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| Created: | Nov 03, 2025 at 4:25 p.m. (UTC) | |
| Last updated: | Nov 03, 2025 at 5:35 p.m. (UTC) | |
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| Sharing Status: | Public |
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Abstract
The fine scale distribution of snow is important for avalanche forecasting and biological refugia. Here, we test how historical context provided by 3 m snow cover observations derived from PlanetScope commercial satellite imagery could be used to downscale fractional snow covered area (fSCA) observed by the MODerate resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and the Harmonized Landsat and Sentinel-2 (HLS) product. We evaluate this over Colorado and California montane meadows using PlanetScope-informed 1) probabilistic snow cover maps, and 2) random forest machine learning models. We then compare versus a downscaling approach that relies only on terrain characteristics, eliminating the need for PlanetScope observations. Downscaling snow cover using random forest models performed best on average, largely because these models corrected annually consistent snow cover biases between PlanetScope and coarser resolution fSCA observations. The approach used to downscale 3 m snow cover was the dominant driver of accuracies, followed closely by the accuracy of the fSCA estimate that snow cover was downscaled from. The pattern of snow cover was also observed well by HLS. Thus, 3 m snow cover downscaled from HLS using only terrain indices was often similar or better than snow cover downscaled from MODIS and VIIRS using context from PlanetScope. This demonstrates how a limited historical record of commercial satellite observations can be used to estimate the fine-scale pattern of snow cover, but also when publicly accessible remote sensing retrievals and information about the terrain may obviate the need for commercial observations.
Subject Keywords
Coverage
Spatial
Temporal
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This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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