Zhao et al. (2026) Uncertainty-Aware Tree Root Morphology and Phenotypic Parameter Reconstructed From Ground-Penetrating Radar Signal via Physics-Informed Variational Networks

Identification

• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Xinyu Zhao, Xiaowei Zhang, Shenghua Lv, Mingdong Li, Jianghao Zhang, Lin Chen, Jian Wen
• DOI: 10.1109/tgrs.2026.3670339

Research Groups

[Not available from provided text]

Short Summary

This paper focuses on reconstructing uncertainty-aware tree root morphology and phenotypic parameters by processing Ground-Penetrating Radar signals using Physics-Informed Variational Networks.

Objective

• To reconstruct uncertainty-aware tree root morphology and phenotypic parameters from Ground-Penetrating Radar signals via Physics-Informed Variational Networks.

Study Configuration

• Spatial Scale: [Not available from provided text]
• Temporal Scale: [Not available from provided text]

Methodology and Data

• Models used: Physics-Informed Variational Networks
• Data sources: Ground-Penetrating Radar (GPR) signals

Main Results

[Not available from provided text]

Contributions

[Not available from provided text]

Funding

[Not available from provided text]

Citation

@article{Zhao2026UncertaintyAware,
  author = {Zhao, Xinyu and Zhang, Xiaowei and Lv, Shenghua and Li, Mingdong and Zhang, Jianghao and Chen, Lin and Wen, Jian},
  title = {Uncertainty-Aware Tree Root Morphology and Phenotypic Parameter Reconstructed From Ground-Penetrating Radar Signal via Physics-Informed Variational Networks},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  year = {2026},
  doi = {10.1109/tgrs.2026.3670339},
  url = {https://doi.org/10.1109/tgrs.2026.3670339}
}