Comparative modeling of subcanopy solar radiation: Evaluating PCSRT and r.sun with multi-source LiDAR data

Accurate modeling of subcanopy solar radiation is vital for ecological modeling, forest management, and urban planning, as it influences vegetation growth, energy balance, and environmental dynamics. This study provides a comprehensive evaluation of two solar radiation models: PCSRT and r.sun, leveraging LiDAR datasets from terrestrial (TLS), unmanned aerial (ULS), and airborne (ALS) scanning. The results demonstrate that the choice of modeling approach and data source substantially impacts the accuracy of solar radiation estimates, particularly in complex forested environments. PCSRT, with its voxel-based 3D modeling, excels in capturing intricate subcanopy radiation dynamics, especially when combined with high-density LiDAR data such as TLS and ULS. In contrast, the raster-based r.sun model, while computationally efficient and scalable, is better suited for broader regional analyses, particularly in less heterogeneous environments such as urban areas. This research underscores the critical role of LiDAR data density in determining model accuracy, with ULS providing the most reliable results, TLS capturing detailed local variations but facing coverage limitations, and ALS offering scalability but with reduced precision in dense canopy structures. Practical implications of this study include tailored recommendations for selecting modeling tools and LiDAR datasets based on the objectives and spatial scale of the study. PCSRT is recommended for high-resolution ecological studies requiring detailed subcanopy analysis, whereas r.sun is preferable for large-scale applications where computational efficiency is prioritized. However, limitations of each approach are acknowledged, including the computational intensity of PCSRT and the lower precision of r.sun in capturing canopy interactions.

Keywords: subcanopy solar radiation, solar radiation models, LiDAR, r.sun, PCSRT