Generalized ocean color inversion model for retrieving marine inherent optical properties

Area of Science:

• Oceanography
• Remote Sensing
• Optical Physics

Background:

• Satellite ocean color measurements provide global daily estimates of marine inherent optical properties (IOPs).
• Semi-analytical algorithms (SAAs) are crucial for inverting satellite-observed water color into IOPs.
• Existing SAAs often lack broad applicability across diverse water masses and seasons.

Purpose of the Study:

• To address limitations in current SAAs and foster community consensus.
• To introduce the generalized IOP (GIOP) model software for flexible SAA construction and evaluation.
• To present a preliminary default configuration (GIOP-DC) and analyze SAA parameterization sensitivities.

Main Methods:

• Deconstruction of existing SAAs to identify similarities and differences.
• Development of the generalized IOP (GIOP) model software enabling runtime SAA construction.
• Verification of GIOP-DC performance against other SAAs using in situ and synthetic data.
• Quantification of SAA output sensitivity to parameterization choices.

Main Results:

• The GIOP model facilitates the construction and evaluation of various SAAs.
• GIOP-DC demonstrates comparable performance to existing popular SAAs.
• Sensitivity analysis reveals hierarchical dependencies of SAA output on parameterizations.
• Identification of key SAA components requiring further research.

Conclusions:

• GIOP provides a unified framework for developing and assessing SAAs for ocean color data.
• Understanding parameterization sensitivities is crucial for improving algorithm accuracy and reducing uncertainties.
• The GIOP model supports ensemble inversion modeling and the development of regionally tuned algorithms for enhanced oceanographic studies.