Microwave remote sensing offers advantages that are particularly well suited to the observation of high-latitude regions. Microwave sensors are far less susceptible to interference by clouds than are optical or infrared sensors, and they are not dependent upon solar illumination, permitting observations at night and throughout the polar winter. Microwave radiometry is particularly sensitive to temperature and moisture distributions in vegetation canopies and in the underlying soil. These quantities are, in fact, the gross physical parameters of greatest significance in land-atmosphere boundary processes.
We are developing land surface process/radiobrightness (LSP/R) models for arctic tundra and prairie grass regions that are linked to satellite observations. Such models provide land-atmosphere boundary conditions and feedback for atmospheric circulation models. An LSP/R model is a biophysical representation of the linkage between the observed microwave emission and conditions deeper within the canopy and the soil. By using observations made over a period of time to constrain the models, sub-surface temperature and moisture conditions may be determinable for these areas of simple vegetation. Several modeling issues, including a scaling strategy, will be highlighted.
By linking the LSP/R model to satellite observations, the performance of the model over a region such as the North Slope of Alaska or the northern US Great Plains can be monitored and estimates of surface temperature and moisture content with a spatial resolution of ~40 km -- the average resolution of the Special Sensor Microwave/Imager (SSM/I) satellite instruments -- should be possible.
Field data from our Radiobrightness Energy Balance Experiments are supporting model development. Data were collected for one full annual freeze-thaw cycle at a tundra site on the North Slope and for summer/fall/winter at a prairie site in South Dakota. Example data will be presented and the development of the LSP/R models and snow-covered/snow-free and frozen/thawed remote classification schemes will be described.
Edward J. Kim and Anthony W. England University of Michigan Department of Electrical Engineering and Computer Science and Department of Atmospheric, Oceanic, and Space Sciences 1301 Beal Ave., Ann Arbor, MI 48109-2122, USA Tel: 313-763-8162; Fax: 313-747-2106 Email: ejk@eecs.umich.edu, england@eecs.umich.edu