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Proceedings Paper

Snow crystal and land cover effects on the scattering of passive microwave radiation for algorithm development
Author(s): James L. Foster; J. S. Barton; Alfred T. C. Chang; Dorothy K. Hall
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Paper Abstract

In developing and tuning passive microwave algorithms, which are used to estimate snow extent, snow water equivalent and snow depth, much of the effort has been directed towards better accounting for the effects of snow crystal size on the microwave response, and relatively little effort has been given to the role that crystal shape or orientation plays in this regard. Modeling using a discrete dipole scattering models has shown that the assumption used in radiative transfer approaches, where snow crystals are modeled as randomly oriented spheres, is adequate to account for the transfer of microwave energy emanating from the ground and passing through a snowpack. With this in mind and by having some knowledge of the size of the particles in the snowpack as well as the snowpack density, snow depth algorithms can be designed for specific basins to assess the snow water equivalent of the basin and to thus, estimate snowmelt runoff and seasonal streamflow. Work performed on an ongoing GCIP/GEWEX experiment for watersheds in the upper Mid West and the northern Great Plains (the Roseau river basin in Minnesota/Manitoba, and the Black river basin in Wisconsin) has shown that for each of these basins, a strong conelation exists between snow depth derived from SSMI passive microwave data and snow depth measured at meteorological stations and determined from airborne gamma overflights. For instance, for the Roseau basin in mid March (Julian day 75), during the period from 1992-1998, the coefficient of determination (R2) is a very strong 0.8975. Thus, ninety percent of the mid March snow depth variation in this basin, during these years, can be explained by the SSMI snow algorithm. Streamfiow has also been correlated with maximum seasonal snow depth for these two basins as well (Figure 3). Using only SSMI-derived snow depth as the predictor or dependent variable, the R2 value for the Roseau basin was 0.715 between the basin-wide snow depth on March 15 and ensuing streamfiow for the month of April. When there is a high degree of assurance that the satellite-derived estimates are reliable (the algorithms produce results which reflect the streamfiow — hydrographs), they can then be used to generate input to hydrologic models.

Paper Details

Date Published: 23 January 2001
PDF: 7 pages
Proc. SPIE 4171, Remote Sensing for Agriculture, Ecosystems, and Hydrology II, (23 January 2001); doi: 10.1117/12.413927
Show Author Affiliations
James L. Foster, NASA Goddard Space Flight Ctr. (United States)
J. S. Barton, General Sciences Corp. (United States)
Alfred T. C. Chang, NASA Goddard Space Flight Ctr. (United States)
Dorothy K. Hall, NASA Goddard Space Flight Ctr. (United States)

Published in SPIE Proceedings Vol. 4171:
Remote Sensing for Agriculture, Ecosystems, and Hydrology II
Manfred Owe; Guido D'Urso; Eugenio Zilioli, Editor(s)

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