Abstract 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.

中文翻译：

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使用机器学习方法与 GNSS 导出的天顶总延迟 (ZTD) 集成，改进对大规模 Sentinel-1 干涉图的对流层校正

摘要 Sentinel-1 任务以其空间覆盖范围广（250 公里）、重访时间短（6 天）和数据传播速度快等特点，为大规模干涉合成孔径雷达（InSAR）分析开辟了新的视角。然而，对流层的时空变化限制了 InSAR 测量用于大范围业务变形监测的准确性。由于对流层模型（如ERA-Interim）和其他外部数据（如全球导航卫星系统（GNSS））的粗节点间距，插值技术无法很好地复制局部和湍流对流层效应。在这项研究中，我们提出了一种基于机器学习 (ML) 高斯过程 (GP) 回归方法的新技术，结合使用小基线干涉图和 GNSS 导出的天顶总延迟 (ZTD) 值来减轻干涉观测中对流层引起的相位延迟。通过将 ML 技术应用于 2016 年 5 月至 10 月期间沿挪威上空的轨道采集的 12 张 Sentinel-1 图像，均方根误差 (RMSE) 平均降低了 83%，而使用 ERA-Interim 模型降低了 50%。