Forest ecosystems store vast amounts of carbon, making them crucial in the fight to stabilize the Earth’s climate. However, deforestation and the effects of climate change could push the Earth toward “tipping points” that lead to rapid global warming, highlighting the urgent need for enhanced monitoring of carbon in forests as well as new methods to enhance forests’ abilities to absorb carbon. Researchers at the University of Maryland and the University of Pittsburgh have been awarded $750,000 from the National Science Foundation to address this challenge by combining remote sensing data, modeling and artificial intelligence. The team’s project is led by Yiqun Xie, an assistant professor in geospatial information science, and his colleagues, Professor George Hurtt and Assistant Research Professor Lei Ma , also of the Department of Geographical Sciences. “By developing novel AI techniques to tackle challenges facing ecosystem models and remote sensing, the project seeks to lay a solid technological foundation for monitoring forest carbon dynamics at the global scale, advancing the understanding of the carbon cycle in the Earth system,” said Xie. Remote sensing and physical-based models have greatly matured in their ability to monitor and predict the movement of carbon between a forest ecosystem and the atmosphere. However, the models are imperfect and unable to put all these data to use. AI models offer new possibilities to fill this gap by harnessing larger volumes of remote sensing data and increasing the speed, resolution and capability of physical models.