Abstract
Snow albedo is highly variable over multiple temporal and spatial scales. This variability is more pronounced in areas that experience seasonal snowpack. Satellite retrievals, physically based models and parameterizations for snow albedo all require ground-based measurements for calibration, initialization, and validation. Ground measurements are generally made using upward and downward-facing pyranometers at opportunistically located weather stations that are sparsely distributed, particularly in mountainous regions. These station-based measurements cannot capture the spatial variability of albedo across the land surface. Uncrewed Aerial Vehicles (UAVs) equipped with upward and downward-facing pyranometers provide near-surface measurements of broadband albedo that are spatially distributed across landscapes, offering improvements over in-situ sensors. At the hillslope to watershed scale albedo measurements from UAVs taken over heterogeneous terrain are a function of the spatial variability in albedo and topography within the downward-facing sensor’s field-of-view (FOV). In this research we propose methods for topographic correction of UAV snow albedo measurements and comparison to gridded satellite albedo products. These methods account for the variability of surface topography and albedo within the sensor FOV, sensor tilt, and the angular response of pyranometers. We applied the proposed methodologies to UAV snow albedo measurements collected over an alpine meadow in southwest Montana, United States (45.23°, −111.28°). Sensitivity analyses were conducted to determine the effect of altering the processing FOV (PFOV) for both topographic corrections and comparison to coincident Landsat 8-derived albedo measurements. Validation from ground-based albedo measurements showed the topographic correction to reduce albedo measurement error considerably over mildly sloping terrain. Our sensitivity analyses demonstrated that outcomes from the topographic correction and satellite comparison are highly dependent on the specified PFOV. Based on field observations and analyses of UAV albedo measurements made at different altitudes, we provide guidelines for strategizing future UAV albedo surveys. This research presents considerable advances in the standardization of UAV-based albedo measurement. We establish the foundation for future research to utilize this platform to collect near-surface validation measurements over heterogeneous terrain with high accuracy and consistency.
Highlights
The albedo of snow exerts a significant control on Earth’s energy balance and water cycle
A sensitivity analysis was conducted to determine the impact of processing FOV (PFOV) on the topographic correction from 47 ground positions (3,918 total measurements) at the Namaste Valley field area taken on three clear-sky days
For the vertical transect data, the reduction in RMSE with increasing PFOV up to 140° was likely a function of two things: 1) more terrain within the pyranometer FOV was accounted for in the topographic correction and 2) more Landsat pixels were incorporated into the weighted average of Landsat 8 (LS8) albedo
Summary
The albedo of snow exerts a significant control on Earth’s energy balance and water cycle. In mountainous areas that experience seasonal snowpack, snow albedo is highly variable throughout space and time (Seidel et al, 2016). The spatiotemporal variability of snow albedo at the seasonal timescale directly influences the timing and magnitude of snowmelt and runoff as well as climatic fluctuations over longer timescales (Hall and Qu, 2006). Improvement of physically based energy balance models and snow albedo parameterizations is dependent on highquality distributed measurements that capture the spatial variability of the surface at a variety of spatial and temporal scales, to satisfy the needs of individual products (Bales et al, 2006; Molotch and Bales, 2006; Rhoades et al, 2018)
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