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Time-series surface water reconstruction method(TSWR)based on spatial distance relationship of multi-stage water boundaries

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International Journal of Digital Earth 2022年第1期15卷 2335-2354页

作者： Mingyang Li Shanlong Lu Cong Du Yong Wang Chun Fang Xinru Li Hailong Tang Muhammad Hasan Ali Baig harrison odion ikhumhen International Research Center of Big Data for Sustainable Development Goals Key Laboratory of Digital Earth ScienceAerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtanPeople’s Republic of China School of Geospatial Engineering and Science Sun Yat-Sen UniversityZhuhaiPeople’s Republic of China Institute of Geo-Information & Earth-Observation (IGEO) PMAS Arid Agriculture University RawalpindiRawalpindiPakistan Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems College of the Environment and EcologyXiamen UniversityXiamenPeople’s Republic of China

Spatiotemporal continuity of surface water datasets widely known for its significance in the surface water dynamic monitoring and assessments,are faced with drawbacks like cloud influence,which hinders the direct extraction of data from time-series remote sensing *** study proposes a Time-series Surface Water Reconstruction method(TSWR).The initial stage of this method involves the effective use of remote sensing images to automatically construct multi-stage surface water boundaries based on Google Earth Engine(GEE).Then,we reconstructed regions the reconstruction of regions with missing water pixels using the distance relationship between the multi-stage water boundaries in previous and later *** applied to 10 large rivers around the world,this method yielded an overall accuracy of 98%for water extraction,an RMSE of 0.41 ***,time-series reconstruction tests conducted in 2020 on the Lancang and Danube rivers revealed a significant improvement in the image *** findings demonstrated that this method could not only be used to accurately reconstruct the surface water distribution missing water images,but also to depict a more pronounced time variation *** successful application of this method on GEE demonstrates its importance for use on large scales or in global studies.

关键词： Google earth engine sentinel-2 surface water reconstruction time-series surface water data

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