Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Zheng, Guangming; Brown, Christopher W.; DiGiacomo, Paul M.

doi:10.1016/j.rse.2023.113660

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Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

2023
By Zheng, Guangming ; Brown, Christopher W. ; DiGiacomo, Paul M.
Source: Remote Sensing of Environment, 295, 113660

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Journal Title:

Remote Sensing of Environment
Personal Author:

Zheng, Guangming ; Brown, Christopher W. ; DiGiacomo, Paul M.

Zheng, Guangming ; Brown, Christopher W. ; DiGiacomo, Paul M. Less -
NOAA Program & Office:

NESDIS (National Environmental Satellite, Data, and Information Service) ; CISESS (Cooperative Institute for Satellite Earth System Studies) ; STAR (Center for Satellite Applications and Research)

NESDIS (National Environmental Satellite, Data, and Information Service) ; CISESS (Cooperative Institute for Satellite Earth System Studies) ; STAR (Center for Satellite Applications and Research) Less -
Description:

Data of chlorophyll (Chl-a) concentration obtained from polar-orbiting ocean color satellite sensors are subject to major spatial and temporal gaps in data coverage owing to the intrinsically low sampling frequency that coincides with adverse conditions such as cloud obstruction. Such gaps can be minimized with geostationary satellites owing to their higher scanning frequency over a given region, allowing more sampling opportunities as clouds move. However, geostationary ocean color missions have not been available for most regions of the globe. The Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R series (GOES-R) is a potential alternative for the U.S. owing to improved signal-to-noise ratio compared with its predecessors.
Source:

Remote Sensing of Environment, 295, 113660
DOI:

https://doi.org/10.1016/j.rse.2023.113660
ISSN:

0034-4257
Format:

pdf
Publisher:

Elsevier BV
Document Type:

Journal Article-Accepted Manuscript
Funding:

Grant no. NA14NES4320003
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https://creativecommons.org/licenses/by-nc-nd/4.0/
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CC BY-NC-ND
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Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

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