Quantifying spatial distribution of the snowpack is crucial for hydrological, ecological, and climate research, as well as their applications. Due to the high spatial resolution and extensive coverage, Airborne Light Detection and Ranging (LiDAR) has emerged as an effective tool for large-scale snow depth estimation. However, discrepancies between LiDAR-derived and manually measured snow depth values exist across areas influenced by topographical and vegetation characteristics such as canopy height, slope, and roughness. This study aims to 1) evaluate the performance of the airborne LiDAR snow depth measurements compared to magnaprobe in-situ data and 2) identify key factors affecting the accuracy of airborne LiDAR snow depth measurements focusing on the boreal forest environment. We utilize airborne LiDAR data and ground-based snow depth observations collected in the Fairbanks region of central Alaska during NASA SnowEx 2023 Alaska Campaign. The study focuses on three subregions: Bonanza Creek Experimental Forest (BCEF), Farmers Loop Creamers Field (FLCF), and Caribou-Poker Creeks Research Watershed (CPCRW). The results showed that the LiDAR snow depth data has a reasonable agreement with in-situ observations (R: 0.605, Mean Absolute Error: 18.8 cm) but exhibits varying levels of errors across the three subregions. By applying regression analysis and machine learning, we quantify the contribution of individual factors to measurement discrepancies and determine which factors are most influential. We employed Gradient Boosting Machine (GBM) model using five LiDAR-derived environmental variables—canopy height, elevation, slope, roughness, and ground point density—as predictors of relative error. Across all subregions and models, canopy height consistently emerged as the most important factor of LiDAR snow depth error.