Characterizing Spatial Structures of Field-Scale Snowpack using Unpiloted Aerial System (UAS) Lidar and SfM Photogrammetry

Abstract. Uncrewed Aerial Systems (UAS) lidar and structure-from-motion (SfM) photogrammetry have emerged as viable methods to map high-resolution snow depths (~1 m). These technologies enable a better understanding of snowpack spatial structure and its evolution over time, advancing hydrologic and ecological applications. In this study, a series of UAS lidar/SfM snow depth maps were collected during the 2020/21 winter season in Durham, New Hampshire, USA with three objectives: (1) quantifying UAS lidar/SfM snow depth retrieval performance using multiple in-situ measurement techniques (magnaprobe and field cameras), (2) conducting a quantitative comparison of lidar and SfM snow depths (< 35 cm) throughout the winter, and (3) better understanding the spatial structure of snow depth and its relationship with terrain features. The UAS surveys were conducted over approximately 0.35 km² including both open fields and a mixed forest. In the field, lidar had a lower error than SfM compared to in-situ observations with a Mean Absolute Error (MAE) of 3.0 cm for lidar and 5.0–14.3 cm for SfM. In the forest, SfM greatly overestimated snow depths compared to lidar (lidar MAE = 2.7–7.3 cm, SfM MAE = 32.0–44.7 cm). Even though snow depth differences between the magnaprobe and field cameras were found, they had only a modest impact on the UAS snow depth validation. Using the concept of temporal stability, we found that the spatial structure of snow depth captured by lidar was generally consistent throughout the period indicating a strong influence from static land characteristics. Considering all areas (forest and fields), the spatial structure of snow depth was primarily influenced by vegetation type (e.g., fields, deciduous, and coniferous forests). Within the field, the spatial structure was primarily correlated with slope and forest canopy shadowing effects.