Spectral degree of linear polarization and neutral points of polarization in snow and ice surfaces

The high-resolution imagery recorded by systems such as the multi-spectral scanners (MSSs) of the Landsat satellites has revolutionized the study of all types of surface in the polar regions. Visible and near-infra-red imagery has found a wide range of glaciological uses. There is, however, a lack of comparability within and between MSS data which may be a contributary factor to some current problems in interpretation of remotely sensed glaciological data.With the expected continuity of MSS coverage for the forseeable future, it is highly desirable to extend use of the data beyond the basic mapping and feature identification which has made it such a valuable resource. One of the most obvious developments is to investigate characteristics of the reflecting surfaces and to achieve absolute identification of snow and ice surfaces. Although conversion of digital MSS grey tones to radiances enables direct comparison with other sources, automatic identification requires detailed and extensive knowledge of the spectral and reflecting characteristics of surfaces which are to be monitored. This is often best achieved through ground-based observation.In order to provide a base line against which corrected radiances from Landsat MSS data can be compared, a spectrally gated photometer has been used to measure albedo at MSS wave bands in a wide range of conditions. The surfaces monitored in several parts of Norway include sea ice, lake ice, snow, firn and glacier ice, permafrost, and reference surfaces. A range of supporting measurements (including grain-size, surface irregularity, density, level, and free-water content) allows accurate characterization of each surface. This enables identification of spectral-response patterns for each surface category and hence the classification of their reflectances as recorded by the MSS. Examples are given of the application of such classifications to imagery of the polar regions.

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Test Methods for Tire Performance Testing on Snow and Ice Surfaces

10.1520/f1572-21 ◽

2021 ◽

Author(s):

Keyword(s):

Performance Testing ◽

Test Methods ◽

Ice Surfaces ◽

Snow And Ice

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Spectral Response Patterns of Snow and Ice Surfaces for the Landsat Multispectral Scanner (Abstract)

Annals of Glaciology ◽

10.1017/s0260305500000902 ◽

1987 ◽

Vol 9 ◽

pp. 249

Author(s):

M. Kristensen ◽

N.F. McIntyre

Keyword(s):

Spectral Response ◽

Surface Irregularity ◽

Base Line ◽

Density Level ◽

Polar Regions ◽

Response Patterns ◽

Absolute Identification ◽

Wide Range ◽

Ice Surfaces ◽

Snow And Ice

The high-resolution imagery recorded by systems such as the multi-spectral scanners (MSSs) of the Landsat satellites has revolutionized the study of all types of surface in the polar regions. Visible and near-infra-red imagery has found a wide range of glaciological uses. There is, however, a lack of comparability within and between MSS data which may be a contributary factor to some current problems in interpretation of remotely sensed glaciological data. With the expected continuity of MSS coverage for the forseeable future, it is highly desirable to extend use of the data beyond the basic mapping and feature identification which has made it such a valuable resource. One of the most obvious developments is to investigate characteristics of the reflecting surfaces and to achieve absolute identification of snow and ice surfaces. Although conversion of digital MSS grey tones to radiances enables direct comparison with other sources, automatic identification requires detailed and extensive knowledge of the spectral and reflecting characteristics of surfaces which are to be monitored. This is often best achieved through ground-based observation. In order to provide a base line against which corrected radiances from Landsat MSS data can be compared, a spectrally gated photometer has been used to measure albedo at MSS wave bands in a wide range of conditions. The surfaces monitored in several parts of Norway include sea ice, lake ice, snow, firn and glacier ice, permafrost, and reference surfaces. A range of supporting measurements (including grain-size, surface irregularity, density, level, and free-water content) allows accurate characterization of each surface. This enables identification of spectral-response patterns for each surface category and hence the classification of their reflectances as recorded by the MSS. Examples are given of the application of such classifications to imagery of the polar regions.

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Spectral degree of linear polarization of light from healthy skin and melanoma

Optics Express ◽

10.1364/oe.23.013605 ◽

2015 ◽

Vol 23 (10) ◽

pp. 13605 ◽

Cited By ~ 5

Author(s):

Romain Ceolato ◽

Muriel Golzio ◽

Cécile Riou ◽

Xavier Orlik ◽

Nicolas Riviere

Keyword(s):

Linear Polarization ◽

Healthy Skin ◽

Spectral Degree ◽

Polarization Of Light

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SNICAR-ADv4: A physically based radiative transfer model to represent the spectral albedo of glacier ice

10.5194/tc-2021-272 ◽

2021 ◽

Author(s):

Chloe A. Whicker ◽

Mark G. Flanner ◽

Cheng Dang ◽

Charles S. Zender ◽

Joseph M. Cook ◽

...

Keyword(s):

Radiative Transfer ◽

Radiative Transfer Model ◽

Transfer Model ◽

Air Bubbles ◽

Snow Layer ◽

Wide Range ◽

Glacier Ice ◽

Ice Surfaces ◽

Smooth Transitions ◽

Snow And Ice

Abstract. Accurate modeling of cryospheric surface albedo is essential for our understanding of climate change as snow and ice surfaces regulate the global radiative budget and sea-level through their albedo and mass balance. Although significant progress has been made using physical principles to represent the dynamic albedo of snow, models of glacier ice albedo tend to be heavily parameterized and not explicitly connected with physical properties that govern albedo, such as the number and size of air bubbles, specific surface area (SSA), presence of abiotic and biotic light absorbing constituents (LAC), and characteristics of any overlying snow. Here, we introduce SNICAR-ADv4, an extension of the multi-layer two-stream delta-Eddington radiative transfer model with the adding-doubling solver that has been previously applied to represent snow and sea-ice spectral albedo. SNICAR-ADv4 treats spectrally resolved Fresnel reflectance and transmittance between overlying snow and higher-density glacier ice, scattering by air bubbles of varying sizes, and numerous types of LAC. SNICAR-ADv4 simulates a wide range of clean snow and ice broadband albedos (BBA), ranging from 0.88 for (30 μm) fine-grain snow to 0.03 for bare and bubble free ice under direct light. Our results indicate that representing ice with a density of 650 kg m−3 as snow with no refractive Fresnel layer, as done previously, generally overestimates the BBA by an average of 0.058. However, because most naturally occurring ice surfaces are roughened "white ice", we recommend modeling a thin snow layer over bare ice simulations. We find optimal agreement with measurements by representing cryospheric media with densities less than 650 kg m−3 as snow, and larger density media as bubbly ice with a Fresnel layer. SNICAR-ADv4 also simulates the non-linear albedo impacts from LACs with changing ice SSA, with peak impact per unit mass of LAC near SSAs of 0.1–0.01 m2 kg−1. For bare, bubble-free ice, LAC actually increase the albedo. SNICAR-ADv4 represents smooth transitions between snow, firn, and ice surfaces and accurately reproduces measured spectral albedos of a variety of glacier surfaces. This work paves the way for adapting SNICAR-ADv4 to be used in land ice model components of Earth System Models.

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