Project collaborators
Dr. Jingyi Huang, Soil Science
Maria Oros, Data Science Institute
Project start and end dates
June-Aug 2024
Project summary
Soil organic carbon (SOC) plays a crucial role in mitigating atmospheric carbon levels, acting as a significant carbon sink. To address the challenges posed by climate change, land use, and agricultural practices, a comprehensive understanding of how these factors influence SOC is essential. This research aims to quantify the spatial and temporal variations of SOC stocks (SOCS) across the contiguous United States. By integrating soil datasets with environmental variables—including climate, land cover, and topography—and employing a machine learning algorithm, we estimate SOCS over large geographic areas and extended time periods. The environmental inputs include high-resolution soil property maps (100 m), annual climate data (1 km), remote-sensing-derived land cover maps (250 m), and a digital elevation model (30 m). To support further research and application, we developed an open-source API that delivers SOC and SOCS data, enabling broader use in scientific research, environmental management, and policy-making.
Project outputs/deliverables
- Public Github repository
- Interactive map (in process)