Utilizing multiple datasets for snow-cover mapping

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 discernible using satellite-obtained optical data because of the high albedo of snow, yet the surface is often obscured by cloud cover. Satellite-obtained passive microwave data, compared with optical data, is relatively unaffected by clouds; however, the snow-cover signature is significantly affected by melting snow and the microwaves may be transparent to thin snow (<3 cm). Both optical and microwave sensors have problems discerning snow beneath forest canopies. This paper describes a method that combines ground and satellite-derived optical and passive microwave data to produce a multiple-dataset snow-cover product. Comparisons with current snow-cover products show that the multiple-dataset product draws together the advantages of each of its component products while minimizing the 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 data, which will be available in 1999, and with Advanced Microwave Scanning Radiometer data, available in 2000, is also discussed. With the assimilation of these data, the resolution of the multiple-dataset product would be improved both spatially and temporally and the analysis would become completely automated. (C)Elsevier Science, Inc., 2000.