Restoration of missing ocean color data in high-latitude oceans using neural network model Article Swipe
Hao Li
,
Xianqiang He
,
Yan Bai
,
Difeng Wang
,
Teng Li
,
Fang Gong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1080/20964471.2025.2474655
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1080/20964471.2025.2474655
Satellite ocean color remote sensing plays a crucial role in monitoring marine environment at both regional and global scales. However, due to the reduced accuracy of atmospheric correction models under large solar zenith angles (≥70°), publicly available satellite ocean color products lack valid datasets for high-latitude oceans (≥50°S or ≥50°N) during winter. Based on a neural network atmospheric correction model designed for high solar zenith angle observation environments (which used a Rayleigh scattering lookup table generated by PCOART-SA to compute Rayleigh scattering radiance and a neural network model to invert remote sensing reflectance from Rayleigh-corrected radiance), this study has established a monthly ocean color product dataset for high-latitude oceans, named NN-LAT50, covering the period from 2003 to 2020. We validated the accuracy of the ocean color products in NN-LAT50 dataset using multiple in situ datasets, and the results indicated that NN-LAT50 had more reliable and accurate retrievals compared to the NASA released ocean color products in high latitude oceans. Furthermore, during autumn and winter, coverage of the NN-LAT50 dataset far exceeds that of products released by NASA. For instance, during the winter in the Southern Hemisphere, the coverage rates are 3.02% for MODIS/Aqua, 21.59% for VIIRS, and 1.74% for OLCI, while the NN-LAT50 dataset maintains a coverage rate of 38.64%. This study is the first to establish a long-term (2003–2020) ocean color product dataset covering high-latitude oceans during winter, which can significantly enhance the observation of ecological changes in polar and subpolar oceans.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/20964471.2025.2474655
- https://www.tandfonline.com/doi/pdf/10.1080/20964471.2025.2474655?needAccess=true
- OA Status
- gold
- Cited By
- 2
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408291413
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- OpenAlex ID
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https://openalex.org/W4408291413Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/20964471.2025.2474655Digital Object Identifier
- Title
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Restoration of missing ocean color data in high-latitude oceans using neural network modelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-10Full publication date if available
- Authors
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Hao Li, Xianqiang He, Yan Bai, Difeng Wang, Teng Li, Fang GongList of authors in order
- Landing page
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https://doi.org/10.1080/20964471.2025.2474655Publisher landing page
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https://www.tandfonline.com/doi/pdf/10.1080/20964471.2025.2474655?needAccess=trueDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.tandfonline.com/doi/pdf/10.1080/20964471.2025.2474655?needAccess=trueDirect OA link when available
- Concepts
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Artificial neural network, Latitude, High latitude, Ocean color, Missing data, Climatology, Geology, Environmental science, Oceanography, Computer science, Artificial intelligence, Geodesy, Satellite, Machine learning, Astronomy, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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