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

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Cited articles

Beaulieu, C., Henson, S. A., Sarmiento, J. L., Dunne, J. P., Doney, S. C., Rykaczewski, R. R., and Bopp, L.: Factors challenging our ability to detect long-term trends in ocean chlorophyll, Biogeosciences, 10, 2711–2724, https://doi.org/10.5194/bg-10-2711-2013, 2013. 
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Blondeau-Patissier, D., Gower, J. F. R., Dekker, A. G., Phinn, S. R., and Brando, V. E.: A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans, Prog. Oceanogr., 123, 123–144, https://doi.org/10.1016/j.pocean.2013.12.008, 2014. 
Cao, Z. G., Ma, R. H., Duan, H. T., Pahlevan, N., Melack, J., Shen, M., and Xue, K.: A machine learning approach to estimate chlorophyll-a from Landsat-8 measurements in inland lakes, Remote Sens. Environ., 248, https://doi.org/10.1016/j.rse.2020.111974, 2020. 
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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.
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