Chen et al. (2026) Estimating Leaf Area Index and Leaf Nitrogen Content for Individual Populus Tree Using UAV Hyperspectral Data With Dual-Branch Deep Learning Architecture

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Zhulin Chen, Huimin Zou, Xuefeng Wang, Naijing Zhang, Guofeng Tao, Jie Li, Shijiao Qiao, Sheng Xu
• DOI: 10.1109/tgrs.2026.3668265

Research Groups

[Information not available from provided text]

Short Summary

This study aims to estimate Leaf Area Index (LAI) and Leaf Nitrogen Content (LNC) for individual Populus trees by leveraging UAV-borne hyperspectral data processed with a dual-branch deep learning architecture.

Objective

• To accurately estimate Leaf Area Index (LAI) and Leaf Nitrogen Content (LNC) for individual Populus trees.

Study Configuration

• Spatial Scale: Individual Populus tree.
• Temporal Scale: [Information not available from provided text]

Methodology and Data

• Models used: Dual-branch deep learning architecture.
• Data sources: UAV hyperspectral data.

Main Results

[Information not available from provided text]

Contributions

[Information not available from provided text]

Funding

[Information not available from provided text]

Citation

@article{Chen2026Estimating,
  author = {Chen, Zhulin and Zou, Huimin and Wang, Xuefeng and Zhang, Naijing and Tao, Guofeng and Li, Jie and Qiao, Shijiao and Xu, Sheng},
  title = {Estimating Leaf Area Index and Leaf Nitrogen Content for Individual <i>Populus</i> Tree Using UAV Hyperspectral Data With Dual-Branch Deep Learning Architecture},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3668265},
  url = {https://doi.org/10.1109/tgrs.2026.3668265}
}