Autonomous Aerial Vehicles (AAVs) as a Tool for Improving the Spatial Resolution of Snow Albedo Measurements in Mountainous Regions

Sproles, E. A.; Mullen, Andrew; Hendrikx, Jordy; Gatebe, Charles K.; Taylor, Suzi

doi:10.3390/hydrology7030041

Cited by 2 publications

(5 citation statements)

References 34 publications

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“…UAVs provide the means to resolve the spatial variability of snow albedo at high resolutions from the individual hillslope to watershed scale, and have the flexibility to be deployed at critical times during the onset of ablation at different times within a single day. These spatially distributed sets of near-surface albedo measurements can be used to supplement calibration and validation data from AWS (Burkhart et al, 2017;Ryan et al, 2017;Levy et al, 2018;Canisius et al, 2019;Sproles et al, 2020). UAV-based snow albedo measurements provide the means to bridge the scaling gap between in-situ snow albedo measurements and satellite observations (Sproles et al, 2020).…”

Section: Current Methods Of Measuring Snow Albedomentioning

confidence: 99%

“…These spatially distributed sets of near-surface albedo measurements can be used to supplement calibration and validation data from AWS (Burkhart et al, 2017;Ryan et al, 2017;Levy et al, 2018;Canisius et al, 2019;Sproles et al, 2020). UAV-based snow albedo measurements provide the means to bridge the scaling gap between in-situ snow albedo measurements and satellite observations (Sproles et al, 2020). However, the utility of these validation measurements depends heavily on processing strategy.…”

Section: Current Methods Of Measuring Snow Albedomentioning

confidence: 99%

“…The pyranometers were positioned to be as far as possible from the tripod legs and observer, but they still obstructed the sensor FOV. These effects on albedo are assumed to be minimal based upon the assessment of how the UAV landing gear and data logger impact albedo measurements (Sproles et al, 2020; Supplementary Figure S2). Observer and observer shadow were assumed to produce a consistent, albeit negligible difference in the measured reflected radiation as well.…”

Section: Field Methodsmentioning

confidence: 99%

“…To date, the UAV albedo research has differed in the way they compare UAV albedo to satellitederived albedo. Some have taken the point-to-pixel approach, without explicitly considering the FOV of the pyranometers onboard the UAV (Sproles et al, 2020). Others have specified an arbitrary restricted processing-FOV (PFOV) for the pyranometer data ranging from 60 ° (Ryan et al, 2017) to 90 ° (Canisius et al, 2019) to 162 ° (Levy et al, 2018) in order to project the sensing footprint onto the ground.…”

Section: Scaling Uncertaintiesmentioning

confidence: 99%

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An Operational Methodology for Validating Satellite-Based Snow Albedo Measurements Using a UAV

Mullen¹,

Sproles²,

Hendrikx³

et al. 2022

Front. Remote Sens.

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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.

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Section: Current Methods Of Measuring Snow Albedomentioning

confidence: 99%

Section: Current Methods Of Measuring Snow Albedomentioning

confidence: 99%

Section: Field Methodsmentioning

confidence: 99%

Section: Scaling Uncertaintiesmentioning

confidence: 99%

See 2 more Smart Citations

An Operational Methodology for Validating Satellite-Based Snow Albedo Measurements Using a UAV

Mullen¹,

Sproles²,

Hendrikx³

et al. 2022

Front. Remote Sens.

Self Cite

View full text Add to dashboard Cite

show abstract

“…There have been case studies, though, that have demonstrated mapping of broadband albedo in snow-covered environments. These studies have used paired pyranometers mounted in upward-and downward-looking configurations (Webster and Jonas, 2018;Sproles et al, 2020;Mullen et al, 2022) or a single downward-looking pyranometer on the UAV and a stationary upward-looking pyranometer within the flight area (Levy et al, 2018). These measurements return a single spectrally integrated value for incident and reflected radiation, from which broadband albedo can be calculated.…”

Section: Previous Workmentioning

confidence: 99%

UAV hyperspectral imaging for multiscale assessment of Landsat 9 snow grain size and albedo

Skiles¹,

Donahue²,

Hunsaker³

et al. 2023

Front. Remote Sens.

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Snow albedo, a measure of the amount of solar radiation that is reflected at the snow surface, plays a critical role in Earth’s climate and in regional hydrology because it is a primary driver of snowmelt timing. Satellite multi-spectral remote sensing provides a multi-decade record of land surface reflectance, from which snow albedo can be retrieved. However, this observational record is challenging to assess because discrete in situ observations are not well suited for validation of snow properties at the spatial resolution of satellites (tens to hundreds of meters). For example, snow grain size, a primary driver of snow albedo, can vary at the sub-meter scale driven by changes in aspect, elevation, and vegetation. Here, we present a new uncrewed aerial vehicle hyperspectral imaging (UAV-HSI) method for mapping snow surface properties at high resolution (20 cm). A Resonon near-infrared HSI was flown on a DJI Matrice 600 Pro over the meadow encompassing Swamp Angel Study Plot in Senator Beck Basin, Colorado. Using a radiative transfer forward modeling approach, effective snow grain size and albedo maps were produced from measured surface reflectance. Coincident ground observations were used for validation; relative to retrievals from a field spectrometer the mean grain size difference was 2 μm, with an RMSE of 12 μm, and the mean broadband albedo was within 1% of that measured near the center of the flight area. Even though the snow surface was visually homogenous, the maps showed spatial variability and coherent patterns in the freshly fallen snow. To demonstrate the potential for UAV-HSI to be used to improve validation of satellite retrievals, the high-resolution maps were used to assess grain size and albedo retrievals, and subpixel variability, across 17 Landsat 9 OLI pixels from a satellite overpass with similar conditions two days following the flight. Although Landsat 9 did not capture the same range of values and spatial variability as the UAV-HSI, on average the comparison showed good agreement, with a mean grain size difference of 9 μm and the same broadband albedo (86%).

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Autonomous Aerial Vehicles (AAVs) as a Tool for Improving the Spatial Resolution of Snow Albedo Measurements in Mountainous Regions

Cited by 2 publications

References 34 publications

An Operational Methodology for Validating Satellite-Based Snow Albedo Measurements Using a UAV

An Operational Methodology for Validating Satellite-Based Snow Albedo Measurements Using a UAV

UAV hyperspectral imaging for multiscale assessment of Landsat 9 snow grain size and albedo

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