Han et al. (2025) Estimation of canopy fAPAR using optical reflectance and airborne LiDAR data

Identification

• Journal: Remote Sensing of Environment
• Year: 2025
• Date: 2025-10-11
• Authors: Dalei Han, Jing Liu, Shan Xu, Tiangang Yin, Songtao Liu, Runfei Zhang, Peiqi Yang
• DOI: 10.1016/j.rse.2025.115065

Research Groups

• State Key Laboratory of Climate System Prediction and Risk Management, Nanjing, China
• Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
• Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
• Academy for Advanced Interdisciplinary Studies, Engineering Research Center of Plant Phenotyping, Ministry of Education, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, China
• Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China

Short Summary

This study developed a physically-based model (fAPARRL) that integrates optical reflectance and airborne LiDAR data to improve the estimation of the fraction of absorbed photosynthetically active radiation (fAPAR), demonstrating superior accuracy and robustness compared to traditional vegetation index- and LiDAR-based methods.

Objective

• To develop and evaluate a physically-based model (fAPARRL) that integrates optical reflectance and airborne LiDAR observations to improve the accuracy and robustness of fAPAR estimation.

Study Configuration

• Spatial Scale: Site-specific (NEON field datasets), synthetic datasets, with an intended application for regional to global scales and large-scale fAPAR estimation.
• Temporal Scale: Not explicitly defined for data collection, but the model is intended for continuous ecosystem monitoring.

Methodology and Data

• Models used: fAPARRL (proposed physically-based model), SCOPE (one-dimensional radiative transfer model), LESS (three-dimensional radiative transfer model).
• Data sources: NEON field datasets, synthetic datasets, optical reflectance data, airborne LiDAR data.

Main Results

• The fAPARRL model, which combines LiDAR and reflectance data, consistently outperformed traditional vegetation index (VI)- and LiDAR-based approaches across various datasets.
• The model achieved maximum improvements in the coefficient of determination (R²) of 0.47 compared to VI-based methods and 0.09 compared to LiDAR-based methods.
• Sensitivity analyses indicated that fAPARRL exhibited higher robustness to variations in chlorophyll content and leaf area index (LAI) than conventional methods.

Contributions

• Proposes a novel physically-based model (fAPARRL) that effectively integrates optical reflectance and airborne LiDAR data for fAPAR estimation.
• Demonstrates significant improvements in accuracy and robustness of fAPAR estimation compared to existing VI- and LiDAR-based methods.
• Offers a promising and robust tool for large-scale fAPAR estimation and ecosystem monitoring, enhancing understanding of the ecosystem carbon cycle and vegetation responses to climate change.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Han2025Estimation,
  author = {Han, Dalei and Liu, Jing and Xu, Shan and Yin, Tiangang and Liu, Songtao and Zhang, Runfei and Yang, Peiqi},
  title = {Estimation of canopy fAPAR using optical reflectance and airborne LiDAR data},
  journal = {Remote Sensing of Environment},
  year = {2025},
  doi = {10.1016/j.rse.2025.115065},
  url = {https://doi.org/10.1016/j.rse.2025.115065}
}