A T-DINEOF model for multiple oceanic variables reconstruction

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

doi:10.5194/os-22-2101-2026

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

Cited articles

Alvera-Azcárate, A., Barth, A., Beckers, J. M., and Weisberg, R. H.: Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields, J. Geophys. Res.-Oceans, 112, C03008, https://doi.org/10.1029/2006JC003660, 2007.

Alvera-Azcárate, A., Barth, A., Sirjacobs, D., and Beckers, J.-M.: Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF, Ocean Sci., 5, 475–485, https://doi.org/10.5194/os-5-475-2009, 2009.

Alvera-Azcárate, A., Barth, A., Parard, G., and Beckers, J. M.: Analysis of SMOS sea surface salinity data using DINEOF, Remote Sens. Environ., 180, 137–145, https://doi.org/10.1016/j.rse.2016.02.044, 2016.

Beckers, J. M. and Rixen, M.: EOF calculations and data filling from incomplete oceanographic datasets, J. Atmos. Ocean. Tech., 20, 1839–1856, https://doi.org/10.1175/1520-0426(2003)020<1839:ECADFF>2.0.CO;2, 2003.

Beckers, J.-M., Barth, A., and Alvera-Azcárate, A.: DINEOF reconstruction of clouded images including error maps – application to the Sea-Surface Temperature around Corsican Island, Ocean Sci., 2, 183–199, https://doi.org/10.5194/os-2-183-2006, 2006.

More articles (40)

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.