# A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data
## 3 Results
### Validation against PM\({}_{2.5}\) Measurements  
As we all know, ground-based stations measure the air quality near the surface, while satellites observe the overall state in the troposphere. There are differences between these observation methods. At present, there is no unified standard, that is, when the PM\({}_{2.5}\) values measured by ground observation are greater than the threshold, it is consistent with the haze determined by the satellite. Therefore, this paper uses the two thresholds of 35 \(\upmu\)g/m\({}^{3}\) and 50 \(\upmu\)g/m\({}^{3}\) of the existing PM\({}_{2.5}\) levels to test the MERSI haze identification effect. Firstly, the 18 MERSI orbits covering eastern China during the haze season were selected, which were mainly concentrated at 12-14 PM Beijing time. The national observation  
Figure 9: Aqua/MODIS cloud mask (left) and MERSI operational cloud mask (right), these three lines represent the distribution on 2 December 2019, 9 February 2020 and 19 October 2020, respectively. PM\({}_{2.5}\) data during the same period were downloaded, and the samples with a PM\({}_{2.5}\) that was larger than 35 \(\upmu\)g/m\({}^{3}\) and 50 \(\upmu\)g/m\({}^{3}\) were respectively screened out for validation. Secondly, the spatial and temporal matching between the ground-based and satellite observations was performed. In the terms of the temporal scale, we chose the PM\({}_{2.5}\) value with the nearest hour to the MERSI orbit, for example, the transit time of MERSI is UTC0515, so we chose PM\({}_{2.5}\) data at 13:00 Beijing time. Spatially, taking the national station as the center, the MERSI pixels falling within a radius of 500 m were selected. If there were multiple pixels, then the nearest one was selected. Thirdly, the pixels identified by MERSI as cloud, ice/snow, inland waterbody ones were further removed from the matched samples, and only the haze and clear pixels were retained. Finally, the ratio of the number of samples identified as haze to that of the sum of the samples identified as haze and clear pixels were calculated to demonstrate the accuracy. Table 4 summarizes the test results based on the selected MERSI orbits. As it can be obviously seen in Table 4, the haze identification rate with a PM\({}_{2.5}\) threshold of 50 ug/m\({}^{3}\) is generally better than that with a PM\({}_{2.5}\) threshold of 35 \(\upmu\)g/m\({}^{3}\). When PM\({}_{2.5}\) > 35 \(\upmu\)g/m\({}^{3}\) was considered to be haze days, 13 samples with an identification rate that was larger than 85% accounted for 72.22% of the total samples (18), and 61.11% of the sample points (11) had an identification rate that was above 90%. When ground-based PM\({}_{2.5}\) > 50 \(\upmu\)g/m\({}^{3}\) was considered as haze days, 83.33% and 66.67% of the samples had identification rates that were higher than 85% and 90%, respectively, indicating that the greater the ground-based PM\({}_{2.5}\) value is, the better the consistency is of the satellite identification of haze. Taking some orbits as an example, Figure 10 shows the true color map and the MERSI identification results overlapping it with the PM\({}_{2.5}\) values that were observed by the national stations of UTC0505 on 20 January 2020, UTC0450 on October 19 and UTC0525 on October 22, respectively, covering the scenarios of cloudy, haz  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Date/Orbit**} & \multicolumn{3}{c}{**PM\({}_{2.5}\) >= 50 \(\upmu\)g/m\({}^{3}\)**} & \multicolumn{3}{c}{**PM\({}_{2.5}\) >= 35 \(\upmu\)g/m\({}^{3}\)**} \\ \cline{2-7}  & **Haze** & **Clear** & **Hit Rate (\%)** & **Haze** & **Clear** & **Hit Rate (\%)** \\ \hline
**14 January 2020-0430** & 48 & 2 & 96.00 & 76 & 5 & 93.83 \\ \hline
**19 January 2020-0435** & 163 & 2 & 98.79 & 195 & 6 & 97.01 \\ \hline
**20 January 2020-0555** & 145 & 42 & 75.94 & 190 & 66 & 74.21 \\ \hline
**28 January 2020-0505** & 201 & 33 & 85.  
different surface types. For the MERSI identification results, cloud, snow, ice and inland water were combined into masks, and they are marked with yellow.