Elzbieta H. Czyzowska 1, 2 , William J.D. van Leeuwen 3, 4, Stuart E. Marsh 3, 4 , Katherine K. Hrschboeck 2, Wit T. Wisniewski 5
1 Arid Lands Resource Sciences, 2 Laboratory of Tree Ring Research, 3 Office of Arid Lands Studies, 4 School of Geography and Regional Development, 5 Department of Chemistry and Biochemistry,
University of Arizona
elzbieta@email.arizona.edu
There is an undisputed need to increase accuracy of snow cover estimation in regions of complex terrain, especially in areas dependent on winter snow accumulation for a substantial portion of their water supply, such as the Western United States. Presently, the most pertinent needs in snow cover extent (SCE) monitoring are: (1) to deliver detailed fractional snow cover (FSC) products to improve SCE monitoring, which perform in the temporal/spatial environmental heterogeneity of forested and/or alpine terrains; (2) to provide accurate measurements of FSC at the watershed scale for use in snow water equivalent (SWE) estimation for regional water management; (3) to provide detailed distributions of FSC in mountainous regions to investigate temporal/spatial distribution of SCE/SWE in relations to recent climate changes; (4) to use FSC products as input for climate models at multiple scales; and (5) to estimate SCE and SWE for use in ecological studies (e.g., vegetation cover, water stress, primary production, fire, insect outbreaks, and pulses in tree demography).
The main aim of the presented research is to develop Landsat Fractional Snow Cover (LandsatFSC) as a measure of temporal/spatial distribution of alpine SCE, using a fusion methodology between remotely sensed data in Landsat TM/ETM+ and Ikonos images utilized at their highest respective spatial resolutions. Artificial Neural Networks (ANNs) have been used to capture the multi-scaled information structure of the data by means of the ANN training process, followed by the ANN extracting FSC from all available information in the Landsat images. The LandsatFSC algorithm has been validated (RMSE ~ 0.09; mean error ~ 0.001–0.01 FSC) in watersheds characterized by diverse environmental factors such as: terrain, slope, exposition, vegetation cover, and wide-ranging snow cover conditions. The results are presented for the research areas located in the San Juan Mountains, Colorado, and Black Hills, South Dakota.