Abstract
Spectral mixture analysis has a history in mapping snow, especially where mixed pixels prevail. Using multiple spectral bands rather than band ratios or band indices, retrievals of snow properties that affect its albedo lead to more accurate estimates than widely used age-based models of albedo evolution. Nevertheless, there is substantial room for improvement. We present the Snow Property Inversion from Remote Sensing (SPIReS) approach, offering the following improvements: 1) Solutions for grain size and concentrations of light absorbing particles are computed simultaneously; 2) Only snow and snow-free endmembers are employed; 3) Cloud-masking and smoothing are integrated; 4) Similar spectra are grouped together and interpolants are used to reduce computation time. The source codes are available in an open repository. Computation is fast enough that users can process imagery on demand. Validation of retrievals from Landsat 8 operational land imager (OLI) and moderate-resolution imaging spectroradiometer (MODIS) against WorldView-2/3 and the Airborne Snow Observatory shows accurate detection of snow and estimates of fractional snow cover. Validation of albedo shows low errors using terrain-corrected in situ measurements. We conclude by discussing the applicability of this approach to any airborne or spaceborne multispectral sensor and options to further improve retrievals.
Highlights
S affects regional and global climate, provides water resources over significant parts of the world, and sustains diverse ecosystems
5) When absorbers are within the instantaneous field-of-view, ascertaining whether they are in the snow, next to the snow, or beneath a shallow snowpack is difficult with multispectral imagers such as moderate-resolution imaging spectroradiometer (MODIS) or Landsat 8 operational land imager (OLI)
For areas where the GLIMS database entries are created from Landsat imagery, this approach does not resolve at 10-m resolution, but for many areas of the Western U.S, glacier outlines are available at 10 m [60]
Summary
S affects regional and global climate, provides water resources over significant parts of the world, and sustains diverse ecosystems. A longstanding approach applied to multispectral instruments, the Normalized Difference Snow Index (NDSI) that Dozier [4] introduced, is NDSI = Rλ(VIS) − Rλ(SWIR). The NDSI was the first spectral index designed to identify snow from space and is still widely used to classify snow cover, for example, the moderate-resolution imaging spectroradiometer (MODIS) algorithm in NASA’s Earth Observing System [6]. For MODIS, a regression-based approach was developed to convert NDSI to fractional (i.e., subpixel) snow-covered area ( fsca) [8], but this technique showed high root mean square error (RMSE) values [9] so the MODIS data system no longer provides a fractional snow product. Spectral mixture analysis [11] has been successfully used to map snow cover with multispectral sensors such as the Landsat Thematic Mapper [12] or MODIS [13] and spectroscopic.
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