The concentration of carbon dioxide (CO2) in Earth’s atmosphere is increasing due to human activities and the resulting effects on the global climate system have initiated sev- eral policy-driven approaches to reduce emissions of this greenhouse gas. Quantifying the effectiveness of such policies requires both bottom-up and top-down approaches to estimate CO2 emissions. This work investigates, for the first time, the potential of using SAM observations from NASA’s OCO-3 instrument to disaggregate sector-specific emissions from instrument observations. Optimized sector-specific timeseries were produced using Bayesian inversion techniques and compared to proxy activity data from the transportation, commercial maritime, and industrial sectors. Results demonstrate that dense space-based observations of atmospheric CO2 are capable of disentangling sector-specific CO2 fluxes, paving the way for accurate monitoring of the effects of carbon-reduction policies and operational carbon monitoring systems.
