A. W. Nolin
Terrestrial snow is a key component of both the climate system and the hydrologic cycle. We need to know the extent of snow cover as well as its physical properties (depth, albedo, grain size). Satellite remote sensing provides extensive spatial coverage and a regular repeat cycle for mapping snow covered regions. Airborne mapping delivers high spatial resolution and, in many cases, offers sensor types not yet available on satellite platforms. The following is a brief synopsis of several key mappalbe quantities, their significance, and the instrumentation and computational approaches used for retrieving these snow properties.
The field-of-view of a sensor often contains more than one land surface cover type causing "mixed pixels". For instance, alpine snow is typically spatially associated with rock and vegetation. Traditional snow mapping methods generate erroneous results by assuming that a pixel is either 100% snow-covered or completely non-snow-covered. Spectral mixture analysis is a new mehtod that performs a linear unmixing of the reflectance spectra of components in a pixel to derive the fraction of snow cover in a pixel. It has been used successfully with both Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Landsat Thematic Mapper (TM).
Surface albedo, the hemispheric reflectivity integrated over the solar spectrum, is a fundamental component for determining the radiation budget of the Earth-atmosphere system. Since snow is highly to moderately reflecting in the visible and near-infrared wavelengths, inaccurate estimates of albedo may translate into large errors in estimating snowpack energy balance. Snow is an anisotropic reflector and it does not reflect equally at all wavelengths. Remote sensing measurements at a few wavelengths and a single viewing geometry require a calibration to obtainthe spectrally-integrated albedo. A method for converting a single reflectance measurement into albedo has been developed using results from a radiative transfer model. This conversion scheme has been used with both airborne and spaceborne sensor data, in mountainous terrain and in the polar regions.
Snow grain size is an important indicator of the thermodynamic state of a snowpack. Snow albedo is also controlled primarily by grain size. Accurate estimates of snow surface grain size have been obtained by inverting results from a radiative transfer model. AVIRIS surface reflectance measurements have been used to map snow grain size even in rugged terrain. Soon-to-be launched spaceborne sensors hold great promise for making quantitative estimates of snow grain size.
Use of passive microwave data can be highly effective for mapping snow. A simple combination of the measured brightness temperature at the 19 GHz and 37 GHz frequencies provide an estimate of the snow water equivalent (SWE) for dry snow. However, when the snowpack contain liquid water from melting, it behaves like a blackbody making determination of SWE impossibe. Two methods are presented that use satellite-based passive microwave data for mapping dry snow SWE and for detecting wet snow. A second approach, based on the attenuation of gamma rays has been in operational use with an airborne instrument, producing accurate maps of SWE for wet and dry snow.
An airborne laser altimeter ahs been used to map surface elevation and surface roughness over the Greenland ice sheet. These measurements will be discussed and related to snow surface roughness and other measurements to be made by a planned spaceborne laser altimetry mission.
Contact Information:
Anne W. Nolin
National Snow and Ice Data Center (NSIDC)
Campus Box 449
University of Colorado
Boulder, Colorado 80309-0449
Telephone: (303) 492-6508
email: nolin@spectra.colorado.edu