Preprints
https://doi.org/10.5194/essd-2024-6
https://doi.org/10.5194/essd-2024-6
02 Feb 2024
 | 02 Feb 2024
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Haibin Ye, Chaoyu Yang, Yuan Dong, Shilin Tang, and Chuqun Chen

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-6', Chen Jun, 08 Mar 2024
  • RC2: 'Comment on essd-2024-6', Anonymous Referee #2, 30 Mar 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-6', Chen Jun, 08 Mar 2024
  • RC2: 'Comment on essd-2024-6', Anonymous Referee #2, 30 Mar 2024
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

Viewed

Total article views: 589 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
467 84 38 589 26 31
  • HTML: 467
  • PDF: 84
  • XML: 38
  • Total: 589
  • BibTeX: 26
  • EndNote: 31
Views and downloads (calculated since 02 Feb 2024)
Cumulative views and downloads (calculated since 02 Feb 2024)

Viewed (geographical distribution)

Total article views: 568 (including HTML, PDF, and XML) Thereof 568 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Jun 2024
Download
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.
Altmetrics