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Estimating pixel-level uncertainty in ocean color retrievals from MODIS
Optics Express
|October 15, 2022
Summary
This study introduces a new method to calculate uncertainty in ocean color measurements, crucial for understanding marine ecosystems. The approach accurately estimates uncertainty in satellite-retrieved reflectance, aiding in reliable ocean property analysis.
Area of Science:
- Ocean color science
- Remote sensing
- Marine optics
Background:
- Marine remote sensing reflectance (Rrs) is vital for deriving ocean properties.
- Accurate estimation of Rrs uncertainty is essential for reliable biogeochemical property retrieval.
- Atmospheric correction (AC) is a primary source of uncertainty in Rrs retrieval.
Purpose of the Study:
- To develop and verify a derivative-based method for calculating pixel-level uncertainty in Rrs.
- To assess uncertainty sources in Rrs retrieval using NASA's MSEPS AC algorithm.
- To evaluate the performance of the uncertainty quantification method using MODIS data.
Main Methods:
- A derivative-based method for uncertainty propagation was established.
- Monte Carlo (MC) analysis was used for verification.
- The method was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Aqua satellite.
Main Results:
- The derivative-based method for Rrs uncertainty (uc(Rrs)) was verified against MC analysis and in situ data.
- Relative uncertainty in Rrs at blue bands spatially correlated with chlorophyll-a concentration.
- Specific percentages of clear water pixels met the <5% relative uncertainty goal at key blue bands (412, 443, 488 nm).
Conclusions:
- The developed derivative-based method provides reasonable uc(Rrs] for ocean color retrievals.
- Further research is needed to improve forward model uncertainty and instrument calibration systematic uncertainty.
- Accurate uc(Rrs) is critical for advancing ocean color science and applications.
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