Articles | Volume 15, issue 12
https://doi.org/10.5194/essd-15-5281-2023
https://doi.org/10.5194/essd-15-5281-2023
Data description paper
 | 
29 Nov 2023
Data description paper |  | 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, Di Long, Xingdong Li, Yiming Wang, Jianmin Zhang, Mohamed A. Hamouda, and Mohamed M. Mohamed

Viewed

Total article views: 2,487 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,710 661 116 2,487 82 84
  • HTML: 1,710
  • PDF: 661
  • XML: 116
  • Total: 2,487
  • BibTeX: 82
  • EndNote: 84
Views and downloads (calculated since 10 Jul 2023)
Cumulative views and downloads (calculated since 10 Jul 2023)

Viewed (geographical distribution)

Total article views: 2,487 (including HTML, PDF, and XML) Thereof 2,410 with geography defined and 77 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2024
Download
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.
Altmetrics
Final-revised paper
Preprint