# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 4 Carbon Flux Data Assimilation and Inversion Studies
### Application of Carbon Flux Estimation Based on Assimilation Schemes
#### 4.4.2 Assimilation of High-Resolution Carbon Fluxes Based on Satellite Observations  
In recent years, the increasing volume of CO2-observation data has highlighted a significant challenge: the computational cost of traditional atmospheric inversion methods for CO2 column concentration (XCO2) products from satellite observations is prohibitively high. This makes it difficult to assimilate large datasets for high-resolution carbon flux optimization. The global carbon-assimilation system offers a promising solution to this problem. By employing data-assimilation techniques, it helps mitigate the discrepancies between carbon flux predictions from land surface models and atmospheric CO2 concentration measurements. This approach reduces uncertainties in land surface models and enhances carbon flux estimates by integrating observed carbon-related data [180]. For instance, European scientists have developed the Global Carbon Data-Assimilation System (CCDAS). This system assimilates a variety of data sources, including satellite and ground-based atmospheric CO2 concentrations, site flux data, remote sensing land surface parameters, and more, to optimize ecosystem model parameters and carbon flux estimates. This process improves the accuracy of regional and global carbon source and sink estimations and lays the groundwork for developing high-resolution global carbon-assimilation systems. However, CCDAS faces challenges due to its substantial computational demands. As a result, it operates with relatively low spatial resolution and has limited capacity for incorporating ground-based CO2 concentration observations. These limitations affect the accuracy of data assimilation and the ability to capture regional variations in CO2 emissions. To address these challenges, data assimilation using atmospheric transport models and observational data is a common method to enhance the accuracy of CO2 flux and  
Figure 25: Schematic diagram of the BU model methodology. concentration measurements. Research on the assimilation of atmospheric CO\({}_{2}\) from satellite and ground-based observations indicates that satellite data significantly improve the accuracy of both regional and global carbon source and sink estimations. This advancement facilitates the development of high-resolution global carbon-assimilation systems. Integrating satellite data into carbon flux-assimilation inversion systems holds substantial promise for enhancing the precision of carbon source and sink estimations and deepening our understanding of land carbon sink dynamics. Progress has been made in carbon-assimilation inversion studies utilizing satellite observation data. For example, [PERSON] et al. [181] integrated CO\({}_{2}\) column concentration data from the OCO satellite into the EnKF data-assimilation algorithm, using the GEOS-Chem atmospheric chemistry transport model. This approach improved regional-scale estimates of land surface CO\({}_{2}\) flux by reducing prediction uncertainties. Similarly, [PERSON] et al. [182] employed the SRONKIT RemoTeC method with GOSAT satellite column concentration data for global land carbon flux inversion. Their results highlighted the effectiveness of satellite data in enhancing the accuracy of carbon sink estimations, particularly in areas with limited ground observations. [PERSON] et al. [183] combined atmospheric transport models, land flux exchange models, and ocean flux simulation models to develop a CO\({}_{2}\) flux-assimilation inversion system, incorporating GOSAT satellite data to produce global monthly average CO\({}_{2}\) flux estimates. [PERSON] et al. [184] utilized various atmospheric transport models and inversion algorithms to analyze global CO\({}_{2}\) flux, assessing the uncertainty and robustness of the results based on GOSAT satellite data. [PERSON] et al. [185] established a global carbon-assimilation inversion system using the GEOS-Chem model and examined the uncertainty of CO\({}_{2}\) flux estimates in both land and ocean regions with satellite data. Additionally, [PERSON] et al.