Comparing Aerial Lidar Observations with Terrestrial Lidar and Snow-Probe Transects from NASA\u27s 2017 SnowEx Campaign

NASA\u27s 2017 SnowEx field campaign at Grand Mesa, CO, generated Airborne Laser Scans (ALS), Terrestrial Laser Scans (TLS), and snow‐probe transects, which allowed for a comparison between snow depth measurement techniques. At six locations, comparisons between gridded ALS and TLS observations, at 1‐m resolution, had a median snow depth difference of 5 cm, root‐mean‐square difference of 16 cm, mean‐absolute difference of 10 cm, and 3‐cm difference in standard deviation. ALS generally had greater but similar snow depth values to TLS, and results were not sensitive to the gridded cell size between 0.5 and 5 m. The greatest disagreements were where snow‐off TLS scans had shrubs and high incidence angles, leading to deeper snow depths (\u3e10 cm) from ALS than TLS. The low vegetation and oblique angles caused occlusion in the TLS data and thus produced higher snow‐off bare Earth models relative to the ALS. Furthermore, in subcanopy areas where both ALS and TLS data existed, snow depth differences were comparable to differences in the open. Meanwhile, median values from 52 snow‐probe transects and near‐coincident ALS data had a mean difference of 6 cm, root‐mean‐square difference of 8 cm, mean‐absolute difference of 7 cm, and a mean difference in the standard deviation of 1 cm. Snow depth probes had greater but similar snow depth values to ALS. Therefore, based on comparisons with TLS and snow depth probes, ALS captured snow depth magnitude with better than or equal agreement to what has been reported in previous studies and showed the ability to capture high‐resolution spatial variability

A First Overview of SnowEx Ground-Based Remote Sensing Activities During the Winter 2016–2017

NASA SnowEx\u27s goal is estimating how much water is stored in Earth\u27s terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016-2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex

Resolving the Influence of Forest-Canopy Structure on Snow Depth Distributions with Terrestrial Laser Scanning

Predicting changes in forested seasonal snowpacks under altered climate scenarios is one of the most pressing hydrologic challenges facing today’s society. Approximately 2 billion people worldwide, as well as numerous ecosystems and ecosystem services depend on water released from snowmelt. Airborne- and satellite-based remote sensing methods hold the potential to transform measurements of terrestrial water stores in snowpack, improve process representations of snowpack accumulation and ablation, and generate high quality predictions that inform potential strategies to better manage water resources. While the effects of forest on snowpack are well documented, many of the fine-scale processes influenced by the forest-canopy are not directly accounted for because most snow models don’t explicitly represent canopy structure and canopy heterogeneity. The goal of this project is improving snow remote-sensing methods in forested ecosystems using fine scale lidar measurements to identify capabilities and limitations of coarser scale remote sensing. We use terrestrial laser scanning (TLS) data collected during NASA’s 2017 SnowEX campaign to resolve canopy and sub-canopy snow distributions at high resolution, and to understand the relationships between canopy and snow distributions across scales. Our sample scales range from individual trees to patches of trees across the Grand Mesa, Colorado, USA, NASA SnowEx site