High frequency passive microwave radiometry over a snow-covered surface inAlaska

Millimeter-wave Imaging Radiometer (MIR) data (ranging in frequency from 89 to 325 GHz) were collected from NASA ER-2 flights over Alaska in April 199 5. This study determines whether these data can be used to identify clouds, vegetation type, and snow cover. The procedure used is as follows: (1) det ermine whether a purely MIR-based cloud detection scheme is possible over a snow-covered surface, (2) analyze the influence of changing vegetation typ e on the brightness temperatures, and (3) compare completely snow-covered s cenes with partially snow-covered and snow-free regions for cloudy and clea r-sky periods to determine whether varying snow conditions affect the MIR d ata. Results show that surface features can be identified using the less opaque channels at 89, 150, and 220 GHz, although the 150-GHz (2.0-mm wavelength) and 220-GHz (1.4-mm) channels are more sensitive to atmospheric phenomena c ompared with 89 GHz (3.4 mm), because the atmospheric contribution to the u pwelling radiation is larger for shorter wavelengths. Statistical examinati on of the MIR data shows that the determination of cloudy pixels over a sno w-covered surface is not possible using a simple brightness temperature thr eshold technique. Furthermore, it is concluded that, while no statistical d iscrimination between specific vegetation classes can be made, significance is obtained when the vegetation is grouped into two classes only, for exam ple, vegetated and barren. It is also shown that the stale of the snow cove r (complete coverage, melting, or patchy) has a distinct effect on these re sults.