Utilizing Multiple Datasets for Snow Cover Mapping

Andrew Tait¹, Dorothy Hall², Jim Foster², Richard Armstrong³

1 Universities Space Research Association, 7501 Forbes Blvd., Suite 206, Lanham, MD 20706.
2 NASA / Goddard Space Flight Center, Code 974, Greenbelt, MD 20771.
3 National Snow and Ice Data Center, University of Colorado, Campus Box 449, Boulder, CO 80309.

ABSTRACT

Snow cover maps generated from surface data are based on direct measurements, however they are prone to interpolation errors where climate stations are sparsely distributed. Snow cover is clearly discernable using optical data because of the high albedo of snow, yet the surface is often obscured by cloud cover. Passive microwave (PM) data is unaffected by clouds, however the snow-cover signature is significantly affected by melt water and the microwaves may be transparent to thin snow (< 3cm). Both optical and microwave sensors have problems discerning snow beneath forest canopies.

This paper describes a method that combines these three snow cover products to produce a Multiple-Dataset Snow Cover Product (MDSCP). Comparisons with current snow-cover products show that the MDSCP draws together the advantages of each of its component products while minimizing their potential errors.

Improved estimates of the snow-covered area are derived through the addition of two snow-cover classes ("thin or patchy" and "high elevation" snow cover) and from the analysis of the climate station data within each class. The compatibility of this method for use with Moderate Resolution Imaging Spectroradiometer (MODIS) data, which will be available in 1999, and with Advanced Microwave Scanning Radiometer (AMSR) data, available in 2000, is also discussed. With the assimilation of these data, the resolution of the MSDCP would be improved both spatially and temporally and the analysis would become completely automated.