Dissertation/Thesis Abstract

Comparing SSM/I snow depth estimates to in-situ and interpolated multi-source measurements
by Chin-Murray, Susan Amee, M.S., University of Maryland, College Park, 2011, 131; 1501222
Abstract (Summary)

Spaceborne remote sensing data from the Special Sensor Microwave Imager (SSM/I) have been used for several decades to estimate snow depth over large regions. The SSM/I snow depth accuracy is not well quantified in non-uniform terrain. In this study, SSM/I snow depth estimates for the Columbia River Basin and surroundings in the Western USA and Canada are compared with in-situ manual snow-course measurements and interpolated snow water equivalent from the National Operational Hydrologic Remote Sensing Center. Snow depth is estimated for 25-km pixels from SSM/I brightness temperatures with the widely used Chang algorithm, adjusted for canopy cover. Interactive Data Language and ESRI ArcGIS are used to generate maps and time-series graphs, and to analyze the agreement between SSM/I snow depth and the other data sources. Measures of agreement are cross-tabulated with quantitative landscape descriptors, including: mean pixel elevation, elevation standard deviation (a measure of terrain complexity), and evergreen canopy cover.

Indexing (document details)
Advisor: Brubaker, Kaye L.
Commitee: Kasischke, Eric S., Pinker, Rachel T.
School: University of Maryland, College Park
Department: Marine-Estuarine-Environmental Sciences
School Location: United States -- Maryland
Source: MAI 50/02M, Masters Abstracts International
Subjects: Hydrologic sciences, Water Resource Management, Remote sensing
Keywords: Columbia Basin, Complex terrain, Snow depth, Snow water equivalent
Publication Number: 1501222
ISBN: 978-1-124-96710-3
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy