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The acquisition of surface water information is of great significance of the study of global change. In this study, the Koppen climate zone was firstly used to select sample data onto different climate zones around the world, and then water and non-water samples were collected for each sample. Get as complete a sample of information as possible worldwide. Deep Neural Networks method is adopted to improve the model building, The features data is FY3D/MERSI, containing 6 bands 250m resolution data, add the NDVI and EVI to enhance characteristic data, use the attention mechanism to enhance the model, to strengthen the adaptability for different regions, the model for predicting data using global standardization process. The results show that this method can effectively extract the information on land surface water, especially for different disturbance conditions such as cloud and shadow under cloud. Water tests for 33 different regions of the world show that the method of sample set and model can adapt to different regions of the world.

Water body extraction can help eco-environmental policymakers to intuitively grasp surface water resources. Remote sensing technology can accurately and quickly extract surface water information, which is of great significance for monitoring surface water changes. Fengyun satellite images have the advantages of high time resolution and multispectral bands. This provides important image data suitable for high-frequency surface water monitoring. Based on Fengyun 3 medium resolution spectral imager (FY-3/MERSI) data, 7 methods were applied in this study, which include single-band threshold method, water body index method, knowledge decision tree classification method, supervised classification method, unsupervised classification method, spectral matching based on discrete particle swarm optimization (SMDPSO), and improved spectral matching based on discrete particle swarm optimization with linear feature enhancement (SMDPSO+LFE). These methods were used to extract the land surface water of Poyang Lake, check the samples from the Landsat image with similar times to the FY-3 images, and calculate the classification accuracy via the confusion matrix. The results showed that the overall classification accuracy (OA) of the SMDPSO+LFE is 97.64%, and the Kappa coefficient is 0.95. To analyze the stability of the surface water extracted by SMDPSO+LFE in different regions, this paper selected eight test sites with different surface water types, landscapes, and terrains to extract surface water. Based on an analysis of the land surface water results at the eight test sites, every OA in the eight sites was higher than 94.5%, the Kappa coefficient was greater than 0.88. In conclusion, the SMDPSO+LFE is found to be the most suitable method among the 7 methods and effectively distinguish between different surface water bodies and backgrounds with good stability.