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Approach for Propagating Radiometric Data Uncertainties Through NASA Ocean Color Algorithms
Lachlan I W McKinna1,2, Ivona Cetinić2,3, Alison P Chase4
1Go2Q Pty Ltd., Buderim, QLD, Australia.
Frontiers in Earth Science
|July 11, 2020
Summary
This study introduces a method to estimate uncertainties in ocean color data products derived from satellite observations. Applying first-order first-moment (FOFM) calculus helps quantify measurement errors for key oceanographic variables.
Area of Science:
- Remote Sensing
- Oceanography
- Data Science
Background:
- Ocean color remote sensing provides vital data on ocean properties.
- Existing algorithms derive geophysical products from satellite radiometry.
- Accurate estimation of measurement uncertainty in these products is lacking.
Purpose of the Study:
- To provide a comprehensive overview of first-order first-moment (FOFM) calculus for propagating radiometric uncertainties.
- To demonstrate FOFM uncertainty formulations for key NASA ocean color data products.
- To enable pixel-by-pixel uncertainty estimation in routine data processing.
Main Methods:
- Formulation of FOFM calculus for uncertainty propagation through bio-optical models.
- Application to chlorophyll-a, diffuse attenuation coefficient, particulate organic carbon, and inherent optical properties.
- Validation of FOFM using Monte Carlo simulations with in situ remote sensing reflectance data.
Main Results:
- FOFM uncertainty calculations were demonstrated for multiple ocean color products.
- Comparison of uncertainties under different relative uncertainty assumptions (1%, 5%, 10%) for remote sensing reflectance.
- Highlighting the importance of spectral covariances in FOFM methodology using SeaWiFS data.
Conclusions:
- FOFM calculus offers a computationally efficient method for estimating measurement uncertainty in ocean color data products.
- Accurate uncertainty quantification is critical for long-term climate studies and algorithm development.
- This method provides valuable information for end-users selecting data products or developing new algorithms.
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