Snow Mapping Complexities

Snowstorms can blanket a region and significant ablation can occur as quickly as one day. Thus, a monthly or bi-weekly product may be unsatisfactory for many modeling purposes, particularly during the fall and spring transition seasons. Satellite mapping of snow cover offers regular, repeat coverage at spatial and temporal scales useful for climate simulations. However, while snow cover extent is a relatively straightforward parameter to retrieve using space-borne measurements, the detection of melt and estimation of snow albedo are more difficult. An optimal product would be one that concurrently maps snow-covered area, snow albedo, and snow water equivalent at 1-5 day intervals, all at the same spatial resolution. Current operational snow mapping is performed using optical sensors such as the Advanced Very High Resolution Radiometer (AVHRR) and passive microwave sensors such as the Defense Mapping Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I). AvHRR records data at two spatial resolutions (1.1 km and 4 km) and is restricted to measurement during sunlit times. Therefore, winter observations at high latitudes are limited. Also, clouds interfere with AVHRR observations, a common problem during the snow season. Although albedo data from this sensor can be used for multi-temporal relative comparisons, the accuracy is insufficient for absolute snow albedo measurements (Stroeve et al., 1997).

Passive microwave estimates are based on the scattering effect of ice particles on microwave radiation emitted from the ground below. As snow accumulates, its scattering of microwave energy reduces the brightness temperature values recorded by satellite radiometers. Empirical algorithms have been developed to estimate snow extent and snow water equivalent from passive microwave sensors (Goodison, 1989; Chang et al., 1990; Chang and Tsang, 1992; Basist et al., 1997). Unlike optical region measurements, passive microwave observations are uninfluenced by darkness and non-precipitating clouds. Ice particles in clouds are transparent to the passive microwave frequencies being used to map snow. However, liquid water droplets in clouds will affect the algorithm results and warm storms may be mapped as snow. SSM/I is useful for mapping snow extent and is also effective at mapping snow water equivalent in regions without dense forests. However, in melting snow the presence of liquid water changes snow emis-sivity and masks the snow signal (Grody, 1991; Grody and Basist, 1996). A second disadvantage of SSM/I is its coarse spatial resolution (25 x 25 km) which makes it difficult to combine with albedo estimates from optical data. In addition, complex systems such as the northern boreal forest, where snow remains on trees and beneath them, present further snow detection complications. Basist et al. (1996) found that an AVHRR-derived operational snow cover product performed better than an experimental SSM/I-derived product when mapping snow extent under dense vegetation, whereas the SSM/I algorithm worked better over mountainous regions, under clouds, and during times when the snowpack is rapidly changing. Tait and Armstrong (1996) found that underestimation of snow depth from passive microwave data occurred in regions of boreal forest. Dense vegetation increases the brightness temperature, giving the false impression of less snow (Schweiger et al., 1987). To date, accurate mapping of snow cover in boreal forest regions remains problematic for passive microwave systems.

A new optical remote sensing instrument, the Moderate Resolution Imaging Spectroradiometer (MODIS), was launched in December 1999 and is mapping global snow covered area as one of its standard products (Hall et al., 1998). The MODIS Snowmap product provides daily and 8-day composite snow cover maps at 500-m spatial resolution. Snowmap exploits the reflectance contrast for snow between the visible and shortwave infrared wavelengths. The algorithm uses the Normalized Difference Snow Index (NDSI) defined as:

where, VIS and NIR are the pixel reflectances in selected visible and near-infrared channels. The algorithm assumes a threshold, above which a pixel is determined to contain more than 50% snow cover. Currently, the NDSI threshold is set to 04. The MODIS Snowmap product represents an improvement over existing snow products because the MODIS-derived snow maps are produced at higher spatial and temporal resolution than the NOAA weekly snow cover charts (Matson, 1986) and the 1-km resolution regional snow products over North America from the National Operational Hydrolo-gic Remote Sensing Center (Carroll, 1990). The Snowmap product offers improved snow detection accuracy by incorporating vegetation cover information over forested areas (Klein et al., 1998). Expected maximum error for the Northern Hemisphere is «7.5% with largest errors in the boreal forest regions, «9-10% (Hall et al., 1998). In addition, the MODIS 8-day snow product will contain statistics for snow cover duration and persistence for each 500-m grid cell.

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