# Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data
## 2 Data and Methods
### Data and Method for Validation  
In this study, a validation of AOD estimations was carried out with the surface measurement data from four sites of the Aerosol Robotic Network (AERONET) in Beijing. Figure 7 shows the geolocations of these four sites (the latitudes and longitudes are shown in Table 4). As a federation of ground-based aerosol measurement networks ([[https://aeronet.gsfc.nasa.gov/](https://aeronet.gsfc.nasa.gov/)]([https://aeronet.gsfc.nasa.gov/](https://aeronet.gsfc.nasa.gov/))), AERONET uses CE-318 automatic solar photometers to measure direct and scattered radiation from the sun. These photometers provide near-real-time observational data, such as globally distributed aerosol spectral optical depth and aerosol particle size distribution [26], which can retrieve optical parameters such as single scattering albedo, asymmetry factors, and scattering phase functions of aerosol particles [27]. Less influenced by surface parameters and aerosol forward scatter [28], these photometers have an AOD acquisition accuracy of 0.02-0.01 [29] and are widely used in verifying AOD estimations. The data can be divided into the unscreened Level 1.0 product, the cloud-detected Level 1.5 product, and the Level 2.0 product, which has undergone cloud detection and quality control.  
In this study, the AOD retrieved from satellite observations was the AOD at 550 nm. However, the AERONET does not contain observations at this wavelength, and interpolation needed to be conducted [25]:  
\[\tau(\lambda)=\beta\cdot\lambda^{\alpha}, \tag{16}\]  
where \(\tau\) is the AOD at wavelength \(\lambda\), \(\alpha\) is the Angstrom index, and \(\beta\) is the atmospheric turbidity coefficient.  
Four statistical indicators, namely root mean square error (RMSE), mean absolute error (MAE), and expected error (EE), were used to validate and analyze the inversion results. Meanwhile, EE represents the percentage of retrieved results within the envelope of the expected error region, defined as  
\[\Delta\tau=\tau_{i}\pm(0.05+0.20\tau_{i}), \tag{17}\]  
where \(\Delta\tau\) is the envelope of the expected error region (error lines), and \(\tau_{i}\) is the AOD measured by the AREONET sites.  
Figure 7: Spatial distribution of the Aerosol Robotic Network (AERONET) sites in Beijing.  
In this study, two approaches were carried out for validation of the AODs retrieved from HJ-1 A/B CCD data. In validation approach I, in situ measurements of four AERONET sites were all used for validation. In validation approach II, the simplified aerosol retrieval algorithm (SARA) approach [30; 31; 32] was adopted to improve the quality of the retrievals. In this approach, parts of the AERONET measurement data were used as an input for obtaining an optimal offset step \(i\), and the other measurements were used for validation. In the year 2012, the Beijing_CAMS site was used as an input, and the Beijing, Beijing_RADI, and XiangHe sites were used for validation; in the year 2013, the XiangHe site was used as an input, and the Beijing, Beijing_RADI, and Beijing_CAMS sites were used for validation; in the year 2014, the Beijing site was used as an input, and the XiangHe, Beijing_RADI, and Beijing_CAMS sites were used for validation; and in the year 2015, the Beijing_CAMS site was used as an input, and the Beijing, Beijing_RADI, and XiangHe sites were used for validation.