As an important greenhouse gas, CO₂ has a significant impact on the global climate due to its concentration changes. The continuous, stable, and large-scale characteristics of satellite remote sensing make it an effective tool for monitoring atmospheric CO₂. However, due to the influence of satellite payload settings and factors such as clouds and aerosols in the atmosphere, it is currently difficult for a single carbon satellite to obtain continuous high-resolution global CO₂ concentration distribution information. Therefore, in order to better determine the multi-source satellite CO₂ fusion method, it is necessary to analyze the uncertainty of different satellite products. This paper utilizes ground-based Total Carbon Column Observing Network (TCCON) data from 2019 to 2021 to conduct an uncertainty analysis of CO₂ retrieval accuracy for the GOSAT, OCO-2, and GOSAT-2 satellites. Based on the analysis results, a global multi-source CO₂ fusion model was established using the error inverse distance weighting method incorporating unit weight principles and the Kriging interpolation method. The spatiotemporal distribution patterns of the fused CO₂ were then further analyzed. The analysis results show that the uncertainty of OCO-2 is the lowest, with a root mean square error E_RMS of 1.10 × 10^-6, followed by GOSAT with an E_RMS of 1.88 × 10^-6, and GOSAT2 has the highest uncertainty, with an E_RMS of 3.02 × 10^-6. The fusion model established has good accuracy, with a mean absolute error of 0.91 × 10^-6 and a mean absolute error percentage of 0.22%. In terms of CO₂ spatial distribution, it is found that the concentration of CO₂ in the northern hemisphere is higher than that in the southern hemisphere, with high-value areas appearing in some regions. While in terms of seasonal changes, the CO₂ concentration is higher in spring and winter than in summer and autumn, with the highest concentration in spring.