###### Abstract
Based on the MODIS spatial resolution of 250 m data, MERSI data add 3 bands to the resolution of this data, especially the thermal infrared data at 10.5-12.5 m in the first channel, which enables FY-3/MERSI images to detect the 250 m spatial resolution of some natural disasters such as typhoons, debris flow, and landslide. There are few studies on using FY-3/MERSI to extract surface waters, and the application of FY-3/MERSI data still has significant room for exploration. The parameters comparison between FY-3/MERIS and MODIS is shown in Table 1.  
In the water body extraction method, since the 1970s, increasingly more scholars have engaged in the exploration and analysis of the remote sensing applications of surface waters. The methods for classifying surface waters based on visible remote sensing data include the single-band threshold method, the multi-band method, the conventional land classification method, and the special water extraction method. Among the commonly used methods for extracting land surface water, the single-band threshold method and the multi-band method are simple to calculate and convenient to construct, but they have many topographic restrictions. For images in different regions and at different times, the threshold settings are different, and these two methods' adaptations are relatively poor [10; 11; 12; 13; 14; 15]. Conventional land classification methods, such as supervised classification, unsupervised classification, and the decision tree method, have a large workload in the process of building a \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**MODIS**} & \multicolumn{2}{c}{**FY-3/MERSI**} \\ \hline  & **Band Range (nm)** & **Spatial Resolution (m)** & **Band Range (nm)** & **Spatial Resolution (m)** \\ \hline
1 & 620–670 & 250 & 445–495 & 250 \\
2 & 841–876 & 250 & 525–575 & 250 \\
3 & 459–479 & 500 & 625–675 & 250 \\
4 & 545–565 & 500 & 840–890 & 250 \\
5 & 1230–1250 & 500 & 1000–12,500 & 250 \\
6 & 1628–1652 & 500 & 402–422 & 1000 \\
7 & 2105–2155 & 500 & 433–453 & 1000 \\ \hline Spectrum range (nm) & 400–14,400 & & 400–12,500 \\ \hline Number of bands & 26 & & 20 \\ \hline Ground resolution (km) & 0.25–1 & & 0.25–1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of moderate resolution imaging spectroradiometer (MODIS) and Fengyun 3 medium resolution spectral imager (FY-3/MERSI). relevant model. At the same time, it is necessary to adjust the conditions in the classification of images for different times and regions. Moreover, their adaptabilities are relatively poor. Surface water extraction methods make full use of the spectral characteristics of land surface water bodies. At present, the particle swarm optimization (PSO) technique is a new kind of evolutionary algorithm based on the observations of bird groups looking for food [16]. Currently, discrete particle swarm optimization (DPSO) has been fully used to extract land surface information from remote sensing images, in order to improve the integrity of the algorithm for water body extraction. PSO has intelligent characteristics of self-adaptation and self-organization, as well as the advantages of simple calculation, fast convergence, and strong stability [17; 18; 19]. Thus, as an intelligent optimization algorithm, PSO has a very strong ability to determine an optimal solution, which has been increasingly more introduced for the special classification of surface waters in remote sensing images. By combining the characteristics of DPSO with spectral information, [PERSON] et al. made full use of water information with fewer parameters to complete the classification process for land surface water and published the SMDPSO [20]. With the advantages of low parameter demand and strong portability, this method can be easily combined with more surface water features. Fengyun satellite data has good timeliness, which can help us to evaluate the flood invasion area in time and provide necessary guidance for disaster relief. [PERSON] et al.