中文版 | English
Title

Development of a deep learning-based atmospheric correction algorithm for oligotrophic oceans

Author
Publication Years
2022
DOI
Source Title
ISSN
0196-2892
EISSN
1558-0644
Volume60Pages:1-19
Abstract
Although the 5% mission goal for NASA’s standard atmospheric correction (AC) algorithm (i.e., the near-infrared (NIR) algorithm) for oligotrophic oceans has been met, this algorithm applies only to blue bands and is highly sensitive to contamination from cloud straylight and sunglint. Here, we developed an AC algorithm for clear waters based on deep learning (namely, DLAC). The algorithm was trained using 3.6 million pairs of MODIS-Aqua high-quality Rrs from the NIR algorithm and Rayleigh-corrected reflectances selected across the global oceans and from all seasons. Validations using in situ data and a chlorophyll (Chl) constraint-based approach showed that the uncertainties in the Rrs retrievals for DLAC are lower than those for the NIR algorithm, especially for the green and red bands. More importantly, the DLAC algorithm is more tolerant to cloud adjacency effects and moderate sunglint. As a result, the number of valid observations increased by ~50%, and the coverage of monthly global Level-3 Rrs composites increased by up to 20%. More spatially and temporally consistent patterns were also found for the Level-3 Rrs and Chl products, and large changes in their magnitudes (up to 20% for Rrs and 30% for Chl) were detected in some oceanic regions. With these improvements in the quality and quantity of data, our DLAC algorithm may be valuable as another option for processing global data.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
National Key Research and Development Program of China["2018YFB0504900","2018YFB0504904"] ; National Natural Science Foundation of China["42071325","42271322","42176183"]
WOS Research Area
Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS Subject
Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS Accession No
WOS:000879050800004
Publisher
ESI Research Field
GEOSCIENCES
Scopus EID
2-s2.0-85140721857
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9924172
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/407150
DepartmentSchool of Environmental Science and Engineering
Affiliation
1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
2.School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, China
3.NOAA Center for Satellite Applications and Research, College Park, MD, USA
Recommended Citation
GB/T 7714
Men,Jilin,Tian,Liqiao,Zhao,Dan,et al. Development of a deep learning-based atmospheric correction algorithm for oligotrophic oceans[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:1-19.
APA
Men,Jilin,Tian,Liqiao,Zhao,Dan,Wei,Jianwei,&Feng,Lian.(2022).Development of a deep learning-based atmospheric correction algorithm for oligotrophic oceans.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,1-19.
MLA
Men,Jilin,et al."Development of a deep learning-based atmospheric correction algorithm for oligotrophic oceans".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):1-19.
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