As one of the most important greenhouse gases, carbon dioxide (CO2) exhibits spatiotemporal variations that directly affect the accuracy of global carbon inventories. In recent years, multiple satellites have successively been deployed for observing the column-averaged CO2 dry-air mole fraction (XCO2). However, these satellites perform quite differently, so it is crucial to evaluate their XCO2 products systematically for both scientific and practical reasons. Most existing studies rely on ground-based observations or the CarbonTracker (CT) model data as reference benchmarks. Nevertheless, because ground-based stations are sparsely distributed and model data are subject to prior errors, biases may be introduced into the evaluation results. In contrast, the Three-Cornered Hat (TCH) method can estimate the relative errors of multi-source data without true values. Based on this, the current study systematically evaluates the XCO2 products of the four following satellites—Greenhouse Gases Observing Satellite (GOSAT), GOSAT-2, Orbiting Carbon Observatory 2 (OCO-2), and OCO-3—over China by integrating the TCH method, ground-based observations and CarbonTracker model data. The results show that the monthly coverage of the four satellite XCO2 products in China is limited. In terms of overall performance, the OCO-series outperforms the GOSAT-series, with OCO-3 showing the relatively best performance. Additionally, the TCH method proves to be applicable and reliable for uncertainty analysis of XCO2 data. This study provides a new perspective for the quality grading and fusion application of multi-source satellite XCO2 data, and is of great significance for carbon assimilation models.
Ruan et al. (Fri,) studied this question.