ALBERT

Description: Remotely sensed infrared radiance emitted by a surface is a function both of its kinetic temperature and its spectral emissivity. Consequently, assumptions are usually made about the emissivity of earth surface materials to allow their temperatures to be determined, or vice versa. To increase the accuracy of these assumptions, the directional hemispherical spectral reflectance of a wide range of natural earth surface materials has been measured and is summarized here. These include igneous, metamorphic, and sedimentary rocks, desert varnish, soils, vegetation, water, and ice. Kirchhoff's Law can be used to predict directional spectral emissivity from these data.

Description: By the end of this century the Earth Observing System (EOS) will provide worldwide, thermal infrared, multispectral images of the Earth, presenting geologists with a new kind of remote sensing data for interpretation. Thus it has become essential to understand the spectral emittance behavior of terrestrial surface materials. Perhaps the most fundamental question to be answereed is the extent to which such materials follow Kirchhoff's law (epsilon = 1 -R) under laboratory and field conditions, especially when a sample displays a thermal gradient. We present the first rigorous quantitative comparison of directional and hemispherical reflectance and directional emittance of rock and soil samples in the laboratory, with thermal gradients induced by heating them from below and allowing them to radiate to a colder background. The results show that only an extemeley low density sample composed of fine particles sifted into a 'fairy castle' structure displays a thermal gradient steep enough within the infrared skin depth to cause significant (6%) departure from Kirchhoff's law. There is no detectable effect on the more normal terrestrial samples, such as soils and rocks measured in the laboratory, even when semitransparent coatings are involved. Thus both emittance and reflectance measurements can be used to calculate sample emissivity for most terrestrial surface materieals. However, the effect on Kirchhoffian behavior of different field environments, which may induce a steeper thermal gradient in particulate samples, has yet to be determined, and some low-density surface materials like newly fallen snow, frost, and efflorescent salts on playas have yet to be measured in emittance.

Description: Because much of Earth's surface is covered by frost, snow, and ice, the spectral emissivities of these materials are a significant input to radiation balance calculations in global atmospheric circulation and climate change models. Until now, however, spectral emissivities of frost and snow have been calculated from the optical constants of ice. We have measured directional hemispherical reflectance spectra of frost, snow, and ice from which emissivities can be predicted using Kirchhoff's law (e = 1-R). These measured spectra show that contrary to conclusions about the emissivity of snow drawn from previously calculated spectra, snow emissivity departs significantly from blackbody behavior in the 8-14 micrometer region of the spectrum; snow emissivity decreases with both increasing particle size and increasing density due to packing or grain welding; while snow emissivity increases due to the presence of meltwater.

Description: Particle size of soils plays a significant role in erosion potential and other mechanical properties. Most soils are dominated by the residual mineral quartz, which displays prominent reststrahlen bands in the 8-14 microns atmospheric window. The Earth Observing System will likely provide world-wide multispectral imagery in the 8-14 microns region via the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument. The ratio of ASTER bands 10/14 can be used to estimate particle size in soils, if other ASTER bands are used to minimize the confusion factors provided by soil moisture, vegetation cover, soil organic content, and the presence of abundant minerals other than quartz. Use of band ratios minimizes the effects of poor surface temperature estimates, but maximizes the need for high signal-to-noise data.