Satellite remote sensing can provide automated, near real-time locations and characteristics of active fires. Diagnostic and prognostic applications, particularly in climate and air quality modeling, benefit from the use of satellite fire products where it is important to identify emission sources to model total emissions and emission transport. Every fire product has strengths and limitations; geostationary satellites provide high temporal resolution that is necessary to identify diurnal fire signatures and low earth orbit satellites provide fine spatial resolution that enables the detection of smaller fires. Data fusion provides the opportunity to present more information than one satellite product can provide by itself by taking advantage of the strengths of each fire product. One of the largest problems the modeling community faces is the real-time assimilation of active fire products. To produce an accurate multi-satellite merged fire product it is necessary to discriminate between fires detected by multiple satellites and fires detected by only one satellite. Some differences are expected due to orbit, instrument, and algorithm differences. In light of this disagreement between fire products, confusion exists in the user community. To improve user confidence, this study employs new methods to characterize and understand the differences between the GOES WF_ABBA and MODIS fire products for improved applications in model assimilation.