Identifying Snow Surface Controls on UAV Lidar Intensity for Grain Size Retrieval

Beyond lidar’s established role in snow hydrology for mapping snow depth, lidar intensity measurements offer a promising means of informing surface energy balance through retrieval of snow grain size, a primary control on snow albedo and net shortwave radiation. In particular, near-infrared aerial lidar intensity has demonstrated strong potential for retrieving snow reflectance and grain size over mountainous watersheds when corrections for range and incidence angle are applied. However, the accuracy of lidar intensity correction (and subsequent grain size retrieval) is highly sensitive to the quality of calibration data, which has typically relied on coincident imaging spectroscopy reflectance measurements. To improve the robustness and transferability of lidar intensity calibration approaches, a clearer understanding of how the laser pulse interacts with snow surface properties is needed. Here, we address this gap using a time series of UAV lidar flights conducted in the Wasatch Mountains near Sundance, Utah using a DJI M300 equipped with a Zenmuse L1 sensor. Each flight is accompanied by detailed snow pit observations and comprehensive in situ measurements of snow physical and optical properties. We apply machine learning techniques to model UAV lidar intensity returns and to quantify the relative influence of measured snow properties on the laser signal. These results provide new insight into the dominant controls on UAV lidar intensity over snow and identify key snow surface properties governing the laser-snow interaction. Together, these findings suggest a pathway toward simplified and more transferable calibration strategies that do not require coincident imaging spectroscopy. As a result, UAV lidar intensity can directly complement existing methods by enabling fine-scale snow grain size estimation independent of solar illumination.