Image data collected near the ground, in the boundary layer, or from low altitude planes must contend with the detrimental effects of atmospheric turbulence on the image quality. So it is useful to predict operating regimes (wavelength, height of target, height of detector, total path distance, day vs. night viewing, etc.) where atmospheric turbulence is expected to play a significant role in image degradation. In these regimes, image enhancement techniques such as speckle processing, deconvolution and Wiener filtering methods can be utilized to recover near instrument-limited resolution in degraded images. We conducted a literature survey of various boundary layer and lower troposphere models for the structure coefficient of the index of refraction (Cn2). Using these models, we constructed a spreadsheet tool to estimate the Fried parameter (r0) for different scenarios, including slant and horizontal path trajectories. We also created a tool for scenarios where the height along the path crudely accounted for the topology of the path. This would be of particular interest in mountain-based viewing platforms surveying ground targets. The tools that we developed utilized Visual Basic© programming in an Excel© spreadsheet environment for accessibility and ease of use. In this paper, we will discuss the Cn2 profile models used, describe the tools developed and compare the results obtained for the Fried parameter with those estimated from experimental data.