This study explores advanced methodologies for estimating subcanopy solar radiation using LiDAR (Light Detection and Ranging)-derived point clouds and GIS (Geographic Information System)-based models, with a focus on evaluating the impact of different LiDAR data types on model performance. The research compares the performance of two modeling approaches—r.sun and the Point Cloud Solar Radiation Tool (PCSRT)—in capturing solar radiation dynamics beneath tree canopies. The models were applied to two contrasting environments: a forested area and a built-up area. The r.sun model, based on raster data, and the PCSRT model, which uses voxelized point clouds, were evaluated for their accuracy and efficiency in simulating solar radiation. Data were collected using terrestrial laser scanning (TLS), unmanned laser scanning (ULS), and aerial laser scanning (ALS) to capture the structural complexity of canopies. Results indicate that the choice of LiDAR data significantly affects model outputs. PCSRT, with its voxel-based approach, provides higher precision in heterogeneous forest environments. Among the LiDAR types, ULS data provided the most accurate solar radiation estimates, closely matching in situ pyranometer measurements, due to its high-resolution coverage of canopy structures. TLS offered detailed local data but was limited in spatial extent, while ALS, despite its broader coverage, showed lower precision due to insufficient point density under dense canopies. These findings underscore the importance of selecting appropriate LiDAR data for modeling solar radiation, particularly in complex environments.

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