An Assessment of Anthropogenic CO(2) Emissions by Satellite-Based Observations in China

Carbon dioxide (CO(2)) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO(2) emissions. Quantifying anthropogenic CO(2) emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO(2) concentration. In this study, we propose an approach for estimating anthropogenic CO(2) emissions by an artificial neural network using column-average dry air mole fraction of CO(2) (XCO(2)) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO(2) anomalies (dXCO(2)) derived from XCO(2) and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO(2) in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO(2) emissions especially in the areas with high anthropogenic CO(2) emissions. Our results indicate that XCO(2) data from satellite observations can be applied in estimating anthropogenic CO(2) emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO(2) uptake and emissions, from satellite observations.