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Abstract-An approach to the remote sensing of land surface temperature is developed using the methods of Bayesian inference. The starting point is the maximum entropy estimate for the posterior distribution of radiance in multiple bands. In order to convert this quantity to an estimator for surface temperature and emissivity with Bayes' theorem, it is necessary to obtain the joint prior probability for surface temperature and emissivity, given available prior knowledge. The requirement that any pair of distinct observers be able to relate their descriptions of radiance under arbitrary Lorentz transformations uniquely determines the prior probability. Perhaps surprisingly, surface temperature acts as a scale parameter, while emissivity acts as a location parameter, giving the prior probabilityGiven this result, it is a simple matter to construct estimators for surface temperature and emissivity. A Monte Carlo simulation of land surface temperature retrieval in selected MODIS bands is presented as an example of the utility of the approach.

In this article, we present a review of the current state of metabolic engineering in Catharanthus roseus. A significant amount of research has contributed to characterization of several individual steps in the biosynthetic pathway of medicinally valuable alkaloids. However, knowledge of the regulation of these pathways is still sparse. Using hairy root cultures, we studied the responses of alkaloid metabolism to environmental stimulation such as light and elicitation. Through precursor feeding studies, the putative rate‐limiting steps of the terpenoid pathway in hairy root cultures also have been examined. Relating this knowledge to specific events at the molecular level, and the cloning of corresponding genes are the next key steps in metabolic engineering of the C. roseus alkaloids. © 1998 John Wiley & Sons, Inc. Biotechnol Bioeng 58:333–338, 1998.

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