A fully automatic and high-accuracy surface water mapping framework on Google Earth Engine using Landsat time-series

作     者：Linwei Yue Baoguang Li Shuang Zhu Qiangqiang Yuan Huanfeng Shen 

作者机构：School of Geography and Information EngineeringChina University of GeosciencesWuhanPeople’s Republic of China National Engineering Research Center of Geographic Information SystemChina University of GeosciencesWuhanPeople’s Republic of China School of Geodesy and GeomaticsWuhan UniversityWuhanPeople’s Republic of China School of Resources and Environmental ScienceWuhan UniversityWuhanPeople’s Republic of China 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2023年第16卷第1期

页      面：210-233页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China[grants numbers 42171375 and 41801263] 

主　　题：Water mapping automatic training samples temporal correction Google Earth Engine 

摘      要：Efficient and continuous monitoring of surface water is essential for water resource *** effort has been devoted to the task of water mapping based on remote sensing ***,few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic ***,water area statistics are sensitive to the satellite image observation *** study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine(GEE)with a supervised random forest classifier.A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence *** samples for permanent and seasonal water were mapped and collected separately,so that the supplement of seasonal samples can increase the spectral diversity of the sample *** reduce the uncertainty of the derived water occurrences,temporal correction was applied to repair the classification maps with invalid *** experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping *** tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods,as well as the GSWD and GLAD datasets.