In the current budget climate, the U.S. Forest Inventory and Analysis program is under increased pressure to do more with less. While reliance solely on field data under the current annual inventory system is a suitable solution when funding is adequate and stable, decreasing budgets and increasing need for timely information may necessitate solutions that can augment field data collection with remote sensing and forest projection models in a cost-effective way. There is a long, rich history of using remote sensing in forest inventory applications. As the role of remote sensing has expanded, so has the need for more flexible statistical procedures to take advantage of increasingly better ancillary data. In this paper, we document pivotal remote sensing projects in our history, and simultaneously track the evolution of statistical methods accompanying them. We highlight current studies improving statistical efficiency and information quality, and recommend viable alternatives for reducing costs in forest inventories across the continental United States.