Sensor harmonization is required to produce consistent Landsat imagery for long-term change detection. This study investigated the effect of seasonality and aerosol optical thickness (AOT) on linear harmonization functions, which are frequently used to create consistent Landsat 7 ETM+ and Landsat 8 OLI time series data. We found that training harmonization functions with pixels that have low or average AOT can greatly reduce the difference between near-coincidental Landsat 7 and Landsat 8 observations, and that seasonally trained harmonization models outperform models trained on year-round data. We assessed the effect of ETM+/OLI sensor harmonization on forest type classification with a Random Forest model, and found that seasonally harmonized imagery provided more consistent classification maps than the alternatives. This study illustrates important details related to the creation of harmonized datasets and is a significant step toward creating more consistent Landsat data for long-term change detection analysis.