Optical and radar remote sensing images have been increasingly utilized to delineate aquaculture areas [11; 12], and many methods have been developed for local [13], regional [14], and national scale [15] aquaculture mapping. Meanwhile, due to the periodic repeated observations of satellites, remote sensing images were used not only to map aquaculture areas at a single time [16], but also to map their time series distributions [17]. Previous studies can be roughly summarized into two categories according to basic mapping units: pixel-based and object-based approaches. The pixel-based methods are widely applied to the images with low and medium spatial resolutions. For example, artificially designed spectral and textural features were computed for each pixel, and a supervised machine learning classifier was used to map large scale aquaculture areas [18]; [PERSON] et al. [19] applied a wavelet-based filter for detecting inland-aquaculture areas from MODIS time series images, and deep-learning-based methods were used for aquaculture classification [20; 21]. For the object-based classification method [22; 23; 24], images are segmented into many homogeneous segments, which are further classified through machine learning classifiers or classification rules derived from expert rules. For example, [PERSON] et al. [25] segmented Landsat images into objects using multi-resolution segmentation method to extract raft-type aquaculture areas. Considering data sources, most studies used only optical images for they are visually intuitive and easy to be understood. For mapping aquaculture facilities over small areas, high spatial resolution remote sensing images are frequently applied [26; 27], whereas medium resolution images are generally used for mapping aquaculture facilities at a regional or national scale due to their wide coverage and better spectral resolution. For example, [PERSON] et al. [28] combined Landsat series images and an object-based classification method to map the spatiotemporal distribution of aquaculture ponds in China's coastal zone. Synthetic aperture radar (SAR) images are also used for aquaculture mapping [14; 29]. For examples, [PERSON] et al. [29] detected floating raft aquaculture from SAR image using statistical region merging and contour feature; [PERSON] et al. [14] employed time series Sentinel-1 images and object-based approach to map aquaculture ponds over river basins; and [PERSON] et al. [30] mapped marine raft aquaculture areas using a deep learning approach by enhancing the contour and orientation features of Sentinel-1 images. Most studies applied satellite images at a certain time to map aquaculture areas, which may affect the accurate mapping of aquaculture areas for their dynamics. Although the influence of weather could be suppressed by carefully selecting images, other factors may also affect the accurate mapping of aquaculture areas. For example, some paddy fields are still water-dominated at the early stage of farming, and inland aquaculture ponds are drained during harvest time. Previous studies demonstrated that more accurate aquaculture maps could be obtained by using time series SAR images [31], and time series optical images was also proved to be effective in improving mapping accuracy [32; 33]. Therefore, time series images are an ideal and reliable data source for aquaculture mapping. The Google Earth Engine (GEE) platform provides a series of free remote sensing images, many kinds of image processing algorithms, and high-performance computing capabilities, and it can process huge amount of time series remote sensing images over a large-scale area [34; 35; 36]. Therefore, GEE has been widely used for mapping wetlands [37] and agricultural lands [38], and it also has the potentials to map aquaculture areas [39; 40; 41; 42]. For examples, [PERSON] et al. [39] proposed a framework for extracting aquaculture ponds by integrating existing multi-source remote sensing images on the GEE platform; [PERSON] et al.