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

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.

Global DEMs vary from one to another:an evaluation of newly released Copernicus,NASA and AW3D30 DEM on selected terrains of China using ICESat-2 altimetry data

A good understanding of the quality of digital elevation model(DEM)is a perquisite for various applications.This study investigates the accuracy of three most recently released 1-arcsec global DEMs(GDEMs,Copernicus,NASA and AW3D30)in five selected terrains of China,using more than 240,000 high-quality ICESat-2(Ice,Cloud and land Elevation Satellite)ALT08 points.The results indicate the three GDEMs have similar overall vertical accuracy,with RMSE of 6.73(Copernicus),6.59(NASA)and 6.63 m(AW3D30).While the accuracy varies considerably over study areas and among GDEMs.The results show a clear correlation between the accuracy and terrain slopes,and some relationship between the accuracy and land covers.Our analysis reveals the land cover exerts a greater impact on the accuracy than that of the terrain slope for the study area.Visual inspections of terrain representation indicate Copernicus DEM exhibits the greatest detail of terrain,followed by AW3D30,and then by NASADEM.This study has demonstrated that ICESat-2 altimetry offers an important tool for DEM assessment.The findings provide a timely and comprehensive understanding of the quality of newly released GDEMs,which are informative for the selection of suitable DEMs,and for the improvement of GDEM in future studies.