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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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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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Content
README.txt
Domain files and snow cover estimates from PlanetScope and downscaled from MODIS, VIIRS, and HLS. Data here corresponds with the data from Pflug et al. (202X): Downscaling 3m resolution snow cover from MODIS, VIIRS, and HLS using commercial satellite imagery and terrain information. Authors: Justin M. Pflug, Kehan Yang, Nicoleta Cristea, Emma T. Boudreau, Carrie M. Vuyovich, and Sujay V. Kumar Zipped files contain netcdf data, each of which correspond to seven domains: Dana Meadows (DAN, California), Devils Postpile (DPO, California), Gin Flat (GIN, California), Ostrander Lake (STR, California), Joe Wright (551, Colorado), Schofield Pass (737, Colorado), and Willow Creek Pass (869, Colorado). For more information on the domains and the data included here, please see the Pflug et al. (202X) study referenced above. Each zipped directory, corresponding to each of the domains listed above, is organized as follows:.tar.gz: zipped directory corresponding to each domain - _Planet.nc: PlanetScope-derived snow cover between 2019 and 2023 processed by Pflug et al. (2024) * This file also contains the x/y coordinates and dates for all following netcdf files - _probability_withheld .npy: pixelwise probability calculated using all years 2019-2023, except - _SVI_ m_ .nc: snow variability index calculated using terrain at various s and weighting factors - downscaled_SCA: - Random_ .nc: snow cover downscaled using random pixel assignment using the given - C17_ m_ _ .nc: snow cover downscaled using the terrain-based using the given * and correspond with the SVI map used (see SVI bullet above) * 'C17' since motivated by Cristea et al. (2017) - R21_ .nc: snow cover downscaled using the probabilistic approach using the given * 'R21' since motivated by Revuelto et al. (2021) - M24_ .nc: snow cover downscaled using the probabilistic approach using the given * 'M24' since motivated by Mahanthege et al. (2024) Note: Possible : 03, 15, 30 (all in meters) Possible : 0.25, 0.50, 0.75 Possible : MODIS, VIIRS, HLS Cristea, N.C., Breckheimer, I., Raleigh, M.S., HilleRisLambers, J., Lundquist, J.D., 2017. An evaluation of terrain-based downscaling of fractional snow covered area data sets based on LiDAR-derived snow data and orthoimagery. Water Resour. Res. 53, 6802–6820. https://doi.org/10.1002/2017WR020799 Mahanthege, S., Kleiber, W., Rittger, K., Rajagopalan, B., Brodzik, M.J., Bair, E., 2024. A Spatially-Distributed Machine Learning Approach for Fractional Snow Covered Area Estimation. Water Resour. Res. 60, e2023WR036162. https://doi.org/10.1029/2023WR036162 Pflug, J.M., Yang, K., Cristea, N., Boudreau, E.T., Vuyovich, C.M., Kumar, S.V., 2024. Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent. Water Resour. Res. 60, e2024WR037983. https://doi.org/10.1029/2024WR037983 Revuelto, J., Alonso-González, E., Gascoin, S., Rodríguez-López, G., López-Moreno, J.I., 2021. Spatial Downscaling of MODIS Snow Cover Observations Using Sentinel-2 Snow Products. Remote Sens. 13, 4513. https://doi.org/10.3390/rs13224513
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