In the IPCC models of climate change mitigation, most scenarios ensuring less than 2ºC of warming assume deployment of some form of “negative emissions technology,” alongside dramatic reductions in emissions and other major societal changes. Proposed negative emissions technologies include bioenergy with carbon capture and storage, enhanced weathering of minerals, direct air capture, and afforestation / reforestation. Among these technologies, the use of trees for carbon sequestration through photosynthesis is well established, requires little energy, has comparable sequestration potential, and can be deployed at scale for relatively low cost. The primary constraint on using trees for sequestration is land, which is limited and increasingly subject to competitive demand. Thus, maximizing the capacity and long-term stability of every hectare used for planting would bolster the critical role of trees in a broad negative emissions strategy. Here, we propose to build a new data resource and optimization tool that leverages modern measurements and machine learning to help address this need.