Capturing the dynamic fluctuations in structure and adsorbate coverage on catalytic surfaces under operating conditions is of critical importance to the identification of active sites and subsequent design of materials for targeted bond activation but remains challenging to achieve both experimentally and computationally. Field ion microscopy achieves such in situ reaction monitoring on single catalytic grains via applied electric fields which ionize atmospheric gases at the surface, providing images of the working catalyst surface. However, such images remain difficult to deconvolute without simplifying assumptions. Here, we use density functional theory to probe the mechanism for field ionization of a series of imaging gases (O<
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2<
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, N<
sub>
2<
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, and NO) over oxygen, nitrogen, sulfur, and carbon covered Pd(331). From this dataset of field ionization systems, we determined that the primary factors affecting field ionization are the relative energy levels for the imaging gas?s occupied states and the surface?s unoccupied states, both of which are tunable through choice of imaging gas and the presence of adsorbates, respectively. Using such quantitative descriptors of the electronic accessibility of both the imaging gas and adsorbate covered surface, we develop a predictive tool for the rapid assessment of any system?s field ionization potential. Altogether, this work provides fundamental insight into the field ionization mechanism and presents an easy-to-use predictive tool that can assist with both the analysis of existing field ion microscopy images and the design of future experiments.