自然资源遥感2026，Vol.38Issue(2)：70-78,9.DOI:10.6046/zrzyyg.2025061
GEE框架下融合边界信息的Landsat影像水体丰度估计
Estimating water abundance from Landsat imagery integrated with boundary information under the Google Earth Engine framework
摘要
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.关键词
GEE/深度学习/边界信息/Landsat影像/水体丰度Key words
Google Earth Engine(GEE)/deep learning/boundary information/Landsat imagery/water abun-dance分类
信息技术与安全科学引用本文复制引用
殷志祥,王子睿,吴鹏海,卢杰,凌峰..GEE框架下融合边界信息的Landsat影像水体丰度估计[J].自然资源遥感,2026,38(2):70-78,9.基金项目
国家自然科学基金项目"基于特征深度挖掘的河流水体遥感超分辨率制图研究"(编号:42201429)和安徽省自然科学基金优青项目"面向水环境监测的多源遥感定量信息时空谱一体化深度融合"(编号:2308085Y29)共同资助. （编号:42201429）
