Comparison of in-situ snow depth measurements and impacts on validation of unpiloted aerial system lidar over a mixed-use temperate forest landscape

Abstract. The accuracy and consistency of snow depth measurements depend on the measuring device and the conditions of the site and snowpack in which it is being used. This study compares collocated snow depth measurements from a magnaprobe automatic snow depth probe and a Federal snow tube, then uses these measurements to validate snow depth maps from an unpiloted aerial system (UAS) with an integrated Light Detection and Ranging (lidar) sensor. We conducted three snow depth sampling campaigns from December 2020 to February 2021 that included 39 open field, coniferous, mixed, and deciduous forest sampling sites in Durham, New Hampshire, United States. Average snow depths were between 9 and 15 cm. For all sampling campaigns and land cover types, the magnaprobe snow depth measurements were consistently deeper than the snow tube. There was a 12 % average difference between the magnaprobe (14.9 cm) and snow tube (13.2 cm) average snow depths with a greater difference in the forest than the field. The lidar estimates of snow depth were 3.6 cm and 1.9 cm shallower on average than the magnaprobe and snow tube, respectively. While the magnaprobe had a better correlation with the UAS lidar, the root mean square errors were higher for the magnaprobe than the snow tube, likely due to overprobing by the magnaprobe into leaf litter. Even though the differences between the in-situ sampling methods resulted in modest performance differences when used to validate the UAS lidar snow depths in this study, measuring vegetation height, leaf litter, and soil frost with in-situ snow depths from multiple sampling techniques helped to account for the errors of in-situ snow depth for robust validation of the UAS snow depth maps.