# 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
#### 4.4.3 Assimilation Inversion Based on Regional Scale Models  
Currently, most carbon flux inversion studies in China focus on global or continental scales, utilizing global transport models with relatively low resolution. This approach faces significant challenges due to the heterogeneity in the spatiotemporal distribution of the biosphere, errors in transport models, and limited in-situ observational data. Consequently, there is considerable uncertainty in carbon flux assimilation, with results often lacking the resolution needed for accurate regional carbon source and sink research. The expansion of the CO\({}_{2}\) in-situ network and new atmospheric CO\({}_{2}\) remote sensing data have spurred the development of regional inversion systems, which use regional chemical transport models (CTMs) for higher-resolution CO\({}_{2}\) flux and atmospheric transport analysis at feasible computational costs. However, CTM simulations still suffer from high uncertainty, leading to notable discrepancies in CO\({}_{2}\) flux estimates at sub-continental scales [187].  
[PERSON] et al. [189] highlighted the critical role of assimilation in enhancing regional CTM performance, suggesting that EnKF (Ensemble Kalman Filter) and CMAQ (Community Multiscale Air Quality Modeling System) can improve the accuracy of synchronized-scale CO\({}_{2}\) concentrations. [PERSON] et al. [190] utilized WRF-Chem/DART to assimilate OCO-2 retrieval results, enhancing CO\({}_{2}\) concentration estimates. Recent studies have employed regional CTMs to invert CO\({}_{2}\) fluxes from ground stations, towers, and aircraft, using models like CMAQ, WRF-Chem, CHIMERE, and FLEXPART Lagrangian. These efforts have estimated land ecosystem exchanges (e.g., Europe, North America, East Asia) and urban CO\({}_{2}\) emissions (e.g., Los Angeles, Paris, Indianapolis), demonstrating the increasing recognition of regional CTMs in resolving fine-scale CO\({}_{2}\) concentrations [190; 191; 192; 193; 194; 195].  
Despite these advancements, regional CTMs are rarely used for inverting satellite carbon data for land carbon sinks in China. Future work should focus on developing inversion schemes that integrate extensive observational data and account for correlations between XCO\({}_{2}\) observations and model errors [196]. A promising approach is to combine CO\({}_{2}\) inversion schemes with atmospheric data-assimilation systems to jointly optimize surface CO\({}_{2}\) fluxes and atmospheric variables. Previous studies have shown that dual-state carbon flux inversion systems can reduce uncertainties in initial CO\({}_{2}\) fields and fluxes [193; 197; 198]. Recently, [PERSON] et al. [199; 200; 201] improved air quality forecasting and emission estimation in China by creating a novel flux prediction model based on an ensemble-based joint data-assimilation framework (JDAS), allowing for high-resolution concentration and flux ensembles. [PERSON] et al. [202] extended this work by developing a high-resolution CO\({}_{2}\) flux inversion using CMAQ and the ensemble Kalman smoother (EnKS) with historical GOSAT observations, showing advantages over global models. [PERSON] et al. [160] proposed the TRACE (Transport and Chemical Evolution over Complex Terrain) regional atmospheric-carbon ensemble data-assimilation system, which uses ensemble-based synchronous state and parameter estimation (ESSPE) to jointly optimize atmospheric CO\({}_{2}\) mole fractions and surface CO\({}_{2}\) fluxes, incorporating extensive satellite observational data. Figure 26 illustrates the main components of the TRACE system.