GEE框架下融合边界信息的Landsat影像水体丰度估计OA

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.