Please note: This PhD seminar will take place online.
Petri Varsa, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Gladimir V. G. Baranoski
Snow is a ubiquitous natural material that plays an important role in human welfare as well as the Earth's climatological system and energy resource budget. Due to its prevalence at extreme latitudes, monitoring of snowpack quantities is often performed via remote observation, either from satellite observations or from aircraft. The angular pattern of sunlight that is reflected from the surface is affected by the geometry of the observation. I.e., the bidirectional reflectance distribution function (BRDF) values are, in part, determined by the angle of incidence of the solar illumination, and the angle of observation. It is also known that the BRDF is a function of the wavelength of light. In addition, it has been reported in the literature, that granular properties of the snowpack also markedly influence the BRDF. Unfortunately, little work has been presented in the literature which investigates the effect of snow characterization properties on bidirectional reflectance. Measured BRDF responses existent in the literature are limited to the sample of study, thus preventing detailed measurement of the effect of changes in characterization on bidirectional reflectance. Many studies do not even provide a detailed characterization of the sample. To address these limitations and enhance the current understanding of the effect snow grain characteristics have on the BRDF of snow, we propose the use of an in silico experimental framework that is supported by measured data. Our findings demonstrate the effect that snow granular properties have on bidirectional reflectance of snowpack. Thus, we show the importance of accounting for snow granular properties in remote sensing applications. In addition, our in silico experiments allow for the assessment of bidirectional reflectance for two key wavelengths of illumination.