# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 1 Introduction
Current trends in CO\({}_{2}\)-assimilation systems include: (1) joint assimilation of XCO\({}_{2}\) data from both ground-based and satellite observations; (2) integration of atmospheric CO\({}_{2}\) concentrations, site fluxes, remote sensing surface parameters, and other relevant data; (3) optimization of ecosystem and anthropogenic carbon fluxes. Review articles have highlighted the capabilities of remote sensing technology in observing CO\({}_{2}\) concentrations and its significance for understanding global carbon fluxes. [PERSON] et al. [7] reviewed advancements in satellite remote sensing of atmospheric CO\({}_{2}\), focusing on satellite payloads, inversion algorithms, and validation applications. [PERSON] et al. [8] discussed the development of CO\({}_{2}\) retrieval algorithms, spatial interpolation methods, and ground observations, emphasizing the need for high-precision carbon source simulation methods. [PERSON] et al. [9] explored remote sensing capabilities for estimating carbon fluxes in ecosystems, analyzing spatial sensitivities and model-specific assumptions [10], and meteorological input choices [11]. [PERSON] et al. [12] outlined the technological development of carbon remote sensing, detailing three stages for two generations of satellites and clarifying application needs for accuracy, integration of information, and network observation trends. [PERSON] et al. [13] introduced carbon emission-monitoring methods and remote sensing technologies, particularly focusing on current and future onboard sensors for monitoring main carbon emission gases. [PERSON] et al. [14] provided a comprehensive overview of global carbon inventory satellite remote sensing, summarizing research progress on greenhouse gas remote sensing, carbon sink estimation, and carbon flux assimilation, and proposed a carbon-monitoring satellite program for China. Given the advancements in greenhouse gas satellite observation technology, XCO\({}_{2}\) data requires further analysis. As satellite CO\({}_{2}\) data continues to accumulate, a more thorough analysis of XCO\({}_{2}\) remote sensing data is needed. This includes transitioning from simple space-time grid averaging to more complex data-assimilation methods. This paper reviews the progress in global satellite short-wave infrared hyperspectral CO\({}_{2}\) remote sensing detection and outlines the current status of greenhouse gas remote sensing satellites and gas inversion techniques. It also describes CO\({}_{2}\) data-assimilation techniques used to study global carbon sources and sinks, as well as the techniques and processes involved in the assimilation cycle. The contributions of this paper are as follows:  
1. An overview of CO\({}_{2}\) satellite observation technologies, launched and planned CO\({}_{2}\) satellites was presented. 2. Introduction to the principles and development of algorithms for various CO\({}_{2}\) atmospheric transport models and inversion methods. 3. Introduction to carbon dioxide data-assimilation techniques and the global CO\({}_{2}\)-assimilation system, as well as the application of the assimilation program for carbon flux estimation. The structure of this paper is outlined in Figure 3. Section 1 introduces the research's context, significance, and contributions. Section 2 reviews advancements in ground-based and satellite CO\({}_{2}\) observations, the XCO\({}_{2}\) dataset, and uses CiteSpace to analyze current research trends. Section 3 details various forward models and inversion algorithms for XCO\({}_{2}\) remote sensing. Section 4 covers the development of the CO\({}_{2}\)-assimilation system, focusing on data-assimilation algorithms, frameworks, and carbon flux applications. Section 5 provides a summary of the paper.