Zhao et al. (2026) Improving Full-Spectrum Reconstruction of Solar-Induced Chlorophyll Fluorescence Using Principal Component Analysis Based on an SIF-Free Leaf Reflectance Dataset

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Ding Zhao, Shanshan Du, Yi Du, Xia Liu, Liangyun Liu
• DOI: 10.1109/tgrs.2026.3655806

Research Groups

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Short Summary

This paper aims to improve the full-spectrum reconstruction of Solar-Induced Chlorophyll Fluorescence (SIF) by utilizing Principal Component Analysis (PCA) applied to an SIF-free leaf reflectance dataset.

Objective

• To improve the full-spectrum reconstruction of Solar-Induced Chlorophyll Fluorescence (SIF).

Study Configuration

• Spatial Scale: Leaf level.
• Temporal Scale: []

Methodology and Data

• Models used: Principal Component Analysis (PCA).
• Data sources: SIF-free leaf reflectance dataset.

Main Results

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Contributions

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Funding

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Citation

@article{Zhao2026Improving,
  author = {Zhao, Ding and Du, Shanshan and Du, Yi and Liu, Xia and Liu, Liangyun},
  title = {Improving Full-Spectrum Reconstruction of Solar-Induced Chlorophyll Fluorescence Using Principal Component Analysis Based on an SIF-Free Leaf Reflectance Dataset},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3655806},
  url = {https://doi.org/10.1109/tgrs.2026.3655806}
}