# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 5 Conclusions and Prospect  
With advancements in carbon-cycle research, observing and analyzing the spatiotemporal distribution of atmospheric CO\({}_{2}\) concentrations has become crucial for accurately estimating carbon sources and sinks. This article reviews the progress in ground-based observation stations and carbon satellite sensors. Unlike traditional \"bottom-up\" methods, modern observations of atmospheric CO\({}_{2}\) are no longer confined by ground-based limitations, allowing for high-resolution and precise concentration monitoring. The article also discusses various satellite CO\({}_{2}\) inversion datasets and explains the inversion principles and algorithms of current carbon remote sensing satellites. Despite the success of these remote sensing inversion algorithms, there remains a need to enhance high-precision remote sensing measurements and CO\({}_{2}\) inversion technologies. Additionally, atmospheric radiative transfer models require further refinement. Moreover, integrating advanced technologies such as big data analytics, carbon assimilation, and machine learning can improve the computational efficiency of inversion models and validate satellite-derived estimates against ground-based observations. Satellite remote sensing technology has provided extensive, high-resolution spatiotemporal data on atmospheric CO\({}_{2}\), significantly enhancing global carbon flux estimation capabilities. Current carbon-assimilation systems can effectively simulate global carbon sources and sinks at grid and weekly scales. However, challenges persist, including the simplification of forecast operators, the accuracy of atmospheric transport models, and the design of assimilation algorithms. These challenges limit the inversion accuracy and precision of assimilation systems and represent key bottlenecks requiring ongoing research. Future research in CO\({}_{2}\)-assimilation systems should focus on regional-scale studies, the integration of ground-based and satellite data, and the simultaneous assimilation of atmospheric CO\({}_{2}\) concentrations, site fluxes, and remote sensing surface parameters. Given that satellite  
Figure 26: Simplified schematic of the TRACE system. The dashed lines indicate how the ensemble of posterior atmospheric fields, including atmospheric CO\({}_{2}\) mole fractions, and posterior flux parameters are passed to the next assimilation cycle as the new prior. and ground-based data have different spatial representations, developing a global carbon-assimilation system capable of integrating these diverse data forms remains a crucial research direction. Conceptualization, K.H. ; methodology, K.H. and X.F. ; software, P.S., Z.L. and X.F. ; formal analysis, X.F. ; investigation, X.F., S.W., Y.W. and H.W. ; writing--original draft preparation, X.F., Y.X., S.W., Y.W. and H.W. ; writing--review, K.H., X.F., L.D. and M.X. ; editing, K.H. and Q.Z. ; visualization, X.F. ; supervision, K.H. and M.X. ; project administration, X.F., Q.Z., P.S. and Z.L. All authors have read and agreed to the published version of the manuscript. The research in this article is supported by the National Natural Science Foundation of China (42075130). Not applicable. During the preparation of this work the authors used ChatGPT in order to improve language and readability. After using this tool/service, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication. The research in this article is supported by the financial support of Nanjing Ta Liang Technology Co., Ltd., and Nanjing Fortune Technology Development Co., Ltd. is deeply appreciated. The authors would like to express heartfelt thanks to the reviewers and editors who submitted valuable revisions to this article. [PERSON], [PERSON] and [PERSON] were employed by the Economic and Technical Research Institute of State Grid Henan Electric Power Company. [PERSON] was employed by the State Grid Henan Electric Power Company.