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Snowpack Persistence Day of Year Mean (1993 - 2022)

Creators: Ian BreckheimerORCID
Year: 2023
License: See source for details
Location: Upper East River / Gunnison Basin, Colorado
Temporal extent: 1992-10-01 to 2022-09-30
Bounding box: 38.432°N to 39.100°N, -107.254°W to -106.282°W
Publisher: RMBL
Tags: remote sensing, snow, SDP, climate, microclimate, landsat, release4, Climatology Meteorology Atmosphere, Environment, Hydrology & Watersheds, Snow & Ice, Remote Sensing & Imagery, Data Science & Modeling, RMBL & Gothic, Gunnison Basin

Description

This dataset represents an estimate of the mean day of year (i.e., "Julian Day") of the persistence of the seasonal snowpack from 1993 - 2022.&nbsp; Specifically these are estimates of the first day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993.&nbsp; These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.&nbsp; 2021).&nbsp; The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds).<br /><br />A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series.<br /><br />This data represents the mean of annual estimates from 1993 - 2022.<br /><br />References: <br /><br />Gao, X., Gray, J. M., &amp; Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484 <br /><br />&nbsp;<br />&nbsp;In addition to the download links on this page, you can access this dataset and metadata using the <a href="https://github.com/rmbl-sdp/rSDP/">rSDP R Package</a>:<br />&nbsp;<br />&nbsp;#devtools::install_github('rmbl-sdp/rSDP')<br />&nbsp;library(rSDP)<br />&nbsp;dataset &lt;- sdp_get_raster('R4D061')<br />&nbsp;metadata &lt;- sdp_get_metadata('R4D061')<br />&nbsp;<br />&nbsp;For more information about rSDP, visit the <a href="https://rmbl-sdp.github.io/rSDP/"> package homepage</a>.

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