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 

机构地区：[1]School of Geography and Information Engineering,China University of Geosciences,Wuhan,People’s Republic of China [2]National Engineering Research Center of Geographic Information System,China University of Geosciences,Wuhan,People’s Republic of China [3]School of Geodesy and Geomatics,Wuhan University,Wuhan,People’s Republic of China [4]School of Resources and Environmental Science,Wuhan University,Wuhan,People’s Republic of China

出　　处：《International Journal of Digital Earth》2023年第1期210-233,共24页国际数字地球学报（英文）

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

摘　　要：Efficient and continuous monitoring of surface water is essential for water resource management.Much effort has been devoted to the task of water mapping based on remote sensing images.However,few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner.Moreover,water area statistics are sensitive to the satellite image observation quality.This 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 products.The 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 space.To reduce the uncertainty of the derived water occurrences,temporal correction was applied to repair the classification maps with invalid observations.Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results.Comparative 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.

关 键 词：Water mapping automatic training samples temporal correction Google Earth Engine 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]