Wildfire emissions are challenging to measure and model, but simple and realistic estimates can benefit multiple disciplines. We evaluate the potential of MODIS (Moderate Resolution Imaging Spectroradiometer) data to address this objective. A total of 11,004 fire pixels detected over 92 days were clustered into 242 discrete fire events in a mountainous region of North America. Burned areas were estimated with spatial buffers around the MODIS detections, and all events were matched and compared with administrative fire records based on their location and duration. Linear regression between recorded and estimated burned areas showed excellent agreement (slope = 0.93 and R2 = 0.96). Aerosol emission rates were estimated for each MODIS detection using its fire radiative power measurement. Results were compared with estimates from the Canadian Fire Behaviour (CANFB) prediction system in Canada and the US Emissions Production Model (USEPM) for detections in the US. Median emission rates were similar for the MODIS and CANFB methods (600 and 579 g s-1 respectively) but not for the MODIS and USEPM methods (575 and 382 g s-1 respectively). The MODIS rates were much more variable in both comparisons. Linear regression on emission rates summed daily across the study area shows that the MODIS method is more consistent with CANFB (slope = 0.71, R2 = 0.71) than with USEPM (slope = 0.24, R2 = 0.68). We conclude that simple calculations based on remote sensing data can yield results that are comparable with those obtained with more complex methods.