fraction for an optically homogeneous water body. This new inversion approach may accelerate the use of ocean color remote sensing.