Abstract: In recent years, the rapid expansion of the aquaculture pond industry has given rise to a series of ecological and environmental issues. The Jianghan Plain is recognized as one of the most important freshwater aquaculture bases in China, and investigating changes in its aquaculture ponds is crucial for China’s ecological conservation. Focusing on the Jianghan Plain, this study proposed a method for extracting and monitoring changes in aquaculture ponds using Google Earth Engine (GEE) and Sentinel-2 dense time-series images. Using this method, which combined K-means clustering and a hierarchical decision tree classification algorithm, this study achieved accurate information extraction and spatiotemporal pattern analyses of aquaculture ponds in the plain in each year from 2016 to 2022. The results indicate that the combination of K-means and the hierarchical decision tree algorithm that integrated time-varying features allowed for accurate classification of aquaculture ponds, with an overall classification accuracy of 91.90% and a Kappa coefficient exceeding 0.84. In 2022, the aquaculture pond area of Jianghan Plain is 2 126.43 km². Among these area of aquaculture ponds, 43.24% were concentrated in Jingzhou City, while Yichang City had the fewest area of aquaculture ponds, accounting for only 0.76%. From 2016 to 2022, aquaculture ponds in the Jianghan Plain exhibited an upward trend overall and dynamics with pronounced spatial heterogeneity. Specifically, the total area increased to 2 126.43 km² from 1 947.43 km², increasing by 9.19%. The proposed methodology provides an important reference for the precise monitoring of aquaculture ponds, and the resulting dataset serves as a valuable reference and holds great practical significance for the ecological conservation and the assessment of sustainable development goals in the Jianghan Plain.