## 3 Results and Validation
### Validation
Because they lack overseas satellite data-receiving stations, the Chinese satellites can only cover China and its surrounding areas; subsequently, for GF1/WFV imagery, only 81 images from 2018 to 2019 are retrieved at Dalanzadgad site alone and only 41 corresponding AOD measurements from Dalanzadgad AERONET site are collected. The comparison between the retrieved AOD and the observed AOD is shown in the bottom left plot of Figure 6. However, the R\({}^{2}\) value is only 0.5897, the AOD discrepancy is lower than 0.05, which is very small. The mean of retrieved AOD and the RMSE are 0.0895 and 0.0301 respectively. Because Dalanzadgad is far away from cities, the aerosol loading is very small and very small errors will bring very high uncertainty, which will degrade the correlation; subsequently, the RMSE is a better indicator for retrieving accuracy evaluation at this site instead of R\({}^{2}\) value. All the data from all the three sensors and all the four sites are plotted in the bottom right plot in Figure 6 and it shows a very good agreement between the observed and the retrieved AOD. The R\({}^{2}\) value is very close to 0.8 and the RMSE is 0.0907. The validation shows that AOD derived from the proposed method and the ground measurements from AERONET sites agree very well. The results from Landsat/OLI are the best among the three sensors and the reason is that Landsat/OLI is very close to nadir viewing. In addition, the data lacking at the other three sites of GF1 WFV degrades its validation and further investigation will be carried out in near future. Figure 6: The validation of the retrieved AOD using 4 AERONET sites’ measurements around the world. The plots from the top left to the bottom right are the validation for (**a**) Landsat/OLI, (**b**) Sentinel2/MSI, (**c**) GF-1/WFV, and (**d**) all images from all the three sensors, respectively. In order to better evaluate the performance of the proposed method, the validation for the AOD from the MODIS algorithm using ground-measured AOD from the above 4 AERONET sites has been done. For MODIS aerosol products over land, there are two algorithms: The dark target (DT) [8] and the deep blue (DB) [10] algorithms. The DT method assumes that there is a dark target area on the remote-sensing image to be corrected, the surface of this dark area has Lambert reflection characteristics, and the atmospheric condition of this dark area is uniform. On the premise of neglecting the multiple scattering of the atmosphere and the diffuse reflection of neighboring pixels, the reflectance of the dark target increases due to the influence of atmosphere, which is considered to be the effect of atmospheric range radiation. The DT method depends on the surface that is covered by dark targets, such as dense vegetation, water body, etc., which make the method doesn't suitable for other surfaces. The DB method uses the characteristics of strong atmospheric reflection and weak surface reflection in the blue band. The aerosol can be retrieved by substituting the surface reflectance of a clean day into the radiative transfer model. The DB method can be applied to most of the Earth's surface, such as vegetation, bare soil, water and even desert. For bright surfaces, the DT method usually fails and the DB method succeeds in retrieving aerosol information. In addition, the DT and DB have been combined (DB+DT) to refine the retrieved AOD and the AOD from the DB+DT method is more stable and accurate than that from the DB method only; subsequently, the retrieved AOD from the DB+DT method is used to undertake the validation. The left plot in Figure 7 shows the validation using all the retrieved AOD from both Aqua and Terra and all the validated AERONET sites, and the major indicators are the R2 value (0.7643) and the RMSE (0.1181), which shows that the proposed method with higher R2 value (0.7958) and lower RMSE (0.0907) is slightly better than the MODIS DB+DT method.