## 8 Conclusions
Considering the feasibility and generality of the aerosol model for all aerosol types and land surface types, the aerosol parameters of AOD, H\({}_{\text{p}}\), H\({}_{\text{w}}\), \(\
u_{\text{eff}}\)\({}^{\text{f}}\), \(r_{\text{eff}}\)\({}^{\text{c}}\), \(a_{\text{r}}\)\({}^{\text{f}}\) and \(a_{\text{r}}\)\({}^{\text{c}}\) are recommended to be retrieved simultaneously with CO\({}_{2}\), except in the case of MA, which only requires the retrieval of five parameters (AOD, H\({}_{\text{p}}\), H\({}_{\text{w}}\), \(r_{\text{eff}}\)\({}^{\text{c}}\) and \(a_{\text{r}}\)\({}^{\text{c}}\)). If additional information on aerosol properties could be added from other sources to initially distinguish DD from other aerosol types, two microphysical parameters of coarse mode aerosol (\(r_{\text{eff}}\)\({}^{\text{c}}\) and \(b_{\text{r}}\)\({}^{\text{c}}\)) could be retrieved with fewer XCO\({}_{2}\) errors for DD, which would be in place of the fine particle parameters (\(\
u_{\text{eff}}\)\({}^{\text{f}}\) and \(b_{\text{r}}\)\({}^{\text{f}}\)) for UI and BB. Overall, the retrieved aerosol parameters in the optimal aerosol model for CO\({}_{2}\) retrieval are determined with fewer aerosol-induced XCO\({}_{2}\) errors and still yield large amounts of CO\({}_{2}\) information. In the next step, the performance of this optimal aerosol model will be verified by running the actual CO\({}_{2}\) retrieval based on the IAPCAS algorithm structure from GOSAT, OCO-2 or TanSat observations. The methods and rules applied to optimize the aerosol model in this study may also be applicable to CO\({}_{2}\) retrieval from other satellites. This research forms a basis for enhancing the accuracy of high-precision CO\({}_{2}\) retrieval algorithms. The optimal aerosol model proposed here may be applied to future CO\({}_{2}\) retrievals based on realistic TanSat data. The following are available online at [[http://www.mdpi.com/2072-4292/11/9/1061/s1](http://www.mdpi.com/2072-4292/11/9/1061/s1)]([http://www.mdpi.com/2072-4292/11/9/1061/s1](http://www.mdpi.com/2072-4292/11/9/1061/s1)), Figure S1: The Jacobians with respect to all BRDF kernel coefficients, Figure S2: The comparison of the interference error from each aerosol parameter between over vegetation and soil. Figure S3. The comparison of DFS of each aerosol parameter for four aerosol types over vegetation and soil. Figure S4. The SNR curves examined in the laboratory for each band of TanSat ACGS. Figure S5. The surface reflectivity estimated by Ross-Li BRDF model at different observation geometries. Conceptualization, X.C. and D.Y. ; Methodology, Y.L. and D.Y. ; Software, Z.C. and D.Y. ; Validation, X.C. and D.Y. ; Formal Analysis, X.C. ; Investigation, X.C. and D.Y. ; Resources, M.W. ; Data Curation, X.C. ; Writing-Original Draft Preparation, X.C. ; Writing-Review & Editing, D.Y. ; Visualization, X.C. ; Supervision, H.C.; Project Administration, M.W. ; Funding Acquisition, Y.L. This research was funded by the National Key R&D Program of China (No. 2017 YFB0504001), the Key Research Program of the Chinese Academy of Sciences (ZDRW-ZS-2019-1), the China Postdoctoral Science Foundation, the National Key R&D Program of China (No. 2016 YFA0600203), and the External Cooperation Program of the Chinese Academy of Sciences (Grant No. GJHZ1507). **Acknowledgments:** The authors would like to thank PI and CI of AERONET four sites: [PERSON] of Beijing_RADI site, [PERSON] and [PERSON] of Alta_Floresta site, [PERSON] of Capo_Verde site and [PERSON], [PERSON] and [PERSON] of Lanai site, for providing measurement data. They also thank TanSat mission and team for supporting this work. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.