Articles | Volume 15, issue 12
https://doi.org/10.5194/essd-15-5281-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-5281-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
29 Nov 2023
Data description article |
| 29 Nov 2023
| 29 Nov 2023
A global daily gap-filled chlorophyll-a dataset in open oceans during 2001–2021 from multisource information using convolutional neural networks
Zhongkun Hong
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
Xingdong Li
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
Yiming Wang
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
Jianmin Zhang
State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China
Department of Hydraulic Engineering, Institute of Ocean Engineering, Tsinghua University, Beijing 100084, China
Mohamed A. Hamouda
Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, United Arab Emirates
National Water and Energy Center, United Arab Emirates University, Al Ain, United Arab Emirates
Mohamed M. Mohamed
Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, United Arab Emirates
National Water and Energy Center, United Arab Emirates University, Al Ain, United Arab Emirates
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Total article views: 4,606 (including HTML, PDF, and XML)
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Total article views: 1,568 (including HTML, PDF, and XML)
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Cited
22 citations as recorded by crossref.
- The Effect of ‘Roughness’ on Upwelling North of Cape Town in Austral Summer M. Jury https://doi.org/10.3390/oceans6040083
- A fractional Fisher equation approach to modeling chlorophyll-a patterns in Sinabang Bay F. Adami et al. https://doi.org/10.1088/1755-1315/1599/1/012031
- Comparative Evaluation of Machine Learning Models for Satellite Chlorophyll-a Gap Reconstruction in the Chesapeake Bay R. Chidananda et al. https://doi.org/10.3390/rs18111736
- Assessment of gap-filling techniques applied to satellite phytoplankton composition products for the Atlantic Ocean E. Mehdipour et al. https://doi.org/10.5194/gmd-19-1619-2026
- Reconstructing Global Chlorophyll-a Concentration for the COCTS Aboard Chinese Ocean Color Satellites via the DINEOF Method X. Ye et al. https://doi.org/10.3390/rs17203433
- Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll a concentration in the Black Sea A. Barth et al. https://doi.org/10.5194/os-20-1567-2024
- Comparative analysis of Sentinel-2 and PlanetScope imagery for chlorophyll-a prediction using machine learning models E. Wasehun et al. https://doi.org/10.1016/j.ecoinf.2024.102988
- Declining ocean greenness and phytoplankton blooms in low to mid-latitudes under a warming climate Z. Hong et al. https://doi.org/10.1126/sciadv.adx4857
- Deep learning-based chlorophyll prediction: comparison with a dynamic model and applications to fish catch forecasting J. Park et al. https://doi.org/10.5194/esd-17-795-2026
- Mask-aware biased graph learning for marine chlorophyll-a spatiotemporal forecasting under high missing rates X. He et al. https://doi.org/10.1016/j.marpolbul.2026.120196
- A Physics-Informed Convolutional Neural Network for Global Ocean PAR Gap-Filling From Satellite Observations J. Tan et al. https://doi.org/10.1109/TGRS.2026.3696266
- End-to-End Customized CNN Pipeline for Multiparameter Surface Water Quality Estimation from Sentinel-2 Imagery E. Sharaf El Din et al. https://doi.org/10.3390/rs18050794
- Physically-aware deep learning for reconstructing gap-free sea surface temperature in the South China Sea C. Su et al. https://doi.org/10.1016/j.isprsjprs.2026.04.016
- Dynamic masking for chlorophyll-a reconstruction in the Bohai and Yellow Sea: dataset generation and trend analysis J. Wang et al. https://doi.org/10.1088/2515-7620/ae5766
- Gap-Filling of Highly Incomplete Daily Chlorophyll-a Remote Sensing Time-Series Data Over the Eastern China Seas via Spatiotemporal-Periodicity Aware Tensor Completion G. Zhou et al. https://doi.org/10.1109/TGRS.2026.3668244
- Decadal and spatially complete global surface chlorophyll-a data record from satellite and BGC-Argo observations D. Ford et al. https://doi.org/10.5194/essd-18-569-2026
- Spectral Signatures and Target Discrimination in Underwater Multiwavelength Single-Photon LiDAR L. Yang et al. https://doi.org/10.3390/rs18111772
- Satellite Retrieval of Water Quality Indicators Under High Solar Zenith Angles Y. Wang et al. https://doi.org/10.1109/TGRS.2025.3580137
- Sub-daily global vegetation optical depth reconstruction from SMAP using 3D partial convolutional networks J. Kou et al. https://doi.org/10.1080/01431161.2026.2619150
- Ocean color remote sensing: From 2D legacy to the 3D, AI-driven future P. Chen & Z. Zhang https://doi.org/10.1016/j.isprsjprs.2026.04.044
- A Review of Machine Learning Applications in Ocean Color Remote Sensing Z. Zhang et al. https://doi.org/10.3390/rs17101776
- AI in Satellite Remote Sensing of the Ocean X. Li et al. https://doi.org/10.1109/JPROC.2026.3664121
22 citations as recorded by crossref.
- The Effect of ‘Roughness’ on Upwelling North of Cape Town in Austral Summer M. Jury https://doi.org/10.3390/oceans6040083
- A fractional Fisher equation approach to modeling chlorophyll-a patterns in Sinabang Bay F. Adami et al. https://doi.org/10.1088/1755-1315/1599/1/012031
- Comparative Evaluation of Machine Learning Models for Satellite Chlorophyll-a Gap Reconstruction in the Chesapeake Bay R. Chidananda et al. https://doi.org/10.3390/rs18111736
- Assessment of gap-filling techniques applied to satellite phytoplankton composition products for the Atlantic Ocean E. Mehdipour et al. https://doi.org/10.5194/gmd-19-1619-2026
- Reconstructing Global Chlorophyll-a Concentration for the COCTS Aboard Chinese Ocean Color Satellites via the DINEOF Method X. Ye et al. https://doi.org/10.3390/rs17203433
- Ensemble reconstruction of missing satellite data using a denoising diffusion model: application to chlorophyll a concentration in the Black Sea A. Barth et al. https://doi.org/10.5194/os-20-1567-2024
- Comparative analysis of Sentinel-2 and PlanetScope imagery for chlorophyll-a prediction using machine learning models E. Wasehun et al. https://doi.org/10.1016/j.ecoinf.2024.102988
- Declining ocean greenness and phytoplankton blooms in low to mid-latitudes under a warming climate Z. Hong et al. https://doi.org/10.1126/sciadv.adx4857
- Deep learning-based chlorophyll prediction: comparison with a dynamic model and applications to fish catch forecasting J. Park et al. https://doi.org/10.5194/esd-17-795-2026
- Mask-aware biased graph learning for marine chlorophyll-a spatiotemporal forecasting under high missing rates X. He et al. https://doi.org/10.1016/j.marpolbul.2026.120196
- A Physics-Informed Convolutional Neural Network for Global Ocean PAR Gap-Filling From Satellite Observations J. Tan et al. https://doi.org/10.1109/TGRS.2026.3696266
- End-to-End Customized CNN Pipeline for Multiparameter Surface Water Quality Estimation from Sentinel-2 Imagery E. Sharaf El Din et al. https://doi.org/10.3390/rs18050794
- Physically-aware deep learning for reconstructing gap-free sea surface temperature in the South China Sea C. Su et al. https://doi.org/10.1016/j.isprsjprs.2026.04.016
- Dynamic masking for chlorophyll-a reconstruction in the Bohai and Yellow Sea: dataset generation and trend analysis J. Wang et al. https://doi.org/10.1088/2515-7620/ae5766
- Gap-Filling of Highly Incomplete Daily Chlorophyll-a Remote Sensing Time-Series Data Over the Eastern China Seas via Spatiotemporal-Periodicity Aware Tensor Completion G. Zhou et al. https://doi.org/10.1109/TGRS.2026.3668244
- Decadal and spatially complete global surface chlorophyll-a data record from satellite and BGC-Argo observations D. Ford et al. https://doi.org/10.5194/essd-18-569-2026
- Spectral Signatures and Target Discrimination in Underwater Multiwavelength Single-Photon LiDAR L. Yang et al. https://doi.org/10.3390/rs18111772
- Satellite Retrieval of Water Quality Indicators Under High Solar Zenith Angles Y. Wang et al. https://doi.org/10.1109/TGRS.2025.3580137
- Sub-daily global vegetation optical depth reconstruction from SMAP using 3D partial convolutional networks J. Kou et al. https://doi.org/10.1080/01431161.2026.2619150
- Ocean color remote sensing: From 2D legacy to the 3D, AI-driven future P. Chen & Z. Zhang https://doi.org/10.1016/j.isprsjprs.2026.04.044
- A Review of Machine Learning Applications in Ocean Color Remote Sensing Z. Zhang et al. https://doi.org/10.3390/rs17101776
- AI in Satellite Remote Sensing of the Ocean X. Li et al. https://doi.org/10.1109/JPROC.2026.3664121
Saved (final revised paper)
Latest update: 02 Aug 2026
Short summary
Changes in ocean chlorophyll-a (Chl-a) concentration are related to ecosystem balance. Here, we present high-quality gap-filled Chl-a data in open oceans, reflecting the distribution and changes in global Chl-a concentration. Our findings highlight the efficacy of reconstructing missing satellite observations using convolutional neural networks. This dataset and model are valuable for research in ocean color remote sensing, offering data support and methodological references for related studies.
Changes in ocean chlorophyll-a (Chl-a) concentration are related to ecosystem balance. Here, we...
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