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

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Latest update: 04 Sep 2026
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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.
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