SPLITSnow: A spectral light transport model for snow

Varsa, Petri; Baranoski, Gladimir V. G.; Kimmel, Bradley W.

doi:10.1016/j.rse.2020.112272

Cited by 7 publications

(22 citation statements)

References 75 publications

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“…This aspect can be explained by the fact that greater angles of incidence result in more light being reflected on a snow sample surface, and less light being transmitted through its volume. 2 It is important to mention that these behaviors are consistent with qualitative trends identified by in situ investigations on the transmission of light through snowpacks. 3 For more information regarding related observations reported in the literature, please refer to "Snow Spectral Responses.…”

Section: Snow Characterization Variabilitysupporting

confidence: 80%

“…The use of the SPLITSnow model for this purpose was determined not only by its specificity and availability, but also by its predictive capabilities. 2 These provide the empirically-supported foundation for this work. For more information regarding these capabilities, please refer to "Snow Spectral Responses.…”

Section: Transmittance Databasementioning

confidence: 91%

“…Recently, we developed a first-principles light transport model for snow, known as SPLITSnow, whose predictive capabilities have been extensively evaluated in the visible and infrared spectral domains. 2 This model was designed to support snow-related multidisciplinary investigations and applications in fields as diverse as remote sensing, environmental sciences, and computer graphics, just to name a few. It can be employed to compute high-fidelity radiometric data (e.g., spectral reflectance and transmittance) for snow under different illumination and environmental conditions.…”

mentioning

confidence: 99%

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Rendering the Bluish Appearance of Snow: When Light Transmission Matters

Varsa,

Baranoski

2024

IEEE Comput. Grap. Appl.

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Section: Snow Characterization Variabilitysupporting

confidence: 80%

Section: Transmittance Databasementioning

confidence: 91%

mentioning

confidence: 99%

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Rendering the Bluish Appearance of Snow: When Light Transmission Matters

Varsa,

Baranoski

2024

IEEE Comput. Grap. Appl.

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“…According to Malinka et al (2016) such an optical thickness produces in the green range an albedo of the order of τ τ +4 ≈ 0.98, while the measured quantities reliably show a value of about 0.9. This means that the snow samples contain some light-absorbing particles (Warren, 1982). These particles can be incorporated into the model.…”

Section: Optical Modellingmentioning

confidence: 99%

“…3.4 -to an illumination angle of 30 • to minimize this effect. For wavelengths larger than 800 nm, the effect of the limited thickness of the sample and of the presence of light-absorbing impurities (Warren, 1982) is also limited.…”

Section: Optical Measurement Accuracy: Reciprocity Principlementioning

confidence: 99%

Experimental and model-based investigation of the links between snow bidirectional reflectance and snow microstructure

et al. 2021

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Abstract. Snow stands out from materials at the Earth’s surface owing to its unique optical properties. Snow optical properties are sensitive to the snow microstructure, triggering potent climate feedbacks. The impacts of snow microstructure on its optical properties such as reflectance are, to date, only partially understood. However, precise modelling of snow reflectance, particularly bidirectional reflectance, are required in many problems, e.g. to correctly process satellite data over snow-covered areas. This study presents a dataset that combines bidirectional reflectance measurements over 500–2500 nm and the X-ray tomography of the snow microstructure for three snow samples of two different morphological types. The dataset is used to evaluate the stereological approach from Malinka (2014) that relates snow optical properties to the chord length distribution in the snow microstructure. The mean chord length and specific surface area (SSA) retrieved with this approach from the albedo spectrum and those measured by the X-ray tomography are in excellent agreement. The analysis of the 3D images has shown that the random chords of the ice phase obey the gamma distribution with the shape parameter m taking the value approximately equal to or a little greater than 2. For weak and intermediate absorption (high and medium albedo), the simulated bidirectional reflectances reproduce the measured ones accurately but tend to slightly overestimate the anisotropy of the radiation. For such absorptions the use of the exponential law for the ice chord length distribution instead of the one measured with the X-ray tomography does not affect the simulated reflectance. In contrast, under high absorption (albedo of a few percent), snow microstructure and especially facet orientation at the surface play a significant role in the reflectance, particularly at oblique viewing and incidence.

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