Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint

Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint

Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwell...

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Bibliographic Details
Main Authors: Zachary Fasnacht, Joanna Joiner, David Haffner, Wenhan Qin, Alexander Vasilkov, Patricia Castellanos, Nickolay Krotkov
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-05-01
Series:Frontiers in Remote Sensing
Subjects:
ocean color
machine learning
OMI
TROPOMI
PACE
TEMPO
Online Access:https://www.frontiersin.org/articles/10.3389/frsen.2022.846174/full

Internet

https://www.frontiersin.org/articles/10.3389/frsen.2022.846174/full

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