Improving InSAR geodesy using Global Atmospheric Models
Improving InSAR geodesy using Global Atmospheric Models
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DOI：
10.1002/2013jb010588
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发表时间：
2014-03-01
影响因子：
3.9
通讯作者：
Li, Zhenghong
中科院分区：
文献类型：
--
作者：
Jolivet, Romain;Agram, Piyush Shanker;Li, Zhenghong
大气中气压、温度和水汽含量的时空变化会给地面形变的干涉合成孔径雷达（干涉合成孔径雷达）观测和区域应变率的偏差估计带来显著的干扰延迟。对流层延迟的可靠估计仍然是提高干涉合成孔径雷达地面形变测量精度的关键挑战之一。最近的研究表明，全球大气再分析对减轻对流层延迟的影响是有效的，这促使人们进一步探索其潜力。在这里，我们探讨了这些模型在几个地理和构造环境中的有效性，无论是单干涉图和时间序列分析产品。流体静力学和湿贡献的相位延迟是很重要的帐户。我们验证这些路径延迟校正来自被动多光谱成像仪中分辨率成像光谱仪，机载环境卫星的垂直集成大气水汽含量的估计比较。一般来说，预报的性能取决于大气湍流的强度。我们讨论了（1）如何分离大气和轨道的贡献，使人们能够更好地测量长波长变形和（2）大气延迟如何影响地震后的地表变形的测量，以及（3）这样的方法如何使我们能够减少多年应变率估计的偏差，通过减少对流层延迟的不均匀采样的季节性振荡的影响。
Spatial and temporal variations of pressure, temperature, and water vapor content in the atmosphere introduce significant confounding delays in interferometric synthetic aperture radar (InSAR) observations of ground deformation and bias estimates of regional strain rates. Producing robust estimates of tropospheric delays remains one of the key challenges in increasing the accuracy of ground deformation measurements using InSAR. Recent studies revealed the efficiency of global atmospheric reanalysis to mitigate the impact of tropospheric delays, motivating further exploration of their potential. Here we explore the effectiveness of these models in several geographic and tectonic settings on both single interferograms and time series analysis products. Both hydrostatic and wet contributions to the phase delay are important to account for. We validate these path delay corrections by comparing with estimates of vertically integrated atmospheric water vapor content derived from the passive multispectral imager Medium-Resolution Imaging Spectrometer, onboard the Envisat satellite. Generally, the performance of the prediction depends on the vigor of atmospheric turbulence. We discuss (1) how separating atmospheric and orbital contributions allows one to better measure long-wavelength deformation and (2) how atmospheric delays affect measurements of surface deformation following earthquakes, and (3) how such a method allows us to reduce biases in multiyear strain rate estimates by reducing the influence of unevenly sampled seasonal oscillations of the tropospheric delay.