Cambodia is facing widespread deforestation due to agriculture, logging, land grabbing, and infrastructure. The implementation of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects has become a key strategy to protect at-risk forests using the sale of verified emission reductions as financing; generated by reducing forest loss against counterfactual baseline scenarios. We test a series of ex-post baseline assessment methodologies on three Cambodian REDD+ projects using two geospatial datasets (one global and one locally calibrated for maximum accuracy); integrating results to assess the reasonable accuracy of their respective baselines. We find different datasets applied to different control sites produce a wide range of forest loss rates. The baselines of all three projects fall within or below a “zone of reasonable accuracy,” based on an integration of ex-post forest loss rate results, establishing the concept of reasonable accuracy as a valid standard against which to assess REDD+ project baselines.