Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent
Abstract Snow water equivalent (SWE) distribution at fine spatial scales (≤10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that...
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR037983 |
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| author | Justin M. Pflug Kehan Yang Nicoleta Cristea Emma T. Boudreau Carrie M. Vuyovich Sujay V. Kumar |
| author_facet | Justin M. Pflug Kehan Yang Nicoleta Cristea Emma T. Boudreau Carrie M. Vuyovich Sujay V. Kumar |
| author_sort | Justin M. Pflug |
| collection | DOAJ |
| description | Abstract Snow water equivalent (SWE) distribution at fine spatial scales (≤10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope's constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5‐year period (2019–2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave‐based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring SWE. Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine‐scale and high‐frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack. |
| format | Article |
| id | doaj-art-53adb6d80b434428b95c3398178b6e4b |
| institution | Kabale University |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
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| series | Water Resources Research |
| spelling | doaj-art-53adb6d80b434428b95c3398178b6e4b2025-08-23T13:05:51ZengWileyWater Resources Research0043-13971944-79732024-11-016011n/an/a10.1029/2024WR037983Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water EquivalentJustin M. Pflug0Kehan Yang1Nicoleta Cristea2Emma T. Boudreau3Carrie M. Vuyovich4Sujay V. Kumar5Hydrological Sciences Laboratory NASA Goddard Space Flight Center Greenbelt MD USAM3 Works LLC Boise ID USADepartment of Civil and Environmental Engineering University of Washington Seattle WA USADepartment of Civil and Environmental Engineering University of Washington Seattle WA USAHydrological Sciences Laboratory NASA Goddard Space Flight Center Greenbelt MD USAHydrological Sciences Laboratory NASA Goddard Space Flight Center Greenbelt MD USAAbstract Snow water equivalent (SWE) distribution at fine spatial scales (≤10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope's constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5‐year period (2019–2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave‐based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring SWE. Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine‐scale and high‐frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.https://doi.org/10.1029/2024WR037983snowremote sensingsnowmeltmodelingdistribution |
| spellingShingle | Justin M. Pflug Kehan Yang Nicoleta Cristea Emma T. Boudreau Carrie M. Vuyovich Sujay V. Kumar Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent Water Resources Research snow remote sensing snowmelt modeling distribution |
| title | Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent |
| title_full | Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent |
| title_fullStr | Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent |
| title_full_unstemmed | Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent |
| title_short | Using Commercial Satellite Imagery to Reconstruct 3 m and Daily Spring Snow Water Equivalent |
| title_sort | using commercial satellite imagery to reconstruct 3 m and daily spring snow water equivalent |
| topic | snow remote sensing snowmelt modeling distribution |
| url | https://doi.org/10.1029/2024WR037983 |
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