Development and Validation of an Empirical Ocean Color Algorithm with Uncertainties: A Case Study with the...

Area of Science:

• Ocean optics and remote sensing.
• Bio-optical modeling and algorithm development.

Background:

• Empirical ocean color models require robust assessment considering both algorithmic and observational uncertainties.
• Accurate estimation of particulate backscattering coefficient (b(555)) is crucial for ocean color applications.

Purpose of the Study:

• To develop and validate an empirical algorithm for deriving particulate backscattering coefficient at 555 nm (b(555)).
• To investigate the impact of incorporating model and in situ measurement uncertainties into model development and assessment.

Main Methods:

• Developed a new empirical algorithm using a remote sensing reflectance line height (LH) metric to derive b(555).
• Trained the model using a high-quality bio-optical dataset with coincident in situ measurements.
• Validated the LH-based model against two other models using independent data and uncertainty-corrected metrics (bias, MAE).
• Explored zeta-scores and z-tests for assessing model skill.

Main Results:

• The developed LH-based algorithm provides a new method for estimating b(555).
• Measurement uncertainties were shown to significantly influence standard validation metrics like mean bias and MAE.
• The study highlights the importance of accounting for uncertainties in model performance evaluation.

Conclusions:

• The proposed LH metric offers a viable approach for empirical estimation of b(555).
• Incorporating uncertainties in model and observation data is essential for accurate ocean color model assessment.
• Further exploration of advanced statistical methods like zeta-scores is recommended for robust model evaluation.