Infrared Measurement Of Free-Water Content And Grain Size Of Snow

Hyvärinen, Timo; Lammasniemi, Jorma

doi:10.1117/12.7974077

Cited by 20 publications

(16 citation statements)

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“…The results agreed well with those obtained by space probes for icy satellites of the solar system. The reflectance and transmittance of snow have been further studied, e.g., by Kimball and Hand (1930), Gerdel (1948), Dunkle and Bevans (1955), and Hyvarinen and Lammasniemi (1987). These studies were mostly carried out to estimate the melting of the snow cover.…”

Section: Introductionmentioning

confidence: 73%

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Observing Land from Space: Science, Customers and Technology

Verstraete¹,

Menenti²,

Petoniemi³

2000

Advances in Global Change Research

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Section: Introductionmentioning

confidence: 73%

“…We took photographs of the samples, which are accurate enough to allow a rough estimation of the properties above. The size estimation of the snow crystals is in good agreement with the study by Hyvarinen and Lammasniemi (1987).…”

Section: Discussionmentioning

confidence: 99%

Observing Land from Space: Science, Customers and Technology

Verstraete¹,

Menenti²,

Petoniemi³

2000

Advances in Global Change Research

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“…This is particularly of interest when studying the immense Antarctic ice sheet where ground data are sparse. For example, the reflectance of snow in the near-infrared range is known to be dependent on snow grain-size (Dozier, 1984, 1989; Hyvarinen and Lammasniemi, 1987). The objective of the research presented here is to use this dependence to estimate snow grain-size from satellite imagery.…”

Section: Introductionmentioning

confidence: 99%

Snow grain-size determination from Landsat imagery over Terre Adélie, Antarctica

Bourdelles

Fily

1993

Ann. Glaciol.

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A Landsat Thematic Mapper scene over Terre Adélie, Antarctica, is used to estimate the snow grain-size. New calibration coefficients are computed for the TM scene, using sea-water reflectance. Topographic effects are corrected with the visible band TM2. Atmospheric effects are taken into account. The snow theoretical reflectance is calculated with the Wiscombe and Warren (1980) model. Estimated snow grain-sizes are similar to those given in the literature.

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“…A technique for simultaneous estimation of free-water content and grain size using near-infrared reflectance at three optimally selected wavelengths in the 1000-1400 nm range has been developed recently [8], and this method has been used by us in our inversion algorithm. Using the reflectance values at three wavelengths, two reflectance ratios are derived: one which is primarily dependent on average grain size and relatively independent of free-water content, and another which shows high sensitivity to free-water content.…”

Section: A Near-infrared Reflectancementioning

confidence: 99%

“…Aged snow, formed after a few days of temperature gradient metamorphism, has grain sizes in the range 0.2-1.5 mm and densities in the range 0.25-0.45 gm/cc. Repeated melt-freeze cycles cause the snow particles to increase in size to between 0.5-3 mm, and densify to between 0.2-0.5 gm/cc [8]. The free-water content of snow can vary over a wide range, and can attain values as high as 30%, with a transition region around 15% where the particle shape asymmetry factor changes dramatically [9].…”

Section: Introductionmentioning

confidence: 99%

Estimation of surface snow properties using combined millimeter-wave backscatter and near-infrared reflectance measurements

Narayanan

Jackson

1997

Int J Infrared Milli Waves

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Knowledge of surficial snow properties such as grain size, surface roughness, and free-water content provides clues to the metamorphic state of snow on the ground, which in turn yields information on weathering processes and climatic activity. Remote sensing techniques using combined concurrent measurements of near-infrared passive reflectance and millimeter-wave radar backscatter show promise in estimating the above snow parameters. Near-infrared reflectance is strongly dependent on snow grain size and free-water content, while millimeter-wave backscatter is primarily dependent on free-water content and, to some extent, on the surface roughness. A neural-network based inversion algorithm has been developed that optimally combines near-infrared and millimeter-wave measurements for accurate estimation of the relevant snow properties. The algorithm uses reflectances at wavelengths of 1160 nm, 1260 nm and 1360 nm, as well as co-polarized and cross-polarized backscatter at a frequency of 95 GHz. The inversion algorithm has been tested using simulated data, and is seen to perform well under noise-free conditions. Under noise-added conditions, a signal-to-noise ratio of 32 dB or greater ensures acceptable errors in snow parameter estimation.

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Infrared Measurement Of Free-Water Content And Grain Size Of Snow

Cited by 20 publications

References 0 publications

Observing Land from Space: Science, Customers and Technology

Observing Land from Space: Science, Customers and Technology

Snow grain-size determination from Landsat imagery over Terre Adélie, Antarctica

Estimation of surface snow properties using combined millimeter-wave backscatter and near-infrared reflectance measurements

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