Bhatta et al. (2026) A Novel Data-Driven Approach to Leaf Area Index Modeling Using High-Fidelity Simulation-Based Full-Waveform LiDAR Data

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2026
• Date: 2026-01-01
• Authors: Ramesh Bhatta, Manisha Das Chaity, Jan Van Aardt
• DOI: 10.1109/jstars.2026.3652921

Research Groups

[Information not available in the provided text]

Short Summary

This paper introduces a novel data-driven methodology for modeling Leaf Area Index (LAI) by leveraging high-fidelity full-waveform LiDAR data generated through simulations.

Objective

• To develop and evaluate a novel data-driven approach for Leaf Area Index (LAI) modeling using high-fidelity simulation-based full-waveform LiDAR data.

Study Configuration

• Spatial Scale: [Information not available in the provided text]
• Temporal Scale: [Information not available in the provided text]

Methodology and Data

• Models used: Data-driven models (specifics not detailed in the provided text).
• Data sources: High-fidelity simulation-based full-waveform LiDAR data.

Main Results

• [Information not available in the provided text]

Contributions

• [Information not available in the provided text]

Funding

• [Information not available in the provided text]

Citation

@article{Bhatta2026Novel,
  author = {Bhatta, Ramesh and Chaity, Manisha Das and Aardt, Jan Van},
  title = {A Novel Data-Driven Approach to Leaf Area Index Modeling Using High-Fidelity Simulation-Based Full-Waveform LiDAR Data},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2026},
  doi = {10.1109/jstars.2026.3652921},
  url = {https://doi.org/10.1109/jstars.2026.3652921}
}