Abstract: Accurate and efficient monitoring of surface water bodies holds critical significance. To address the accuracy limitations of traditional water body extraction methods in processing mixed pixels, this study proposed a Google Earth Engine (GEE)-based method for estimating the water abundance from Landsat imagery. Specifically, the water body boundary information was extracted through stacked neural networks; the spectral and boundary features were jointly extracted using a pseudo-siamese network; the water abundance was finally estimated by integrating the multi-source features. The model was deployed on the GEE platform to enable online prediction, effectively avoiding the transmission and storage limitations commonly encountered in large-scale applications of traditional offline methods. Using the Landsat and GF-2 data from the Jianghan Plain, the proposed method was tested and compared with a linear regression model, a very deep super-resolution (VDSR) model, and a convolutional neural network (CNN) model without boundary information. The results show that compared to the above three models, the proposed method achieved an average reduction of 10.5% in the root mean square error (RMSE) and 14.5% in the mean absolute error (MAE), and an average improvement of 4.7% in the coefficient of determination (R²), while also significantly saving the data storage space and transmission time.