Asking the pikas politely…
This dataset represents an estimate of the day of year (i.e. , "Julian Day") of the persistence of the seasonal snowpack. Specifically these are estimates of the first day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993. 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 heirarchical Bayesian model (Gao et al. 2021). 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 />References: <br /><br />Gao, X., Gray, J. M., & 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 /> <br /> 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 /> <br /> #devtools::install_github('rmbl-sdp/rSDP')<br /> library(rSDP)<br /> dataset <- sdp_get_raster('R4D001')<br /> metadata <- sdp_get_metadata('R4D001')<br /> <br /> For more information about rSDP, visit the <a href="https://rmbl-sdp.github.io/rSDP/"> package homepage</a>.
Knowledge graph centered on Snowpack Persistence Day of Year Yearly Timeseries with 3 nodes and 3 connections. Top connected: Upper East River Beaver Pond, Multi-model PlanetScope snow cover classification.
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