A Continuous Cryosphere Index for Snow and Ice Reflectance.
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| Title: | A Continuous Cryosphere Index for Snow and Ice Reflectance. |
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| Authors: | Small, Christopher1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1505. 31p. |
| Subjects: | Cryosphere, Spectral reflectance, United States. National Aeronautics & Space Administration, Spectrometers, Remote sensing |
| Abstract: | Highlights: What are the main findings? NASA's EMIT imaging spectrometer is used to characterize the spectral feature space of snow and ice over a range of compositions and a diversity of cryospheric environments. The spectral feature space of snow and ice is continuous with distinct spectral endmembers corresponding to specular, dry and wet snow, white and blue ice. What are the implications of the main findings? A linear spectral mixture model for snow and ice alone is unstable, but a standardized SVD + snow model is shown to be stable and has low RMS misfit across a variety of environments. An optimized Continuous Cryosphere Index (CCI) representing the snow–ice continuum can distinguish dry and wet snow and white and blue ice consistently across all 56 EMIT granules used, as well as on a sub-decameter resolution AVIRIS line spanning the snow–ice gradient on the Greenland Ice Sheet. Because of high visible and near-infrared (VNIR) reflectance, and deep shortwave infrared (SWIR) absorption, snow and ice are unique among terrestrial land cover. As such, both are well-suited to mapping and monitoring using optical remote sensing. However, to date, almost all studies of snow and ice spectroscopy have been limited to single or small numbers of specific cryospheric environments. These studies serve a diversity of objectives, but together also suggest the importance of the global continuum of snow and ice composition and spectroscopy. The continuum of snow and ice composition gives rise to the characteristics that allow different types of snow and ice to be distinguished optically. Particularly with imaging spectrometers. Characterization of this continuum of reflectance can facilitate development of physical models to quantify snow and ice composition and abundance, particularly in the presence of other types of land cover. In this study, a collection of ~140,000,000 visible through SWIR (VSWIR) reflectance spectra, collected by NASA's EMIT imaging spectrometer from 56 diverse cryospheric environments, is used to characterize the continuum of snow and ice reflectance. This continuum is characterized using linear dimensionality reduction to quantify the dimensionality and topology of the spectral feature space of snow and ice. The resulting spectral feature space is effectively two-dimensional with a planar spectral feature continuum bounded by dry and wet snow, ice and dark targets (e.g., shadow, water). Because of the near collinearity of snow and ice endmember reflectances, linear spectral mixture models based only on these endmembers are ill-posed and unstable to inversion. However, in landscapes where sufficiently homogeneous seasonal snow is present with other land cover types, the standardized spectroscopic mixture model based on the Substrate, Vegetation and Dark (SVD) continuum can be extended with an instance-specific snow endmember (SVD + snow) to yield plausible areal fraction estimates with small misfits to observed spectra. More generally, the snow–ice-dark continuum can also be represented accurately with an optimal normalized difference index exploiting compositionally distinct differential absorptions at ~650 and ~1230 nm to distinguish dry from wet snow from white and blue ice. This optimized index, referred to as the Continuous Cryosphere Index (CCI), minimizes BRDF effects of topographic slope and aspect relative to illumination, while avoiding the saturation that causes the Normalized Difference Snow Index (NDSI) to conflate wet snow with white and blue ice reflectance. In addition to imaging spectrometers like EMIT, operational sensors like MODIS, VIIRS and WorldView-3 have spectral bands near 650 nm and 1230 nm, so they could also be used for CCI mapping. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? NASA's EMIT imaging spectrometer is used to characterize the spectral feature space of snow and ice over a range of compositions and a diversity of cryospheric environments. The spectral feature space of snow and ice is continuous with distinct spectral endmembers corresponding to specular, dry and wet snow, white and blue ice. What are the implications of the main findings? A linear spectral mixture model for snow and ice alone is unstable, but a standardized SVD + snow model is shown to be stable and has low RMS misfit across a variety of environments. An optimized Continuous Cryosphere Index (CCI) representing the snow–ice continuum can distinguish dry and wet snow and white and blue ice consistently across all 56 EMIT granules used, as well as on a sub-decameter resolution AVIRIS line spanning the snow–ice gradient on the Greenland Ice Sheet. Because of high visible and near-infrared (VNIR) reflectance, and deep shortwave infrared (SWIR) absorption, snow and ice are unique among terrestrial land cover. As such, both are well-suited to mapping and monitoring using optical remote sensing. However, to date, almost all studies of snow and ice spectroscopy have been limited to single or small numbers of specific cryospheric environments. These studies serve a diversity of objectives, but together also suggest the importance of the global continuum of snow and ice composition and spectroscopy. The continuum of snow and ice composition gives rise to the characteristics that allow different types of snow and ice to be distinguished optically. Particularly with imaging spectrometers. Characterization of this continuum of reflectance can facilitate development of physical models to quantify snow and ice composition and abundance, particularly in the presence of other types of land cover. In this study, a collection of ~140,000,000 visible through SWIR (VSWIR) reflectance spectra, collected by NASA's EMIT imaging spectrometer from 56 diverse cryospheric environments, is used to characterize the continuum of snow and ice reflectance. This continuum is characterized using linear dimensionality reduction to quantify the dimensionality and topology of the spectral feature space of snow and ice. The resulting spectral feature space is effectively two-dimensional with a planar spectral feature continuum bounded by dry and wet snow, ice and dark targets (e.g., shadow, water). Because of the near collinearity of snow and ice endmember reflectances, linear spectral mixture models based only on these endmembers are ill-posed and unstable to inversion. However, in landscapes where sufficiently homogeneous seasonal snow is present with other land cover types, the standardized spectroscopic mixture model based on the Substrate, Vegetation and Dark (SVD) continuum can be extended with an instance-specific snow endmember (SVD + snow) to yield plausible areal fraction estimates with small misfits to observed spectra. More generally, the snow–ice-dark continuum can also be represented accurately with an optimal normalized difference index exploiting compositionally distinct differential absorptions at ~650 and ~1230 nm to distinguish dry from wet snow from white and blue ice. This optimized index, referred to as the Continuous Cryosphere Index (CCI), minimizes BRDF effects of topographic slope and aspect relative to illumination, while avoiding the saturation that causes the Normalized Difference Snow Index (NDSI) to conflate wet snow with white and blue ice reflectance. In addition to imaging spectrometers like EMIT, operational sensors like MODIS, VIIRS and WorldView-3 have spectral bands near 650 nm and 1230 nm, so they could also be used for CCI mapping. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101505 |
