# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 2 Materials and Methods
### MHAM Algorithm
#### 2.3.2 Selection of Spectral Characterization Based on FY3D/MERSI-II  
In this analysis, the apparent reflectance at 0.65 \(\upmu\)m (R\({}_{0.65}\)), the brightness temperature at 11 \(\upmu\)m (BT\({}_{11}\)) and the difference between BT\({}_{11}\) and BT\({}_{3.8}\) (BT\({}_{11}\)-BT\({}_{3.8}\)) were selected to separate the cloudy and clear skies according to different thresholds. It should be emphasized that 0.65 \(\upmu\)m of atmospheric passage was selected in this paper, instead of using 0.47 \(\upmu\)m of it, as was used in the MODIS aerosol algorithm. This is because the molecular scattering contribution is larger at large observation angles, and the apparent reflectance of clear sky pixels is larger than that of some haze pixels. Assuming that the solar zenith angle is 30\({}^{\circ}\), the relative azimuth is 72\({}^{\circ}\), the aerosol model is defined as the continental aerosol type in the mid-latitude summer and the satellite zenith angle ranges from 0 to 80\({}^{\circ}\), Figure 3a,b presents the variation of molecular scattering under different aerosol loadings with the satellite zenith angles. When the observed angles are smaller than 50\({}^{\circ}\), the contribution of molecular scattering changes modestly, whereas the molecular scattering increases significantly when the satellite zenith angles change from 50\({}^{\circ}\) to 80\({}^{\circ}\). The scattering amplitude of the molecules in the red channel is less than 1/3 of that which occurs in the blue channel. The aerosol optical thickness decreases with the increase in the wavelength. As shown in Figure 3c,d, the variation trend of the aerosol scattering signal in the red band with observation angles is consistent with that of the blue band, and it is still sensitive to different aerosol contents. If 0.47 \(\upmu\)m is used as the reflectance identification condition, it is easy to misidentify some clear sky pixels as haze ones. Therefore, it is feasible to select 0.65 \(\upmu\)m as the identification condition in this paper.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Classification** & **Group1** & **Group2** \\ \hline ice/snow & 1 & NDSI \textgreater{} 0.4 \& R\({}_{0.865}\) \textgreater{} 0.1 & / \\ \hline Inland waterbody & 2 & NDVI\({}_{\text{fair}}\) \textless{} 0.4 \& R\({}_{2.13}\) \textless{} 0.08 & / \\ \hline \multirow{4}{*}{cloud} & R\({}_{0.65}\) \textgreater{} 0.45 [_defined as cloud\_c1_] & \\ \cline{2-3}  & R\({}_{0.47\_{\text{sid}}}\) \textgreater{} 0.0075 \& R\({}_{0.65}\) \textgreater{} 0.4 & \\ \cline{2-3}  & [_defined as cloud\_c2_] & / \\ \cline{2-3}  & BT\({}_{11}\) \textless{} 250 K [_defined as cloud\_c3_] & \\ \hline \multirow{4}{*}{clear} & \multicolumn{3}{c}{0 \textless{} R\({}_{0.65}\) \textless{} 0.2 [_defined as clear\_c1_]} \\ \cline{2-3}  & \multicolumn{3}{c}{0 \textless{} Diff\({}_{0.865}\) \textless{} 1.64} \textgreater{} 0 [_defined as clear\_c2_]} \\ \cline{2-3}  & \multicolumn{3}{c}{BT\({}_{11}\) \textgreater{} 285 [_defined as clear\_c3_]} \\ \cline{2-3}  & \multicolumn{3}{c}{BT\({}_{11}\)-BT\({}_{3.8}\) [\(-\)50, \(-\)40] [_defined as clear\_c4_]} \\ \cline{2-3}  & \multicolumn{3}{c}{3 NDVI\({}_{\text{svirr}}\) \textless{} 0.2 \& \(\textless{}\) 0.2 \& \(\textless{}\) R\({}_{0.65}\) \textless{} 0.4 \\ \cline{2-3}  & \multicolumn{3}{c}{_[defined as clear\_c5]_} \\ \hline \hline \end{tabular} 1  
\end{table}
Table 2: Details thresholds for MHAM algorithm using FY3D/MERSI-II observations.