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| Created: | Nov 03, 2025 at 4:25 p.m. (UTC) | |
| Last updated: | Aug 19, 2026 at 6:21 p.m. (UTC) | |
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Abstract
This repository includes terrain data and downscaled snow cover maps corresponding to Pflug et al., Comparing 3 m resolution snow cover downscaled from MODIS, VIIRS, and HLS using commercial satellite imagery and terrain information. This study focuses on downscaling 3 m snow cover estimates from coarser-resolution (30 - 500 m) estimates of fractional snow covered-area (fSCA) using 1) relationships with the terrain, and 2) information about 3 m snow cover from commercial PlanetScope snow cover estimates. Tests are based on three previously-published approaches based on a terrain-based snow variability index, snow cover probabilities informed by PlanetScope observations, and a random-forest machine learning model trained using PlanetScope observations. Results showed that snow cover downscaled using random forest models had the capability of correcting annually persistent snow cover biases, resulting in the best overall performance. The downscaling approach used to estimate 3 m snow cover was the most significant driver of performance, followed by the accuracy of the satellite sensor that fSCA came from. The data in this repository includes the data necessary to reproduce the figures and results from Pflug et al. Users are encouraged to contact the repository owner (Justin M Pflug) with inquiries.
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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