# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies
### History of the Development of the Data-Asimilation System for Atmospheric CO\({}_{2}\)
[122] used the Carbon Tracker system with a three-layer nested model to estimate carbon sources and sinks in the Asian region, and the results showed good performance of the Carbon Tracker system. [PERSON] et al. [123] and [PERSON] et al. [124] simulated and analyzed the spatiotemporal distribution of carbon sources/sinks in the Chinese region using the nested atmospheric CO\({}_{2}\) Transcom assimilation system and the Carbon Tracker assimilation system, respectively. Their results indicated that the terrestrial ecosystem in China is a large carbon sink that has been underestimated. Around 2014, the NASA team developed the CMS-Flux system based on the GEOS-Chem model for CO\({}_{2}\) flux estimation and attribution, using the four-dimensional variational assimilation method. Other global carbon-assimilation systems include PHILIP, AMES, and BAKER, which are based on the GEOS-Chem model and PCTM transport model, respectively. All of these model systems participated in the OCO-2 MIP model intercomparison project, which provided multi-model inversion results of CO\({}_{2}\) fluxes in land systems of various countries from 2015 to 2020. China's advancements in carbon-assimilation inversion research have been noteworthy despite a late start. In 2015, the GCAS (Global Carbon-Assimilation System) was developed, incorporating an atmospheric transport model, a terrestrial ecosystem process model, and a land-atmosphere-assimilation system using the four-dimensional variational assimilation method [124]. Although this system laid a solid foundation, it initially struggled with integrating diverse observational data effectively. The creation of the China Carbon-Tracking-Asimilation System (CT-China) by adapting the CarbonTracker system provided a robust framework but did not fully address previous limitations. In 2016, Nanjing University developed GCAS-v2, enhancing spatial resolution to 1\({}^{\circ}\)\(\times\) 2.5\({}^{\circ}\) and 4D sliding sampling [125], though spatial resolution issues persisted. Regionally, the CFI-CMAQ system by Nanjing University and the Institute of Atmospheric Physics employed advanced ensemble methods for CO\({}_{2}\) data assimilation but faced scaling challenges [126]. [PERSON]'s Tracers Tracker combined the ensemble four-dimensional variational method with the CMAQ model for high-resolution regional analysis but lacked integration with ecological models, limiting its optimization of carbon fluxes from photosynthesis and respiration. Despite these advancements, integrating ecological models and optimizing data assimilation across various scales remains a challenge. All of the above-mentioned global carbon-assimilation systems optimize the CO\({}_{2}\) flux between the land surface and the atmosphere, assuming that the error in anthropogenic carbon flux can be ignored, in order to obtain the net carbon flux between the terrestrial ecosystem and the atmosphere. The atmospheric carbon-assimilation inversion system is still constrained by observational data, and the observational data it relies on are still global ground-based observational data, resulting in large uncertainties in the simulation results of carbon sources/sinks.