# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 4 Conclusions  
Cloud detection products based on FY3D/MERSI-II data have been released as operational products for many years, supplying cloud masks for the improved retrieval of atmospheric environmental products (such as aerosol and PM\({}_{2.5}\), etc. ), and they have continental surface parameters (such as the land surface temperature). For aerosol quantitative retrieval, the cloud detection products of MERSI have limits in their haze area identification. The absolutely clear and cloudy pixels can be distinguished from each other based on the fact that they have opposite characteristics in terms of reflectance in the visible channels and brightness temperature in the infrared channels. Combined with the actual patterns of the MERSI L1 data, the positive corresponding relationship between the apparent reflectance at 0.865 \(\upmu\)m and 1.64 \(\upmu\)m for the clear pixels over regions with a bright surface is proposed as an identification condition. Thus, this study proposes a novel MERSI haze mask (MHAM) algorithm that divides the pixels into one of three categories: cloudy, clear or haze ones, thereby resolving the problem of distinguishing haze pixels from the clear ones over a bright surface. In comparing the operational Aqua/MODIS and MERSI-II cloud detection products with the true color map, it reveals that these two products misclassified the heavy haze pixels as certain cloud, possible cloud and possible clear pixels, and they misclassified the pixels with light/moderate haze conditions as possible cloud, possible clear and certainly clear regions. In contrast, the novel algorithm proposed in this paper is able to capture the haze regions correctly, making it more compatible with the true color images. The real-time PM\({}_{2.5}\) data from the national air quality monitoring stations served as true values, and two thresholds were used to evaluate the new algorithm. The results showed that the novel algorithm has 72.22% (13 out of total 18 samples) accuracy when a PM\({}_{2.5}\) that was larger than 35 \(\upmu\)g/m\({}^{3}\) was considered as haze conditions, while for PM\({}_{2.5}\) values that were larger than 50 \(\upmu\)g/m\({}^{3}\), 83.33% and 66.67% of the total samples were examined with better identification rates than 85% and 90%, respectively, indicating that the greater the ground-based PM\({}_{2.5}\) value is, the better the consistency is in satellite haze identification. Through the cross-comparison with the method proposed by [PERSON] (2022) [23], it was found that the novel algorithm has a better sensitivity to bright surface clear sky areas and haze areas, and there will be more valid pixels when the subsequent quantitative inversion of aerosol characteristics is performed. Haze detection based on satellite remote sensing data is still in the research stage of recognition, and there has been no corresponding official product released so far, thus leading to great difficulties in the quantitative validation of the haze identification effect. With the definition of haze, a richer comparison between the accuracies of this identification method by using combining relative humidity and visibility data will be carried out. In addition, the haze identification method proposed in this study directly depends on the apparent reflectance and brightness temperature, that is, when the calibration coefficient is adjusted, the corresponding identification threshold also needs to be updated synchronously. We will monitor of the MERSI instrument data state and update the recognition threshold iteratively. Conceptualization, Y.S. and X.Z. ; methodology, Y.S. and L.C. ; software, Y.S. ; validation, Y.S. and L.Y. ; investigation, Y.S. and F.W. ; resources, Y.S. ; data curation, L.C., F.W., N.X. ; writing--original draft preparation, Y.S. ; writing--review and editing, L.C., Z.Z., L.Y., F.W., N.X. and X.Z. ; visualization, Y.S. ; supervision, L.C., N.X. and X.Z. ; project administration, Y.S., F.W., L.C. ; funding acquisition, Y.S. and F.W.