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High‐accuracy laboratory measurements of the temperature dependence of the opacity from gaseous sulfur dioxide (SO2) in a carbon dioxide (CO2) atmosphere at temperatures from 290 to 505 K and at pressures from 1 to 4 atm have been conducted at frequencies of 2.25 GHz (13.3 cm), 8.5 GHz (3.5 cm), and 21.7 GHz (1.4 cm). Based on these absorptivity measurements, a Ben‐Reuven (BR) line shape model has been developed that provides a more accurate characterization of the microwave absorption of gaseous SO2 in the Venus atmosphere as compared with other formalisms. The developed BR formalism is incorporated into a radiative transfer model. The resulting microwave emission spectrum of Venus is then used to set an upper limit on the disk‐averaged abundance of gaseous SO2 below the main cloud layer. It is found that gaseous SO2 has an upper limit of 150 ppm, which compares well with previous spacecraft in situ measurements and Earth‐based radio astronomical observations.

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Over the past several years, hyperspectral sensor technology has evolved to the point where real-time processing for operational applications is achievable. Algorithms supporting such sensors must be fully automated and robust. Our approach, for target detection applications, is to select signatures from a target reflectance library database and project them to the at-sensor and collection-specific radiance domain using the weather forecast or radiosonde data. This enables platform-based detection immediately following data acquisition without the need for further atmospheric compensation. One advantage of this method for reflective hyperspectral sensors is the ability to predict the radiance signatures of targets under multiple illumination conditions. A three-phase approach is implemented, where the library generation and data acquisition phases provide the necessary input for the automated detection phase. In addition to employing the target detector itself, this final phase includes a series of automated filters, adaptive thresholding, and confidence assignments to extract the optimal information from the detection scores for each spectral class. Our prototype software is applied to 50 reflective hyperspectral datacubes to measure detection performance over a range of targets, backgrounds, and environmental conditions.