This paper describes a different approach to sensitivity studies for environmental, including atmospheric, physics models. The sensitivity studies were performed on a multispectral environmental data and scene generation capability. The capability includes environmental physics models that are used to generate data and scenes for simulation of environmental materials, features, and conditions, such as trees, clouds, soils, and snow. These studies were performed because it is difficult to obtain input data for many of the environmental variables. The problem to solve is to determine which of the 100 or so input variables, used by the generation capability, are the most important. These sensitivity studies focused on the generation capabilities needed to predict and evaluate the performance of sensor systems operating in the infrared portions of the electromagnetic spectrum. The sensitivity study approach described uses a screening design. Screening designs are analytical techniques that use a fraction of all of the combinations of the potential input variables and conditions to determine which are the most important. Specifically a 20-run Plackett-Burman screening design was used to study the sensitivity of eight data and scene generation capability computed response variables to 11 selected input variables. This is a two-level design, meaning that the range of conditions is represented by two different values for each of the 11 selected variables. The eight response variables used were maximum, minimum, range, and mode of the model-generated temperature and radiance values. The result is that six of the 11 input variables (soil moisture, solar loading, roughness length, relative humidity, surface albedo, and surface emissivity) had a statistically significant effect on the response variables.