Preprints
https://doi.org/10.5194/essd-2024-6
https://doi.org/10.5194/essd-2024-6
02 Feb 2024
 | 02 Feb 2024
Status: this preprint is currently under review for the journal ESSD.

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

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

Abstract. Satellite remote sensing of sea surface chlorophyll products sometimes yields a significant amount of sporadic missing data due to various variables, such as weather conditions and operational failures of satellite sensors. The limited nature of satellite observation data impedes the utilization of satellite data in the domain of marine research. Hence, it is highly important to investigate techniques for reconstructing satellite remote sensing data to obtain spatially and temporally uninterrupted and comprehensive data within the desired area. This approach will expand the potential applications of remote sensing data and enhance the efficiency of data usage. To address this series of problems, based on the demand for research on the ecological effects of multiscale dynamic processes in the South China Sea, this paper combines the advantages of the optimal interpolation (OI) method and SwinUnet and successfully develops a deep learning model based on the expected variance in data anomalies, called OI-SwinUnet. The OI-SwinUnet method was used to reconstruct the MODIS chlorophyll-a concentration products of the South China Sea from 2013 to 2017. When comparing the performances of the DINEOF, OI, and Unet approaches, it is evident that the OI-SwinUnet algorithm outperforms the other algorithms in terms of reconstruction. We conduct a reconstruction experiment using different artificial missing patterns to assess the resilience of OI-SwinUnet. Ultimately, the reconstructed dataset was utilized to examine the seasonal variations and geographical distribution of chlorophyll-a concentrations in various regions of the South China Sea. Additionally, the impact of the plume front on the dispersion of phytoplankton in upwelling areas was assessed. The potential use of reconstructed products to investigate the process by which individual mesoscale eddies affect sea surface chlorophyll is also examined.

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

Status: open (until 08 Apr 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Haibin Ye, Chaoyu Yang, Yuan Dong, Shilin Tang, and Chuqun Chen

Data sets

OI-SwinUnet reconstructed daily Chlorophyll-a products in the South China Sea Haibin Ye, Chaoyu Yang, Yuan Dong, Shilin Tang, and Chuqun Chen https://doi.org/10.5281/zenodo.10478524

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

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Short summary
A deep learning model for gap filling based on the expected variance was developed;OI-SwinUnet achieves a good performance in reconstructing the chlorophyll-a concentration data in the South China Sea;the reconstructed dataset depicts well in both the spatiotemporal patterns at seasonal-scale & fast-change process at weather-scale;the reconstructed data can show chlorophyll perturbations of individual eddy at different life stages, giving academics a unique & complete perspective on eddy study.
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