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Informing ocean color inversion products by seeding with ancillary observations.

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    Adding independent optical data, like particulate backscattering, to ocean color inversion algorithms significantly improves the accuracy of derived inherent optical properties. This enhancement offers a new framework for next-generation ocean color data processing.

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    Area of Science:

    • Ocean optics
    • Remote sensing
    • Biogeochemical modeling

    Background:

    • Ocean reflectance inversion algorithms derive inherent optical properties (IOPs) from spectral remote-sensing reflectances (Rrs(λ)).
    • Existing algorithms have limitations due to the finite information content in Rrs(λ) alone.
    • Incorporating additional data could enhance the accuracy of derived IOPs.

    Purpose of the Study:

    • To investigate the impact of seeding inversion schemes with an independent IOP measurement.
    • To evaluate the accuracy of derived absorption products when using particulate backscattering coefficient at 532 nm (bbp(532)) as an additional input.
    • To propose a framework for next-generation ocean color inversion.

    Main Methods:

    • Utilized the Generalized Inherent Optical Property algorithm framework default configuration (GIOP-DC).
    • Tested the 'seeding' approach by incorporating independently known or estimated bbp(532) into the inversion scheme.
    • Applied the method to both simulated and satellite-derived datasets.

    Main Results:

    • Seeded-inversion absorption products were substantially more accurate than those from the standard GIOP-DC implementation.
    • Global seasonal patterns in seeded-inversion absorption products showed variations exceeding 50% compared to GIOP-DC.
    • Demonstrated significant differences in derived absorption products due to the inclusion of bbp(532).

    Conclusions:

    • Seeding ocean color inversion algorithms with independent IOP data, such as bbp(532), improves product accuracy.
    • This approach offers a viable framework for enhancing future ocean color data processing and modeling.
    • Highlights the potential of integrating multi-sensor optical data for more robust oceanographic insights.