# 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  
#### 4.4.1 Optimising CO\({}_{2}\) Emissions in Urban Areas  
Accurate monitoring of urban CO\({}_{2}\) emissions is essential for guiding sustainable urban development. Currently, CO\({}_{2}\)-emission inventories are compiled using a bottom-up (BU) approach, which offers more precise prior fluxes for inverse assimilation and helps reduce prior flux uncertainties. Ground-based flux-observation networks can validate the \"top-down\" models and enhance inversion models [172]. However, this approach has notable limitations. [PERSON] et al. [173] used X-STILT to analyze urban XCO\({}_{2}\) enhancements in five Middle Eastern cities and found that the simulation results were not sensitive to different spatial emission patterns in the bottom-up inventory, which hinders understanding of spatial and temporal emission variations. While data-assimilation (DA) techniques have been applied to urban environments [174; 175; 176], combining transport models with atmospheric observations to refine urban emissions, challenges remain. Urban emission studies often aim to validate bottom-up CO\({}_{2}\) inventories by constraining fossil fuel fluxes but frequently overlook fundamental emission processes causing discrepancies between observed and simulated concentrations. To address this, higher-resolution prior emissions are needed to capture significant spatiotemporal variations.  
[PERSON] et al. [177] developed the CarbonTracker Data-Assimilation Shell (CTDAS), a city-scale DA system designed to quantify CO\({}_{2}\) emissions from various urban sectors. This system uses a dynamic fossil fuel-emission model with optimized parameters and the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model to represent non-point source emissions. It also estimates model parameter uncertainties, providing insights into spatiotemporal uncertainty and error correlations among emission sources. [PERSON] et al. [178] introduced a new method within the Bayesian inversion framework, combining direct CO\({}_{2}\) flux observations from urban eddy covariance (EC) towers with detailed data from an advanced urban BU surface flux model to refine urban emission estimates. This approach yields optimized gridded CO\({}_{2}\) flux information for various surface flux components--such as building heating,  
Figure 24: Technology roadmap for assimilation-cycle process of satellite column concentration.  
commercial/industrial activities, traffic, human respiration, and biotic net exchange--at a 20-m resolution and weekly time step. Figure 25 illustrates the BU model inputs as detailed by [PERSON] et al. [179].  
The results indicate that urban EC observations can be sustainably used to improve high-resolution BU surface CO2 flux model estimates and provide realistic seasonal variations for each flux component. However, the current approach needs further expansion of the model domain and should be combined with atmospheric inversion methods and tall tower EC to extend the model domain to city-wide emissions and establish an observation-based comprehensive monitoring system to address potential surface flux processes at different scales.