Estimation of canopy fAPAR using optical reflectance and airborne LiDAR data

The fraction of absorbed photosynthetically active radiation (fAPAR) of vegetation canopies is a crucial variable for understanding the ecosystem carbon cycle and assessing vegetation responses to climate change. Light absorption of the vegetation canopy is mainly determined by canopy structure and leaf optical properties. Traditional remote sensing methods typically estimate fAPAR from reflectance signals using radiative transfer models or empirical relationships with vegetation indices (VIs) and fAPAR. However, reflectance-based estimates often show moderate accuracy due to the complex relationship between reflected and absorbed fluxes. Airborne LiDAR provides direct information on canopy structural attributes relevant to radiation interception, such as fractional vegetation cover (fCover), which has been used to estimate fAPAR. However, the shortcomings of LiDAR in capturing the role of leaf optical properties introduce some uncertainty in fAPAR estimation. Combining reflectance with LiDAR data offers a promising pathway for improving fAPAR estimation. In this study, we adapted a physically-based model (${\text{fAPAR}}_{\mathrm{RL}}$) to integrate reflectance and LiDAR observations for fAPAR estimation. This model is grounded in spectral invariant theory and represents fAPAR as a function of visible and near-infrared reflectance and a LiDAR-derived canopy structural parameter. The model was evaluated against both VI- and LiDAR-based methods using NEON field datasets and synthetic datasets generated by the one-dimensional SCOPE and three-dimensional LESS radiative transfer models. Across these datasets, the combination of LiDAR and reflectance through the ${\text{fAPAR}}_{\mathrm{RL}}$ model consistently outperformed VI- and LiDAR-based approaches, with respective maximum improvements in R² of 0.47 and 0.09. Sensitivity analyses on the simulated datasets further indicated that ${\text{fAPAR}}_{\mathrm{RL}}$ exhibited higher robustness to variations in chlorophyll content and leaf area index (LAI) than other conventional methods. The proposed ${\text{fAPAR}}_{\mathrm{RL}}$ model effectively integrates reflectance and LiDAR data through a physically-based scheme, offering improved accuracy and robustness for large-scale fAPAR estimation and ecosystem monitoring.