Yu et al. (2026) 3D-DINEOF: Extension of Decomposition Dimensions of Data Interpolating Empirical Orthogonal Functions

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Menghan Yu, Enjin Zhao, Haoyu Jiang
• DOI: 10.1109/tgrs.2026.3668373

Research Groups

Not available from the provided text.

Short Summary

This paper introduces 3D-DINEOF, an extension of the Data Interpolating Empirical Orthogonal Functions (DINEOF) method to three decomposition dimensions, aiming to improve data interpolation capabilities.

Objective

• To extend the Data Interpolating Empirical Orthogonal Functions (DINEOF) method to three decomposition dimensions (3D-DINEOF) to enhance its capability for interpolating missing data.

Study Configuration

• Spatial Scale: Not available from the provided text.
• Temporal Scale: Not available from the provided text.

Methodology and Data

• Models used: 3D-DINEOF (an extension of Data Interpolating Empirical Orthogonal Functions - DINEOF).
• Data sources: Not available from the provided text.

Main Results

Not available from the provided text.

Contributions

Not available from the provided text.

Funding

Not available from the provided text.

Citation

@article{Yu20263DDINEOF,
  author = {Yu, Menghan and Zhao, Enjin and Jiang, Haoyu},
  title = {3D-DINEOF: Extension of Decomposition Dimensions of Data Interpolating Empirical Orthogonal Functions},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3668373},
  url = {https://doi.org/10.1109/tgrs.2026.3668373}
}