Abstract: Coastal aquaculture provides human beings with high-quality animal protein, which is of great significance to the high-quality development of the marine economy. Satellite remote sensing technology has been widely applied in aquaculture monitoring, but most research focuses on a sole sensor and specified processing method with no comparisons of other sensors or processing methods. This paper selected the northeast part of Beili Island, Guangdong Province, as the study region, used adaptive thresholding, support vector machine (SVM) supervised classification method, and multi-scale segmentation object-oriented classification method to identify aquaculture ponds based on Sentinel-2A and Gaofen-1B (GF-1B) multi-spectral data. Results showed that the higher spatial resolution GF-1B satellite data was far superior to the Sentinel-2A satellite data in view of the pond identification accuracy, especially in areas of densely populated aquaculture water networks. Based on the high spatial resolution GF-1B satellite imagery, the multi-scale segmentation object oriented classification method obtained the highest detection accuracy of 94.65%, which was better than 94.45% from the SVM method, and 84.62% from the adaptive thresholding method. The adaptive thresholding method was more suitable for aquaculture pond extraction based on medium spatial resolution satellite data. The difference of aquaculture water surface areas detected by adaptive thresholding and visual interpretation was less than 4%. Thus, high spatial resolution satellite data is required for operational monitoring of every single aquaculture pond, while medium spatial resolution satellite data is suitable for analysis of large-scale change of aquaculture areas.