The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative involves detecting changes in land cover, use, and condition with the goal of producing land change information to improve the understanding of the Earth system and provide insights on the impacts of land surface change on society. The change detection method ingests all available high-quality data from the Landsat archive in a time series approach to identify the timing and location of land surface change. Annual thematic land cover maps are then produced by classifying time series models. In this paper, we describe the optimization of the classification method used to derive the thematic land cover product. We investigated the influences of auxiliary data, sample size, and training from different sources such as the U.S. Geological Survey’s Land Cover Trends project and National Land Cover Database (NLCD 2001 and NLCD 2011). The results were evaluated and validated based on independent data from the training dataset. We found that refining the auxiliary data effectively reduced artifacts in the thematic land cover map that are related to data availability. We improved the classification accuracy and stability considerably by using a total of 20 million training pixels with a minimum of 600,000 and a maximum of 8 million training pixels per class within geographic windows consisting of nine Analysis Ready Data tiles (450 by 450 km²). Comparisons revealed that the NLCD 2001 training data delivered the best classification accuracy. Compared to the original LCMAP classification strategy used for early evaluation (e.g., Trends training data, 20,000 samples), the optimized classification strategy improved the annual land cover map accuracy by an average of 10%.