# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 1 Introduction  
The issue of global climate change is one of the most serious challenges facing human society in the twenty-first century. Over the decades of the Industrial Revolution, the growth of the global population and the intensification of human activities have led to the massive combustion of fossil fuels and industrial emissions, resulting in a sharp increase in the concentration of greenhouse gases (GHGs) and pollutants in the atmosphere. Meanwhile, significant deforestation has reduced the absorption capacity for GHGs, contributing to a persistent rise in atmospheric GHG concentrations. Among these, carbon dioxide (CO\({}_{2}\)) and methane (CH\({}_{4}\)) are the two most important GHGs [1]. According to the latest Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report Synthesis Report, human activities have primarily caused global warming through the emission of greenhouse gases. Figure 1 shows that the global atmospheric concentration of CO\({}_{2}\) increased from 280 ppm before the Industrial Revolution to 419.3 ppm in 2023, with a rapid annual increase rate of 2 to 4 ppm [2, 3], which contributes to intensified global warming. As the impacts of global warming become increasingly evident, the international community has reached a consensus that \"greenhouse gas emission reduction is the most effective way to curb global warming\". Nearly 200 parties to the United Nations Framework Convention on Climate Change (UNFCCC) reached the Paris Agreement at the ParisClimate Change Conference in December 2015, aiming to limit the increase in global average temperature to less than 2 degC above pre-industrial levels. In October 2018, the IPCC report further proposed a target of limiting the temperature increase to 1.5 degC, which is crucial for the planet's ecosystems and humanity in the coming decades. Consequently, carbon-cycle research has become a key priority in global change science, necessitating continuous and accurate monitoring of atmospheric greenhouse gas concentrations, particularly CO\({}_{2}\). This includes analyzing CO\({}_{2}\) emissions from anthropogenic and natural sources (carbon sources) and CO\({}_{2}\) uptake by terrestrial and oceanic ecosystems (carbon sinks) to detect increases in atmospheric CO\({}_{2}\) due to human activities. With the advancement of carbon-cycle research, methodologies for studying carbon sources and sinks have also evolved. Currently, these methods are primarily divided into two categories: the \"bottom-up\" approach for terrestrial ecosystems and the \"top-down\" approach for the atmosphere. The \"bottom-up\" approach focuses on land-based research, estimating carbon sinks through methods such as surveys and statistics, flux observations, and model simulations. In contrast, the \"top-down\" approach targets the atmosphere, estimating carbon fluxes through an inversion system that includes atmospheric CO\({}_{2}\) concentration-observation data, inversion algorithms, and atmospheric transport models. During the 49 th IPCC Plenary Meeting in 2019, a program to enhance the 2006 IPCC Guidelines for National Inventories was adopted [4]. This program introduced a new calibration method for emission inventories based on greenhouse gas fluxes obtained from the top-down inversion of atmospheric concentration-observation data. The trend emerging from this development is that with the rapid advancement of satellite remote sensing technology, monitoring atmospheric CO\({}_{2}\) concentration changes is no longer limited to traditional ground-based observations. The inversion of carbon fluxes using atmospheric transport modeling and CO\({}_{2}\) concentration measurements will become increasingly important. Satellite remote sensing offers advantages such as objectivity, continuity, stability, large-scale coverage, and the ability to conduct repeated observations. It is a conventional method for achieving high accuracy and high spatial and temporal resolution in carbon-emission monitoring.