Satellite measurements of the spatiotemporal distributions of atmospheric CO 2 concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO 2 column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO 2 data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO 2 concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO 2 concentrations is presented with the goal of generating global land CO 2 distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal
OPEN ACCESSAtmosphere 2014, 5 871 resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO 2 distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO 2 distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO 2 distribution with high accuracies and high spatiotemporal resolution.
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