Abstract

Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.

Highlights

  • Sensed snow-covered area (SCA) provides crucial information for scientists across a variety of disciplines

  • Our analysis indicated that as spatial resolution became coarser, the difference between study area snow cover fraction computed from binary SCA and fractional SCA, and the potential for error in binary

  • Our definition of mixed pixels will likely result in identification of more mixed pixels than would be mapped using Landsat and MODIS, since fSCA mapping algorithms such as MODSCAG often have difficulty mapping snow cover at fractions < approximately 0.15, while Landsat-based algorithms will be prone to overestimation of snow cover when saturation of the visible bands occurs, resulting in fewer mixed pixels when snow cover fractions are slightly below 1

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Summary

Introduction

Sensed snow-covered area (SCA) provides crucial information for scientists across a variety of disciplines. The presence of an insulating snow cover has a large effect on ground surface temperatures and permafrost [7,8] as well as drainage characteristics [9], and SCA time series data can provide important information for scientists monitoring and modeling permafrost and soil conditions. Snow cover can have a large impact on plant species distribution [10,11], plant phenology [12], and animal movement patterns [13,14,15], and SCA data can provide valuable information for ecologists and wildlife biologists. Fractional SCA mapping extracts more information than binary SCA mapping from the same source dataset and, for the MODIS instrument, 500 m fSCA from the MODIS Snow-Covered

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