# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 1 Introduction
This technology meets the demand for quantitative monitoring  
Figure 1: Monthly average of global CO\({}_{2}\) concentrations (from [[https://gml.noaa.gov/ccgg/trends/global.html](https://gml.noaa.gov/ccgg/trends/global.html)]([https://gml.noaa.gov/ccgg/trends/global.html](https://gml.noaa.gov/ccgg/trends/global.html)), accessed on 25 June 2023). of anthropogenic greenhouse gas emissions and provides sustained, stable global observations. It also verifies traditional bottom-up inventory results through the \"top-down\" approach, offering a more comprehensive and accurate data source for international climate change negotiations and long-term climate assessments. Given the unique characteristics of carbon dioxide, there has been growing interest in utilizing high-resolution satellite detection technology for greenhouse gases. The aim is to obtain precise CO\({}_{2}\) concentration-monitoring data and improve the accuracy of atmospheric CO\({}_{2}\) level predictions. This has led to increased research on using satellite-derived XCO\({}_{2}\) observations to invert and optimize CO\({}_{2}\) fluxes and monitor spatial and temporal variations in atmospheric CO\({}_{2}\) concentrations. By leveraging satellite-derived XCO\({}_{2}\) observations, researchers aim to gain insights into CO\({}_{2}\) sources and sinks, better understand the processes affecting its atmospheric distribution, and enhance the tracking and monitoring of CO\({}_{2}\) level changes. As depicted in Figure 2, a key challenge in this approach is distinguishing between background signals and anomalies in XCO\({}_{2}\) observations. Identifying emissions from anthropogenic fossil fuel combustion remains challenging, necessitating effective differentiation between anthropogenic and natural contributions to carbon sinks. This differentiation is crucial for accurate CO\({}_{2}\) flux inversion. Mature satellite remote sensing CO\({}_{2}\) column concentration data can complement ground-based observations, addressing gaps and improving overall monitoring accuracy. Studies have demonstrated that if the accuracy of atmospheric column-averaged CO\({}_{2}\) dry-air mixing ratio (XCO\({}_{2}\)) from satellite observations at a regional scale can achieve 1% or higher (with inversion errors controlled to within 4 ppm), it can effectively address the limitations of ground-based observations. Continuous, high-precision global satellite observations enable researchers to better study the distribution of CO\({}_{2}\) sources and sinks, monitor long-term CO\({}_{2}\) concentration trends, and reduce uncertainty in climate research. As a result, there has been significant effort to enhance the accuracy of atmospheric CO\({}_{2}\) inversions by developing various methods for measuring greenhouse gas concentrations. These measurements are iteratively inverted using complex models to achieve optimal estimates of carbon fluxes, which rely on various assumptions. Beyond improving the quality and quantity of observation data, it is crucial to integrate advanced technologies and methods, such as satellite remote sensing, big data analytics, carbon assimilation, and machine learning. These integrations enhance the computational capacity of inversion models, improve high-precision remote sensing measurements and CO\({}_{2}\) inversion technology, and advance data-assimilation methods and atmospheric chemistry models. Additionally, acquiring high-precision environmental coefficients and ground-checking capabilities is essential for validating satellite estimation results with ground observations [6]. Figure 2: Carbon satellite observation of carbon flux columns [5]. The Carbon-Assimilation System (CAS) represents a \"top-down\" approach for estimating carbon sources and sinks. It integrates measured atmospheric CO\({}_{2}\) concentrations with atmospheric transport modeling and data-assimilation techniques. CAS is widely used for estimating net land-air CO\({}_{2}\) exchange and has become a crucial tool for carbon-cycle studies at national and regional scales.