Statistical comparison of InSAR tropospheric correction techniques
Statistical comparison of InSAR tropospheric correction techniques
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DOI：
10.1016/j.rse.2015.08.035
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发表时间：
2015-12-01
影响因子：
13.5
通讯作者：
Parker, D. J.
中科院分区：
文献类型：
--
作者：
Bekaert, D. P. S.;Walters, R. J.;Parker, D. J.
改正对流层延迟是干涉合成孔径雷达(InSAR)领域面临的最大挑战之一。温度、气压和相对湿度的时空变化在InSAR数据中产生对流层信号，掩盖了由于构造或火山变形造成的较小地表位移。使用天气模式数据、全球导航卫星系统和(或)光谱仪数据的校正方法在过去得到了应用，但往往受到辅助数据的空间和时间分辨率的限制。或者，可以通过假定干涉相位与地形之间的线性或幂函数关系来估计校正。通常，挑战在于将形变信号与对流层相位信号分开。在这项研究中，我们对MERIS和MODIS光谱仪、低和高空间分辨率天气模式(ERA-I和WRF)以及传统的线性和新的幂定律经验方法估计的最新对流层校正进行了统计比较。我们的测试地区包括墨西哥南部、意大利和埃尔耶罗。我们发现，光谱仪对对流层信号的衰减最大，但仅限于无云和日光采集。我们发现，随着云量的增加，各种方法的RMSE都有类似的10%-20%的增长。没有一种其他对流层校正方法在不同区域和时间持续减少对流层信号。我们已经发布了一个新的软件包，名为TRAIN(减少大气InSAR噪声的工具箱)，其中包括所有这些最先进的校正方法。我们建议今后的发展应着眼于以最佳方式结合不同的校正方法。(C)2015年提交人。由Elsevier Inc.出版。这是CC许可下的一篇开放获取文章
Correcting for tropospheric delays is one of the largest challenges facing the interferometric synthetic aperture radar (InSAR) community. Spatial and temporal variations in temperature, pressure, and relative humidity create tropospheric signals in InSAR data, masking smaller surface displacements due to tectonic or volcanic deformation. Correction methods using weather model data, GNSS and/or spectrometer data have been applied in the past, but are often limited by the spatial and temporal resolution of the auxiliary data. Alternatively a correction can be estimated from the interferometric phase by assuming a linear or a power-law relationship between the phase and topography. Typically the challenge lies in separating deformation from tropospheric phase signals. In this study we performed a statistical comparison of the state-of-the-art tropospheric corrections estimated from the MERIS and MODIS spectrometers, a low and high spatial-resolution weather model (ERA-I and WRF), and both the conventional linear and new power-law empirical methods. Our test-regions include Southern Mexico, Italy, and El Hierro. We find spectrometers give the largest reduction in tropospheric signal, but are limited to cloud-free and daylight acquisitions. We find a similar to 10-20% RMSE increase with increasing cloud cover consistent across methods. None of the other tropospheric correction methods consistently reduced tropospheric signals over different regions and times. We have released a new software package called TRAIN (Toolbox for Reducing Atmospheric InSAR Noise), which includes all these state-of-the-art correction methods. We recommend future developments should aim towards combining the different correction methods in an optimal manner. (C) 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license