Articles | Volume 16, issue 7
https://doi.org/10.5194/essd-16-3125-2024
https://doi.org/10.5194/essd-16-3125-2024
Data description paper
 | 
04 Jul 2024
Data description paper |  | 04 Jul 2024

A daily reconstructed chlorophyll-a dataset in the South China Sea from MODIS using OI-SwinUnet

Haibin Ye, Chaoyu Yang, Yuan Dong, Shilin Tang, and Chuqun Chen

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Cited articles

Alvera-Azarate, A., Barth, A., and Rixen, M.: Reconstruction of Incomplete Satelite SST Data Sets Using Empirical Orthogonal Functions: Application to the Adriatic Sea Surface Temperature, Ocean Model., 9, 325–346, 2005. 
Barrot, G.: GlobColour: An EO based service supporting global ocean carbon cycle research Product User Guide Version 1.4, 2010. 
Barth, A., Alvera-Azcárate, A., Licer, M., and Beckers, J.-M.: DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations, Geosci. Model Dev., 13, 1609–1622, https://doi.org/10.5194/gmd-13-1609-2020, 2020. 
Beckers, J. M. and Rixen, M.: EOF Calculations and Data Filling from Incomplete Oceanographic Datasets, J. Atmos. Ocean. Tech., 20, 1839–1856, 2003. 
Bessenbacher, V., Seneviratne, S. I., and Gudmundsson, L.: CLIMFILL v0.9: a framework for intelligently gap filling Earth observations, Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, 2022. 
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Short summary
A deep-learning model for gap-filling based on expected variance was developed. OI-SwinUnet achieves good performance reconstructing chlorophyll-a concentration data on the South China Sea. The reconstructed dataset depicts both the spatiotemporal patterns at the seasonal scale and a fast-change process at the weather scale. Reconstructed data show chlorophyll perturbations of individual eddies at different life stages, giving academics a unique and complete perspective on eddy studies.
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