Zhai et al. (2026) A Physics-Informed Spatiotemporal Deep Learning Algorithm for High-Resolution Leaf Area Index Retrieval

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Dechao Zhai, Huazhong Ren, Yaokui Cui, Zhaoyuan Yao, Naijie Peng, Qunchao He, Zhicheng Huang, Jiazheng Li, Zhenrong Liu
• DOI: 10.1109/jstars.2026.3668316

Research Groups

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Short Summary

This paper introduces a physics-informed spatiotemporal deep learning algorithm designed for the high-resolution retrieval of Leaf Area Index (LAI).

Objective

• To develop and apply a physics-informed spatiotemporal deep learning algorithm for high-resolution Leaf Area Index (LAI) retrieval.

Study Configuration

• Spatial Scale: High-resolution, specific scale not provided in the given text.
• Temporal Scale: Spatiotemporal, specific scale not provided in the given text.

Methodology and Data

• Models used: Physics-Informed Spatiotemporal Deep Learning Algorithm.
• Data sources: Likely satellite or remote sensing data for LAI retrieval, specific sources not provided in the given text.

Main Results

[Key findings, synthetic and quantitative]

Contributions

[Original value of the article with respect to existing literature]

Funding

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Citation

@article{Zhai2026PhysicsInformed,
  author = {Zhai, Dechao and Fan, Wenjie and Ren, Huazhong and Cui, Yaokui and Yao, Zhaoyuan and Peng, Naijie and He, Qunchao and Huang, Zhicheng and Li, Jiazheng and Liu, Zhenrong},
  title = {A Physics-Informed Spatiotemporal Deep Learning Algorithm for High-Resolution Leaf Area Index Retrieval},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3668316},
  url = {https://doi.org/10.1109/jstars.2026.3668316}
}