# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 1 Introduction
In order to improve the discrimination of the cloud detection algorithm, many scholars have performed in-depth analyses of the characteristics of haze pixels. For example, after analyzing the spectral characteristics of cloud, fog, haze and clear pixels in visible-near infrared and infrared channels based on MODIS data, [PERSON] et al. (2016) selected multiple sensitive channels to calculate the threshold combination of the haze distribution, and further, they designed an automatic processing algorithm for haze detection [19]. [PERSON] et al. (2014) used MODIS secondary products for multi-day sampling to compare the difference in the cloud amount, cloud phase and cloud top pressure during clear days andheavy pollution conditions; a recognition algorithm combining the threshold values of these three parameters was established to distinguish the haze pixels from the cloud regions [20]. Based on VIIRS data, [PERSON] et al. (2017) selected cloud, clear sky and haze areas from more than 200,000 pixels of eastern China through visual interpretation, and then, they determined the thresholds of these two variables by fitting the histograms of the standard deviation changes of the three targets at 0.412 \(\upmu\)m and 0.47 \(\upmu\)m, respectively, thus finally establishing a fast cloud, haze and clear sky identification algorithm for AOD inversion [15]. [PERSON] (1998) believed that haze has a smaller optical thickness compared to that of clouds and is distributed on the surface. The digital elevation model (DEM) was added to retain the clear pixels above higher altitude mountains [21; 22]. By analyzing the radiation distribution characteristics of 36 bands of accurate and mis-detected cloud detection points from the MODIS satellite, a cloud detection correction algorithm for mis-detected cloud products in high pollution areas was constructed, which solved the problem of cloud over-identification from the perspective of studying cloud microphysical characteristics [7]. NDVI can be used not only to identify vegetation distribution, but it can also be used to identify clouds and clear skies. [PERSON] (2022) found that the sample with NDVI \(<\) 0.1 were treated as inland water, which is the main reason why the MODIS DT aerosol retrieval algorithm filters out haze areas [23]. Based on FY3D/MERSI-II data, [PERSON] (2022) added a reflectance value of 2.13 \(\upmu\)m, which is below 0.08, to this condition to recover the haze area for retrieval. This method was then adopted by the MODIS DT team to retrieve the haze area from MODIS [24]. In recent years, China's domestic satellite sensor detection technology has developed rapidly. The Fengyun series of satellites, which are resource satellites and ocean satellites, the Gaofen series of satellites, which are environmental satellites, etc., were launched successively, basically realizing the Earth observation system for multiple application fields, thus serving the business needs of various industries. As for the Fengyun satellite series, Fengyun 3 was successfully launched in May 2008 as a polar solar synchronous orbit satellite. After nearly ten years of development in FY-3A, FY-3B, FY-3C and FY-3D, the Fengyun 3 Earth Observation System has matured, and it is in the stage of stable operation [25]. In 2008, the first Moderate Resolution Spectral Imager (MERSI) on FY-3 indicated that domestic satellites could be used for the quantitative remote sensing inversion of land and ocean aerosols [26; 27]. In November 2017, Fengyun 3D was once again equipped with an upgraded version of the MERSI instrument, providing a solid data source for aerosol inversion [28; 29]. The MERSI load performance is comparable to that of MODIS, whose business products are relatively mature. The cloud detection products of MERSI-II are as same as those of MODIS, therefore, both lack of the ability to identify haze.