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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. 

This paper investigates the ability of a relatively low cost, commercially available uncrewed aerial vehicle (UAV), the DJI Mavic 3 Multispectral, to perform cryospheric research. The performance of this UAV, where applicable, is compared to a similar but higher cost system, the DJI Matrice 350, equipped with a Micasense RedEdge-MX Multispectral dual-camera system. The Mavic 3 Multispectral was tested at three field sites: the Lemon Creek Glacier, Juneau Icefield, AK; the Easton Glacier, Mt. Baker, WA; and Bagley Basin, Mt. Baker, WA. This UAV proved capable of mapping the spatial distribution of red snow algae on the surface of the Lemon Creek Glacier using both spectral indices and a random forest supervised classification method. The UAV was able to assess the timing of snowmelt and changes in suncup morphology on snow-covered areas within the Bagley Basin. Finally, the UAV was able to classify glacier surface features using a random forest algorithm with an overall accuracy of 68%. The major advantages of this UAV are its low weight, which allows it to be easily transported into the field, its low cost compared to other alternatives, and its ease of use. One limitation would be the omission of a blue multispectral band, which would have allowed it to more easily classify glacial ice and snow features.