Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths

Thaler, Evan Austin; Crumley, Ryan Landon; Bennett, Katrina Eleanor

doi:10.1016/j.rse.2022.113403

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths

Journal Article · Fri Dec 09 00:00:00 EST 2022 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/j.rse.2022.113403· OSTI ID:1957919

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Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

We report the extent and duration of snow cover, a critical component of the hydrologic cycle and the global climate system, is expected to shift dramatically under climate change. Therefore, developing high-resolution assessments of snow cover change is crucial for estimating the impact of changing snow cover on watershed and ecosystems processes in cold regions. Remote sensing tools provide a powerful method for mapping snow-covered area (SCA) across a landscape. The most common method for estimating SCA utilizes the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI can effectively estimate catchment- to regional-scale SCA, but it cannot be used to assess fine-scale SCA because of current limitations on the spatial resolution of satellite-derived SWIR measurements. Here, we map SCA using a threshold of blue wavelengths and high-resolution satellite imagery. The thresholding method, which we call the Blue Snow Threshold algorithm (BST), has previously been used with digital camera imagery. We refine and automate the algorithm for use with cloud-free high-resolution satellite imagery and find that the BST can be used to assess fine-scale SCA. For validation, we compared BST-derived estimates of SCA to a) airborne lidar surveys, b) Landsat fractional SCA, and c) snow disappearance dates from Snow Telemetry (SNOTEL) stations. When compared to airborne lidar surveys of SCA, the BST predicted SCA had a range of F-scores between 0.81 and 0.94 in four study areas in California and Colorado. We also found general agreement between SCA and snow disappearance at multiple SNOTEL sites across the western United States. Given the relatively recent availability of high-resolution satellite imagery with spectral measurements in the visible wavelengths but lacking in SWIR, the BST offers a reliable and easy-to-apply tool for examining fine-scale snow-related processes.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC). Office of Biological & Environmental Research (BER)

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1957919

Alternate ID(s):: OSTI ID: 1905169

Report Number(s):: LA-UR-22-24095

Journal Information:: Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Vol. 285; ISSN 0034-4257

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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