Modeling hyperspectral normalized water-leaving radiance in a dynamic coastal ecosystem

Area of Science:

• Earth and Ocean Sciences
• Remote Sensing
• Optical Oceanography

Background:

• Next-generation satellite sensors (e.g., PACE OCI, SBG) offer advanced hyperspectral measurements.
• Accurate in-situ validation data for coastal ecosystems is challenging to obtain.
• Hyperspectral water-leaving radiance is crucial for understanding coastal ecosystem dynamics.

Purpose of the Study:

• To model hyperspectral normalized water-leaving radiance ([L_W(λ)]_N) in a dynamic coastal ecosystem.
• To assess the efficacy of radiative transfer modeling for validating satellite-derived data.
• To provide an alternative or complementary method to in-water radiometric profilers.

Main Methods:

• Utilized in-situ inherent optical properties (IOPs) as inputs for the Hydrolight radiative transfer model.
• Modeled hyperspectral normalized water-leaving radiance ([L_W(λ)]_N).
• Compared modeled [L_W(λ)]_N with in-situ radiometric measurements.

Main Results:

• Achieved reduced uncertainty in modeled [L_W(λ)]_N (≤21% RMSE) compared to in-situ measurements (≤33% RMSE).
• Demonstrated the capability of radiative transfer modeling to accurately simulate [L_W(λ)]_N.
• Quantified the improvement in accuracy using the modeling approach.

Conclusions:

• Radiative transfer modeling offers a viable alternative or complement to traditional in-water radiometric measurements for satellite data validation.
• This approach enhances the accuracy of validating hyperspectral satellite data in complex coastal environments.
• Improved validation methods are essential for leveraging next-generation satellite observations of coastal ecosystems.