{"title":"Reconstruction of Petermann glacier velocity time series using multi-source remote sensing images","authors":"Zongze Li, Jinsong Chong, Yawei Zhao, Lijie Diao","doi":"10.1016/j.jag.2024.104307","DOIUrl":null,"url":null,"abstract":"Glacier velocity is one of the crucial parameters in the research of glacier dynamics. Synthetic aperture radar (SAR), as an active microwave sensor, represents a common method to monitor glacier velocity. However, the changes of glacier surface could cause the data missing of glacier velocity due to incoherence. To meet the demand for glacier velocity monitoring, this paper employs the SAR images of Sentinel-1 in long time series and optical images of Sentinel-2 to investigate the velocity of Petermann glacier in 2021. Firstly, the time series of glacier velocity in the whole year of 2021 is obtained by using SAR images. The glacier velocity extracted from the optical image pairs is used as the initial value of the large missing part of the glacier velocity field. Then the spatiotemporal glacier velocity matrix is constructed and empirical orthogonal function (EOF) analysis is carried out. Among them, the glacier velocity is reconstructed by the glacier velocity estimation method based on confidence, and the complete glacier velocity time series is obtained by iterating to minimize the error of the reconstructed glacier velocity. Finally, the obtained time series of Petermann Glacier velocity in 2021 were statistically analyzed. The statistical results quantified the seasonal differences of Petermann Glacier. In addition, the analysis results show that the temporal and spatial variations of Petermann Glacier velocity are affected by topography and temperature.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"20 1","pages":""},"PeriodicalIF":7.5000,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Earth Observation and Geoinformation","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1016/j.jag.2024.104307","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
引用次数: 0
Abstract
Glacier velocity is one of the crucial parameters in the research of glacier dynamics. Synthetic aperture radar (SAR), as an active microwave sensor, represents a common method to monitor glacier velocity. However, the changes of glacier surface could cause the data missing of glacier velocity due to incoherence. To meet the demand for glacier velocity monitoring, this paper employs the SAR images of Sentinel-1 in long time series and optical images of Sentinel-2 to investigate the velocity of Petermann glacier in 2021. Firstly, the time series of glacier velocity in the whole year of 2021 is obtained by using SAR images. The glacier velocity extracted from the optical image pairs is used as the initial value of the large missing part of the glacier velocity field. Then the spatiotemporal glacier velocity matrix is constructed and empirical orthogonal function (EOF) analysis is carried out. Among them, the glacier velocity is reconstructed by the glacier velocity estimation method based on confidence, and the complete glacier velocity time series is obtained by iterating to minimize the error of the reconstructed glacier velocity. Finally, the obtained time series of Petermann Glacier velocity in 2021 were statistically analyzed. The statistical results quantified the seasonal differences of Petermann Glacier. In addition, the analysis results show that the temporal and spatial variations of Petermann Glacier velocity are affected by topography and temperature.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.