# XCO\({}_{2}\) Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker
## 1 Introduction  
Since the industrial revolution, the concentration of carbon dioxide (CO\({}_{2}\)) in the atmosphere has continued to rise, which is an important factor causing the greenhouse effect [1; 2]. In recent years, in order to control the trend of global warming and slow down the gradual increase in atmospheric CO\({}_{2}\) concentration, various countries have formulated corresponding emission reduction measures, but the atmospheric CO\({}_{2}\) concentration is still rising [3]. The sixth assessment report of the IPCC in August 2021 revealed that since the 1970s, the rate of global warming has been higher than that of any 50 years in the past 2000 [4]. In order to realize the great vision of peak CO\({}_{2}\) emission by 2030 and carbon neutralization by 2060, the monitoring of global atmospheric CO\({}_{2}\) concentration is essential. The traditional CO\({}_{2}\) ground-based network observation can obtain relatively high-precision data, but its sites are few and unevenly distributed, and it is difficult to establish sites in the ocean and other regions, so it is difficult to obtain sufficient observation information. Therefore, it presents a significant challenge to obtain the distribution information of global carbon sources and sinks. With the development of remote sensing technology, greenhouse gas observation satellites have gradually developed and become an important means by which to obtain greenhouse gas data [5; 6; 7; 8; 9; 10]. Compared with ground-based observation networks, satellite remote sensing observation can cover the Earth's surface to a great extent, but there are also some deficiencies [11; 12]. For example, due to the observation limitations of passive satellites, the atmospheric carbon dioxide column concentration (XCO\({}_{2}\)) can only be obtained under weather conditions of no cloud cover or low aerosol concentration [13; 14]. Due to the restriction of the solar altitude angle, it is also difficult for satellites to observe effective data in high latitudes. Furthermore, because of the error ofthe inversion algorithm and the limitation of the Earth's surface or atmospheric conditions, the obtained CO\({}_{2}\) concentration data has a certain deviation. In order to meet the needs of climate research, the inversion accuracy of satellite observation XCO\({}_{2}\) needs to be better than 1% [15]. [PERSON] proposed a spectral correction method for TANSAT based on the UOL FP algorithm, and verified its XCO\({}_{2}\) inversion product with TCCON data, with an RMSE of 1.59 ppm [16]. [PERSON] used the optimization estimation method to retrieve XCO\({}_{2}\), and verified the inversion accuracy of the GF-5 satellite by using the data from TCCON ground sites [17]. The results showed that the inversion accuracy of XCO\({}_{2}\) was 0.67%, but the inversion algorithm had insufficient ability to correct the long-range complex atmospheric interference. [PERSON] and [PERSON] proposed to use the Bayesian hierarchical spatio-temporal random effect (STRE) model to process AIRS data and compared it to the real dataset of simulation research and global satellite carbon dioxide measurement, but the model only improved the calculation speed [18]. [PERSON] used seven CO\({}_{2}\) inversion algorithms of the GOSAT and SCIAMACHY satellites. Assuming that there were few outliers, they chose the median as the value in a grid to generate a global high-precision CO\({}_{2}\) fusion dataset EMMA [19]. However, they did not improve the temporal and spatial resolution of CO\({}_{2}\) data. [PERSON] used the CO\({}_{2}\) data of GOSAT and AIRS, but the method only obtained the CO\({}_{2}\) concentration in the low troposphere [20]. [PERSON] presented a HASM XCO\({}_{2}\) surface obtained by fusing TCCON measurements with GEOS-Chem model results [21]. [PERSON] used the neural network method to estimate XCO\({}_{2}\) data from OCO-2 measurement, but it was very time-consuming to train the model in practical application [22].