## 3 Methodology  
### Pre-Process  
A method that can be used to improve the geometric errors of the FY-3/MERSI image was adopted. According to the image characteristics of the FY-3/MERSI image, four corresponding error categories (90% control point error (CE90), plane geometric error (RMSE), meridional geometric error (RMSE), and zonal geometric error (RMSE)) were determined to calculate the geometric error of the FY-3/MERSI image. Firstly, the image registration in the FY-3/MERSI research area was confirmed via the specific registration points and geometric error analysis, and then the geometric error correction was carried out for the other time-series images in the research area. Lastly, the radiometric calibration was completed.  
The research area of Poyang Lake in this paper is relatively small, so the regional shape of the research area was cut out from the FY-3/MERSI image. Meanwhile, the Landsat@ OLI image for accuracy verification is obtained from the same area [10,12,28]. In the stability analysis of the surface water extracted by the relatively most accurate water extraction method, the corresponding study area images were also selected from the FY-3/MERSI images.  
The data in this paper used remote sensing image automatic matching technology to perform control point matching processing on the FY-3/MERSI satellite remote sensing data that has been systematically geometrically corrected. Then FY-3/MERSI data were used to determine its geometric positioning error according to the control point of the same name and perform detailed errors on a single image verification and analysis.  
In this study, Level 1a data, which is unprocessed instrument data with geo-referenced and time-referenced parameters [29], was applied, and the geometric correction was performed based on the latitude and longitude data it provides. The geometric correction in this process is a systematic geometric correction. Then the data was mosaicked and trimmed according to the spatial scope of the study area to generate data of spatial extent covered the study area.  
In this pre-processing, Proba-V100m image data is selected as the reference image for analyzing the positioning accuracy of FY-MERSI image data. Using computer artificial intelligence processing methods, automatically find the same control points of the reference image and the image to be processed, the most important of which is the corner detection operator. There are many types of image matching operators; image matching based on image features is the most common operator. Among the image matching operators based on point features, the Moravec corner detection algorithm is one of the earliest corner detection algorithms. The algorithm detects each pixel of the image, treats a certain window around the pixel as a block, and detects the correlation between this block and other surrounding blocks. This correlation is measured by the sum of the squared differences between two blocks. The smaller the value, the higher the similarity. The image matching in this paper is realized under the support of ENVI. The ENVI software provides Moravec and Forster operators to find the same control points. In this process, the Moravec operator is selected and the correlation coefficient is set to 0.95 to determine the effective control points [30].