globalchange  > 气候变化与战略
DOI: 10.1016/j.rse.2019.111608
论文题名:
Improving tropospheric corrections on large-scale Sentinel-1 interferograms using a machine learning approach for integration with GNSS-derived zenith total delay (ZTD)
作者: Shamshiri R.; Motagh M.; Nahavandchi H.; Haghshenas Haghighi M.; Hoseini M.
刊名: Remote Sensing of Environment
ISSN: 344257
出版年: 2020
卷: 239
语种: 英语
英文关键词: Gaussian processes (GP) regression ; Global navigation satellite system (GNSS) ; Large-scale ; Machine learning (ML) ; Sentinel-1 ; Synthetic aperture radar (SAR) ; Troposphere ; Zenith total delay (ZTD)
Scopus关键词: Communication satellites ; Gaussian distribution ; Gaussian noise (electronic) ; Global positioning system ; Interferometry ; Machine learning ; Mean square error ; Space-based radar ; Troposphere ; Gaussian process ; Global Navigation Satellite Systems ; Large-scale ; Sentinel-1 ; Zenith total delays ; Synthetic aperture radar ; accuracy assessment ; correction ; GNSS ; interpolation ; machine learning ; regression analysis ; satellite data ; satellite imagery ; satellite mission ; Sentinel ; synthetic aperture radar ; troposphere ; Norway
英文摘要: Sentinel-1 mission with its wide spatial coverage (250 km), short revisit time (6 days), and rapid data dissemination opened new perspectives for large-scale interferometric synthetic aperture radar (InSAR) analysis. However, the spatiotemporal changes in troposphere limits the accuracy of InSAR measurements for operational deformation monitoring at a wide scale. Due to the coarse node spacing of the tropospheric models, like ERA-Interim and other external data like Global Navigation Satellite System (GNSS), the interpolation techniques are not able to well replicate the localized and turbulent tropospheric effects. In this study, we propose a new technique based on machine learning (ML) Gaussian processes (GP) regression approach using the combination of small-baseline interferograms and GNSS derived zenith total delay (ZTD) values to mitigate phase delay caused by troposphere in interferometric observations. By applying the ML technique over 12 Sentinel-1 images acquired between May–October 2016 along a track over Norway, the root mean square error (RMSE) reduces on average by 83% compared to 50% reduction obtained by using ERA-Interim model. © 2019 The Authors
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158695
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作者单位: Department of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, 7491, Norway; GFZ German Research Centre for Geosciences, Department of Geodesy, Section of Remote Sensing and Geoinformatics, Potsdam, 14473, Germany; Institute for Photogrammetry and GeoInformation, Leibniz University Hannover, Hannover, 30167, Germany

Recommended Citation:
Shamshiri R.,Motagh M.,Nahavandchi H.,et al. Improving tropospheric corrections on large-scale Sentinel-1 interferograms using a machine learning approach for integration with GNSS-derived zenith total delay (ZTD)[J]. Remote Sensing of Environment,2020-01-01,239
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