Publication Date

12-2024

Date of Final Oral Examination (Defense)

5-29-2024

Type of Culminating Activity

Dissertation

Degree Title

Doctor of Philosophy in Geosciences

Department

Geosciences

Supervisory Committee Chair

Nancy F. Glenn, Ph.D.

Supervisory Committee Member

Christine M. Lee, Ph.D.

Supervisory Committee Member

Hans-Peter Marshall, Ph.D.

Supervisory Committee Member

Jodi Brandt, Ph.D.

Supervisory Committee Member

Alicia M. Kinoshita, Ph.D.

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 property is commonly referred to as snow albedo and impacts water cycles and air temperatures by modulating the timing and magnitude of melt. 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 and climate scientists. 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 modified the GOSHAWK to also solve for terrain during the optimal estimation by exploiting the topographic information present in top-of-atmosphere radiance from spaceborne imaging spectroscopy. I highlighted this in a case study, comparing same-day airborne imaging spectroscopy data. This dissertation advances our understanding of methods for mapping snow surface properties to be better prepared for future missions.

DOI

https://doi.org/10.18122/td.2355.boisestate

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