# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 2 Materials and Methods
### MHAM Algorithm
#### 2.3.3 Identification of Clear Conditions over Bright Surface  
It is difficult to distinguish the clear pixels over the bright surface from the haze conditions due to them having similar spectral characteristics. The accuracy of haze detection could be heavily improved provided that the clear conditions over the bright surfaces can be accurately identified [21]. Through visual interpretation, the scenes of cloud, clear and haze regions on different underlying surfaces were selected, including spring, summer, fall and winter ones. The selected regions were kept constant to reduce the complexity of the scenes. The selected region changed only when the study region during a season was no longer pure. Details about the regions are summarized in Table 3. Inner Mongolia was selected as the clear region with a bright surface, which is defined as clear1. The area in Guangdong province was selected as clear2. The two cloud scenarios were selected to be located in Sichuan and Shaanxi, respectively. The Hebei and Henan provinces, where haze occurs frequently, were selected as the two haze scenarios. We extracted the apparent reflectance values at eight bands of each scenario, and we averaged them by region and season. Figure 4 shows the variation trend of spectral reflectance in each scenario. A similar pattern appeared in two cloudy scenarios, but with different apparent reflectance values. The reflectance of different bands in clear2, the haze1 and haze2 showed similar variation. Surprisingly, in clear1, the apparent reflectance of the B6 and B7 channels are larger than those of the B4 and visible channels, which is contrary to that of the cloudy and haze pixels. Haze often contains absorptive components, which will attenuate the apparent reflectance after absorbing an amount of solar radiation. As we know, 1.64 \(\upmu\)m and 2.13 \(\upmu\)m were often used to study the characteristics of the parameters over land. For the clear pixels, 2.13 \(\upmu\)m is only a little bit affected by the atmosphere, and the apparent reflectance is almost equal to the surface reflectance. Compared with 1.64 \(\upmu\)m, the total apparent reflectance at 2.13 \(\upmu\)m may be smaller than the surface reflectance with the enhancement of the atmospheric signal. As described as Section 2.3.1, visible wavelengths are susceptible to molecular scattering, therefore, the near-infrared channel (B4) and the B6 channel were selected to construct the reflectance variation for identifying the clear pixels over bright surfaces. Non-bright surface  
Figure 3: Variation of the apparent reflectance at two bands from molecule scattering **(a,b)** and aerosol scattering **(c,d)** as simulated by 6 SV; the left panel stands for the variation in blue the channel, and the right one denotes in the red channel. Different colors in sub-panels **(a,b)** represent satellite zenith angles, and those in sub-panels **(c,d)** represent aerosol optical thickness. clear skies (clear2) are usually well identified when one is using a reflectance value that is below a certain threshold. If the cloud pixel coverage area can be accurately identified, clear skies and haze can be distinguished from the remaining samples. To confirm that whether the above parameter is reasonable, Figure 5 shows the difference of between R\({}_{0.865}\) and R\({}_{1.64}\) on 8 November 2019 and 1 December 2019, and a difference that is larger than 0 is masked in the white color. Combined with the true color maps, this parameter is sensitive to the clear sky with the bright surface. Therefore, this parameter can be added to the recognition process to further reduce the misjudgment of clear pixels over bright surfaces. Figure 4: The spectral variation of different pixels over different underlying surfaces using MERSI-II apparent reflectance data. Different colors correspond to the scenarios in Table 2.