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Updated: Jun 6, 2026

An Introduction to Processing, Fitting, and Interpreting Transient Absorption Data
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Published on: February 16, 2024

Atmospheric correction over case 2 waters with an iterative fitting algorithm.

P E Land, J D Haigh

    Applied Optics
    |December 4, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a modular algorithm for atmospheric correction over water. It accurately retrieves water-leaving reflectance and constituent concentrations using iterative model adjustments and satellite data.

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    Near-Infrared Temperature Measurement Technique for Water Surrounding an Induction-heated Small Magnetic Sphere

    Published on: April 30, 2018

    Area of Science:

    • Earth and Environmental Sciences
    • Remote Sensing
    • Ocean Optics

    Background:

    • Accurate satellite-derived ocean color relies on effective atmospheric correction.
    • Water-leaving reflectance is crucial for understanding ocean biogeochemistry and physics.
    • Existing atmospheric correction methods often have limitations in complex water conditions.

    Purpose of the Study:

    • To develop a modular atmospheric correction algorithm for satellite data over water.
    • To retrieve key water constituents (chlorophyll, sediment, gelbstoff) and aerosol properties.
    • To enhance the accuracy of ocean color data products.

    Main Methods:

    • A modular algorithm integrating atmospheric and water content models.
    • Iterative error minimization to adjust model parameters against satellite reflectances.
    • Utilizes single-scattering theory with multiple-scattering corrections and lookup tables for atmospheric modeling.
    • Incorporates aerosol types, chlorophyll, inorganic sediment, and gelbstoff concentrations for water modeling.

    Main Results:

    • The algorithm successfully predicts visible and near-infrared reflectances.
    • It derives water-leaving reflectance, water constituent concentrations, and aerosol optical depth and type.
    • Demonstrated application using model data for the Sea-viewing Wide Field-of-View Sensor (SeaWiFS).

    Conclusions:

    • The proposed modular algorithm offers a robust approach to atmospheric correction for ocean color remote sensing.
    • It enables the retrieval of essential water quality parameters and aerosol information.
    • The method shows promise for improving the accuracy and utility of satellite oceanographic data.