Advancing Forest-Based Climate Solutions through Data-Driven Carbon Flux Estimation

Nature-based climate solutions, including reforestation, require credible carbon accounting frameworks that capture ecosystem-scale carbon fluxes and ensure additionality. However, most existing baselines rely on static biomass estimates that overlook spatial heterogeneity and interannual variation in forest carbon uptake. Here, we present a data-driven framework for estimating monthly Net Ecosystem Productivity (NEP) across eastern U.S. forests at 500-m resolution from 2003 to 2023. We trained Random Forest models using observations from 47 eddy-covariance sites combined with gridded remote sensing and meteorological data. Feature selection and SHAP analyses highlight NDVI, LAI, solar-induced fluorescence, shortwave radiation, and vapor pressure deficit as the primary drivers of NEP. Our results show that eastern U.S. forests have continued to strengthen as a carbon sink over the past two decades, with a mean NEP of −195 ± 122 g C m⁻² yr⁻¹ and an increasing trend of 2.51 g C m⁻² yr⁻¹. Annual NEP exhibits strong year-to-year sensitivity to spring temperature and moisture anomalies, with extreme events causing large variations in carbon uptake that are often followed by partial or full summer recovery, reflecting considerable ecosystem resilience. The substantial spatial and temporal variability in NEP predictions underscores the need for regionally calibrated, observation-based baselines. Our framework supports this need by providing dynamic, annually updated maps of forest carbon uptake to improve evaluation of reforestation and other nature-based climate solutions in the eastern United States.