# XCO\({}_{2}\) Fusion Algorithm Based on Multi-Source Greenhouse Gas Satellites and CarbonTracker
## 5 Conclusions  
OCO-2 and GOSAT satellites are easily restricted by observation conditions, such as aerosols and clouds, resulting in limited coverage of their effective measurements. In order to form XCO\({}_{2}\) data with global coverage, Kriging interpolation was used to improve the coverage and spatial-temporal resolution of OCO-2 and GOSAT data, and CT model data was used as a supplement in this paper. After interpolation, the average coverages of the OCO-2, GOSAT, and CT datasets are 71.66%, 52.45%, and 100%, respectively, and the coverages of OCO-2 and GOSAT are increased by 53.65% and 48.5%, respectively. Furthermore, this paper used the TC algorithm to fuse the CT model dataset with two satellite-interpolated datasets. This algorithm can analyze the uncertainty of datasets by comparing the random errors of three independent datasets, and then determine the weights of three datasets according to the random errors to carry out data fusion. Through cross-validation with TCCON data, the MAE of the fused dataset was 0.6273 ppm, RMSE was 0.7683 ppm, and \(R^{2}\) was 0.8279. The results indicate that this method can significantly improve the coverage and accuracy of the XCO\({}_{2}\) dataset. In this paper, we have obtained the semi-monthly \(1^{\circ}\times 1^{\circ}\) half-monthly average XCO\({}_{2}\) dataset with good accuracy. Due to the coverage of the original satellite observation data, we cannot further temporarily improve the spatial resolution. However, we still hope to further improve the spatiotemporal resolution of the data to better support future carbon neutrality research. With the continuous development of satellite remote sensing technology, more and more carbon observation satellites have been launched or are planned to be launched. For example, China has launched the world's first active lidar CO\({}_{2}\) detection satellite--DQ1. Compared with passive remote sensing, active lidar observation has the advantage of all-time and all-weather observation, and can detect global CO\({}_{2}\) concentration with higher resolution and greater coverage [47; 48]. At the same time, in order to realize the quantitative monitoring of man-made carbon emissions, which is the core goal of the future carbon satellite observation plan, future carbon monitoring will develop a multi-directional satellite network observation of greenhouse gas concentration, air pollution components, and biomass combustion emissions, as well as a global carbon monitoring constellation with active and passive joints [49]. Therefore, in subsequent studies, multi-source greenhouse gas satellite data and carbon assimilation systems can be combined for interpolation and fusion. In addition, with the increasing number of ground-based observation data, we will consider taking the ground-based observation data as the optimal control condition, multi-satellite interpolation results and carbon assimilation data as the input data, and obtain the XCO\({}_{2}\) fused dataset with a higher spatial-temporal resolution, higher accuracy, and larger coverage based on the TC algorithm, so as to promote the estimation of CO\({}_{2}\) emissions from regional sources and contribute to the research of carbon neutralization. The study was completed with cooperation between all authors. [PERSON] and [PERSON] designed the research topic; [PERSON] and [PERSON] conducted the experiment; [PERSON] checked and analyzed the experimental results; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript. This research was funded in part by the Natural Science Foundation of Jiangsu Province, China under Grants BK20190779; in part by the National Natural Science Foundation of China under Grants 42001273. Not applicable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **January** & **April** & **July** & **October** \\ \hline MAE/ppm & 0.4803 & 0.5099 & 0.8143 & 0.5486 \\ RMSE/ppm & 0.6736 & 0.7012 & 1.1405 & 0.7395 \\ \hline \hline \end{tabular}
\end{table}