A global daily gap-filled chlorophyll-<i>a</i> dataset in open oceans during 2001–2021 from multisource information using convolutional neural networks

Hong, Zhongkun; Long, Di; Li, Xingdong; Wang, Yiming; Zhang, Jianmin; Hamouda, Mohamed A.; Mohamed, Mohamed M.

doi:https://doi.org/10.5194/essd-15-5281-2023

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.

https://doi.org/10.5194/essd-15-5281-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 12

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

Data sets

OCNET global daily Chlorophyll-a products Zhongkun Hong, Di Long, Xingdong Li, Yiming Wang, Jianmin Zhang, Mohamed A. Hamouda, and Mohamed M. Mohamed https://doi.org/10.5281/zenodo.10011908

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.