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Snow is a ubiquitous natural material that plays an important role in Earth’s climatological system and energy resource budget. Its insular and reflective properties are key factors contributing to the radiation budget of the cryosphere. Due to its prevalence at extreme latitudes, the monitoring of snowpack quantities is often performed via remote observation. These data are acquired using either satellite readings or by fixing instruments to the underside of aircraft. When acquiring data remotely, it is important to account for the angular configuration of the source illumination and the location of the instrument relative to the surface since reflection is affected by the geometry of the observation. In other words, the bidirectional reflectance distribution function (BRDF) depends on the angle of incidence of the solar illumination and the angle of observation in addition to the wavelength of the incident light. It has been recognized that the granular properties of a snowpack markedly influence its BRDF. Unfortunately, works examining the effects of snow grain characteristics, such as size and facetness, on BRDF outputs are still scarce. Moreover, measured BRDF values from field studies presented in the literature are limited to specific target samples. This further hinders a more comprehensive understanding of the effects of changes in snow characterization parameters on the bidirectional reflectance of snowpack. The measured datasets often do not provide a detailed characterization of the target samples either, which also reduces their usefulness for elucidating these effects. To address these limitations and enhance the current understanding about the sensitivity of snow BRDF to variations in grain characteristics, we have conducted controlled experiments employing a first-principles in silico experimental framework supported by measured data. Our findings unveil the qualitative effects that snow granular properties have on bidirectional reflectance of snowpack, and highlight the importance of accounting for snow granular properties in remote sensing applications. In addition, our in silico experiments provide a high-fidelity assessment of snow BRDF with respect to key wavelengths particularly relevant for remote sensing applications. More broadly, our investigation demonstrates how remote observations of snow-covered terrains can be significantly improved by the correct incorporation of snow grain characteristics into the bidirectional reflectance models used to assess snowpack properties.
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