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Brent Wilder Dissertation Defense

May 29 @ 3:00 pm - 5:00 pm MDT

Title: Modeling snow surface properties from LIDAR and imaging spectroscopy

Abstract: Seasonal snow surface plays an important role in altering terrestrial hydrology and global climate patterns. Snow reflects a majority of incoming shortwave radiation thereby reducing the net shortwave radiation received into snowpack throughout the season. This reflectivity of snow is difficult to measure accurately in mountain environments and at a large enough scale to be meaningful for water resource managers. The work presented herein aims to improve methodologies to measure snow reflectivity from both airborne and spaceborne platforms. This work is especially relevant with future global spaceborne imaging spectroscopy missions planned. In this dissertation, I used airborne lidar flown over the Boise Mountains, USA, to estimate optical grain size, a key feature that controls the reflectivity of snow. I compared these findings with in situ and coincident field spectroscopy measurements. In addition, I established an open-source algorithm, Global Optical Snow properties via High-speed Algorithm With K-means (GOSHAWK), that uses radiative transfer modeling to estimate snow surface and atmospheric state variables through inversions with spaceborne imaging spectroscopy measurements. I validated this algorithm for several sites across North America, using airborne lidar, field spectroscopy, and net-radiometers. Finally, I created a modified version of GOSHAWK that solved for the local incidence angles by exploiting the topographic information present in top of atmosphere radiance from spaceborne imaging spectroscopy. I highlighted this in a case study utilizing high elevation sites in Western United States. This dissertation advances our understanding of methods for mapping snow properties from lidar and imaging spectroscopy in mountain environments.

Advisor: Nancy Glenn

Committee Members: Christine Lee, HP Marshall, Jodi Brandt, Alicia Kinoshita

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