# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques
### Advances in Remote Sensing Algorithms for Satellite Atmospheric CO\({}_{2}\)  
The principle of satellite remote sensing inversion of CO\({}_{2}\) is mainly to use the radiation spectra near the absorption band of CO\({}_{2}\) measured by the detector to invert and calculate the concentration of CO\({}_{2}\) column. The choice between profile inversion and column concentration inversion algorithms depends on specific needs and data availability. Profile inversion is used when detailed vertical distribution information is required, such as in-depth studies of vertical mixing in the atmosphere or emission sources at specific heights. This method provides high-resolution vertical data but is complex and data-intensive. In contrast, column concentration inversion is suitable for cases where detailed vertical data is less critical or not available. It provides an overall estimate by integrating concentration data across the entire column, simplifying the inversion process and making it suitable for large-scale assessments and general monitoring.  
The CO\({}_{2}\) inversion algorithms mainly include two categories, namely, empirical algorithms and physical inversion algorithms, in which the physical methods are based on  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Radiative Transfer** & **Band Range** & \multicolumn{2}{c}{**Absorption**} & \multicolumn{1}{c}{**Scattering**} & \multicolumn{1}{c}{**Polarization**} & \multicolumn{1}{c}{**Reference**} \\ \hline
4A/OP &
\begin{tabular}{c} Near \\ Infrared \\ \end{tabular} & Line by line & Supported & Supported & [48] \\
6S & Visible Wave & Band pattern & Supported & Supported & [47] \\ LOWTRAN & 50,000 cm\({}^{-1}\)\(\sim\)Microwave & Band pattern & Supported & Unsupported & [49] \\ MODTRAN & 50,000 cm\({}^{-1}\)\(\sim\)Microwave & Band pattern/K-distribution & Supported & Supported & [50] \\ SCIATRAN & Ultraviolet\(\sim\)Near & Line by line & Supported & Supported & [51] \\  & Infrared & & & & \\ LBLRTM/FASCODE & Ultraviolet\(\sim\)Millimeter & Line by line & Unsupported & Unsupported & [52] \\  & Wave & & & & \\ VLIDORT & Ultraviolet\(\sim\)Microwave & / & Supported & Supported & [53] \\  & Ultraviolet\(\sim\)Microwave & / & Supported & Supported & [54] \\  & Visible Wave\(\sim\)Thermal & Line by line & Supported & Supported & [55] \\  & Infrared & & & & \\  & Near & & & & \\ RTTOV & Infrared\(\sim\)Thermal & Line by line & Supported & Unsupported & [56] \\  & Infrared & & & & \\ \hline \end{tabular}
\end{table}
Table 4: Common radiative transfer models.  
the radiative transfer model with the advantages of high precision, universality and quantitative estimation of errors; the empirical statistical methods, which have the advantage of high-speed computation, are generally used in inversion scenarios that have a high time limit and a lower demand for precision. Besides, new data-assimilation algorithms have been applied to satellite data inversion. Atmospheric CO\({}_{2}\) column concentrations obtained from CO\({}_{2}\) satellite observations and satellite remote sensing inversion algorithms, combined with atmospheric transport models and atmospheric inversion algorithms, have been used to assess terrestrial carbon sinks.