An Operational Methodology for
Validating Satellite-Based Snow
Albedo Measurements Using a UAV
Andrew Mullen
1
, Eric A. Sproles
1
,
2
*, Jordy Hendrikx
1
, Joseph A. Shaw
3
and
Charles K. Gatebe
4
1
Department of Earth Sciences, College of Letters and Science, Montana State University, Bozeman, MT, United States,
2
Montana Institute on Ecosystems, Montana State University, Bozeman, MT, United States,
3
Department of Electrical and
Computer Engineering, Norm Asbjornson College of Engineering, Montana State University, Bozeman, MT, United States,
4
Ames
Research Center, National Aeronautics and Space Administration (NASA), Moffett Field, CA, United States
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 surfac e 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 correction s 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.
Edited by:
Clare Webster,
WSL Institute for Snow and Avalan che
Research SLF, Switzerland
Reviewed by:
Pavel Švec,
VSB-Technical University of Ostrava,
Czechia
Yu-Hsuan Tu,
King Abdullah University of Science
and Technology, Saudi Arabia
Johanna Malle,
WSL Institute for Snow and Avalan che
Research SLF, Switzerland
*Correspondence:
Eric A. Sproles
eric.sproles@montana.edu
Specialty section:
This article was submitted to
Unmanned Aerial Systems (UASs and
UAVs),
a section of the journal
Frontiers in Remote Sensing
Received: 31 August 2021
Accepted: 10 December 2021
Published: 11 January 2022
Citation:
Mullen A, Sproles EA, Hendrikx J,
Shaw JA and Gatebe CK (2022) An
Operational Methodology for Validating
Satellite-Based Snow Albedo
Measurements Using a UAV.
Front. Remote Sens. 2:767593.
doi: 10.3389/frsen.2021.767593
Frontiers in Remote Sensing | www.frontiersin.org January 2022 | Volume 2 | Article 7675931
METHODS
published: 11 January 2022
doi: 10.3389/frsen.2021.767593
