Ocean Science
Ocean Science
OS
Articles | Volume 22, issue 4
Articles | Volume 22, issue 4
https://doi.org/10.5194/os-22-2101-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-22-2101-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 4
Research article
 | 
03 Jul 2026
Research article |  | 03 Jul 2026

A T-DINEOF model for multiple oceanic variables reconstruction

Bo Ping, Ruiting Yang, Yunshan Meng, Fenzhen Su, and Cunjin Xue

Viewed

Total article views: 1,279 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
879 284 116 1,279 134 62 77
  • HTML: 879
  • PDF: 284
  • XML: 116
  • Total: 1,279
  • Supplement: 134
  • BibTeX: 62
  • EndNote: 77
Views and downloads (calculated since 09 Mar 2026)
Cumulative views and downloads (calculated since 09 Mar 2026)

Viewed (geographical distribution)

Total article views: 1,279 (including HTML, PDF, and XML) Thereof 1,245 with geography defined and 34 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Jul 2026
Download
Short summary
Satellite observations are often incomplete due to cloud cover, resulting in missing ocean data. To address this, we developed T-DINEOF (Data Interpolating Empirical Orthogonal Function), a reconstruction method that simultaneously estimates sea surface temperature, chlorophyll concentration, and wind conditions by learning relationships among variables. Results show that T-DINEOF improves reconstruction accuracy, especially in regions with sparse data or weak correlations, providing more reliable ocean information for environmental monitoring.
Read more
Share