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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 discretein situobservations 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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This content will become publicly available on April 22, 2026
How commercial SmallSats are revolutionizing the remote detection and mapping of snow algae
Snow algae play an important role in reducing the surface albedo of snow surfaces worldwide and contributing to enhanced melt through a bio-albedo feedback loop. Traditional remote sensing approaches have relied on government-operated satellite platforms, such as Landsat and Sentinel-2, which provide freely available data but are limited by their coarse spatial resolution. Recent advancements in commercial satellite technologies, particularly SmallSats, offer higher spatial and temporal resolutions, enabling more precise detection and mapping of snow algae. This study evaluates the capabilities of commercial satellites, including SkySat, PlanetScope, BlackSky, and WorldView, for snow algae mapping on Mt. Baker, Washington, United States. Leveraging data from NASA’s Commercial SmallSat Data Acquisition (CSDA) program, we apply spectral indices to classify snow algae. Our findings highlight the advantages and limitations of commercial SmallSats compared to traditional government-operated satellites, emphasizing their potential for improving snow algae mapping in ecological and climate studies. The results of this study provide insights into the role of high spatial resolution commercial satellite imagery in advancing our understanding of snow algae distribution and its broader implications for climate feedback mechanisms.
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- Award ID(s):
- 2046240
- PAR ID:
- 10651822
- Publisher / Repository:
- Frontiers in Remote Sensing
- Date Published:
- Journal Name:
- Frontiers in Remote Sensing
- Volume:
- 6
- ISSN:
- 2673-6187
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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