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 extr...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 images.This 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 periods.When applied to 10 large rivers around the world,this method yielded an overall accuracy of 98%for water extraction,an RMSE of 0.41 km2.Furthermore,time-series reconstruction tests conducted in 2020 on the Lancang and Danube rivers revealed a significant improvement in the image availability.These 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 characteristic.The successful application of this method on GEE demonstrates its importance for use on large scales or in global studies.展开更多
基金The research was funded by the National Natural Science Foundation of China[grant no 42171283]the Major Science and Technology Projects of Qinghai Province[grant no 2021-SF-A6]+1 种基金the Second Tibetan Plateau Scientific Expedition and Research Program(STEP)[grant number 2019QZKK0202]Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA19090120].
文摘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 images.This 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 periods.When applied to 10 large rivers around the world,this method yielded an overall accuracy of 98%for water extraction,an RMSE of 0.41 km2.Furthermore,time-series reconstruction tests conducted in 2020 on the Lancang and Danube rivers revealed a significant improvement in the image availability.These 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 characteristic.The successful application of this method on GEE demonstrates its importance for use on large scales or in global studies.
