Snow–Vegetation Interactions and Artifacts in Coordinated Remote Sensing and Ground Observation Studies

Canopies, especially those that protrude above snow, confound remote sensing signals, complicate modeling approaches, and introduce a number of challenges in designing and executing a ground validation campaign. As part of SnowEx (Year 1), we were tasked with designing a field campaign encompassing a range of canopy properties. Unfortunately, when designing and executing a field campaign in a new study area, background geospatial and climatological data can be sparse and timelines are often short. Our objective is to assess our approach in capturing the range of canopy and snow conditions in our study. Tree distributions, thanks to their stature, were of primary interest in designing and executing the coordinated remote sensing and validation work. In windy environments, trees can have a great influence on snow distributions through their interactions with wind and interception.

The NASA measurement campaign was performed in winter 2017 at Grand Mesa, Colorado. Grand Mesa is a 30 km (east-west) by 6 km (north-south) flat-topped mountain (3000-3250 m) on Colorado’s western slope veneered by forest, with substantial areas of subalpine meadow and shrubland. Forest patch size and continuity increased along a general west-to-east gradient, grading from isolated in the western extreme to continuous in the east.

Trees were mapped using November 2010 (winter, snow-on) high-resolution imagery, where snow effectively masked vegetation < 1 m in height. In addition, trees were clustered based on their areal density into four general classes (absent, sparse, open dense), and were used with topography and roads to distribute 103 transects among the four classes. During 6-25 February 2017, field crews conducted measurements along the transects in coordination with aircraft— and ground—based measurements (e.g., lidar, radar). We found that canopy had a large impact on depths at fine scales (transect-level), but differences, when aggregated, were not as stark as expected. Transport and wind impacts, forest variability, and our classification approach offer some explanation for coarse-scale Grand Mesa snow depth trends and variability among tree classes.