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Estimation of the Physical Quantities

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Related Experiment Video

Updated: Aug 25, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

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Published on: September 7, 2019

Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification.

Amir Ibrahim, Bryan A Franz, Andrew M Sayer

|October 18, 2022

Summary

This summary is machine-generated.

This study introduces a Bayesian optimal estimation method for ocean color atmospheric correction, improving reflectance retrieval accuracy and reducing errors in complex waters. The new method also provides reliable uncertainty estimates for remote sensing data.

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Area of Science:

Earth and Environmental Sciences
Oceanography
Remote Sensing

Background:

Ocean color (OC) remote sensing requires accurate atmospheric correction (AC) to infer surface reflectance (Rrs).
Traditional AC methods often do not fully quantify uncertainties, limiting data interpretation.
Bayesian inference offers a framework for simultaneous AC and uncertainty assessment.

Purpose of the Study:

To investigate the optimal estimation (OE) Bayesian method for OC atmospheric correction.
To simultaneously retrieve atmospheric and ocean properties using all available spectral bands.
To assess the performance and uncertainty quantification of the OE algorithm compared to traditional methods.

Main Methods:

Developed a neural network emulator for the radiative transfer (RT) forward model to enhance computational efficiency.
Applied the OE algorithm to synthetic data and Moderate Resolution Imaging Spectroradiometer (MODIS) observations.
Validated results using in-situ data from SeaWiFS bio-optical archive and storage system (SeaBASS) and AERONET-OC.

Main Results:

The OE algorithm significantly improved spectrally resolved remote sensing reflectance (Rrs) estimates across multiple wavelengths (443, 555, 667 nm) compared to the NASA standard operational algorithm.
Unphysical negative Rrs, common in complex waters, were reduced by a factor of three.
OE-derived pixel-level Rrs uncertainty estimates demonstrated skill when assessed against in-situ data.

Conclusions:

The optimal estimation method provides a viable and computationally efficient approach for operational ocean color atmospheric correction.
This Bayesian approach enhances Rrs retrieval accuracy and provides robust uncertainty estimates.
The method shows significant improvements, particularly in challenging aquatic environments.