GNSS辅助下的InSAR对流层延迟垂直分层与湍流分量联合改正模型

doi:10.11947/j.AGCS.2025.20250124

摘要/Abstract

摘要：

GNSS基准站观测的对流层延迟量常用于改正InSAR对流层延迟误差，其实质是将GNSS点位观测到的延迟量推估（内插）至未测点位。传统方法只考虑湍流分量的空间相关特性，通过函数模型或随机模型建立干涉图误差改正模型，忽略垂直分层影响。本文提出兼顾垂直分层与湍流影响的InSAR干涉图误差联合改正模型，分别采用高程相关函数模型和随机模型建模垂直分层与湍流，并通过最小二乘配置同时估计确定性垂直分层模型参数与GNSS观测点位处的湍流分量，最后将二者推估至干涉图其他未测点位。应用本文方法处理美国南加州地区71景Sentinel-1数据，结果表明，本文方法可将70幅最短时间基线干涉图的平均标准差从4.7 rad降低至1.4 rad，改正效果优于GACOS（平均标准差下降至2.7 rad）、线性模型（平均标准差下降至4.1 rad）、GInSAR（平均标准差下降至2.9 rad）和LSC-GInSAR方法（平均标准差下降至1.8 rad）。此外，获得的线性形变速率揭示出试验区长波形变特征，与GNSS观测吻合良好（相关系数为0.67），验证了本文方法可有效改正InSAR干涉图的中长波长对流层延迟，适用于提取大尺度地表形变信号。

关键词: InSAR, 全球导航卫星系统, 对流层延迟, 最小二乘配置, 方差分量估计

Abstract:

GNSS reference station observed tropospheric delays are commonly employed to correct tropospheric delays in InSAR, which involves spatially interpolating the GNSS-observed delays to unmeasured locations. Traditional methods focus solely on the spatial correlation characteristics of turbulent components, achieve interferogram correction through functional or stochastic modeling while neglecting the stratified component. This study proposes a joint correction model that accounts for both stratified and turbulent delay components. Specifically, an elevation-dependent functional model and a stochastic model are adopted to absorb stratified and turbulent delay components, respectively. The deterministic parameters of stratified component and random turbulence at the GNSS-measured points are simultaneously resolved via least squares collocation. Finally, predict them to unmeasured points. Validation using 71 Sentinel-1 datasets over Southern California demonstrates that the proposed method reduces the average standard deviation (STD) of 70 short temporal baseline interferograms from 4.7 rad to 1.4 rad, outperforming both GACOS (average STD reduced to 2.7 rad), linear model (average STD reduced to 4.1 rad), GInSAR (average STD reduced to 2.9 rad) and LSC-GInSAR (average STD reduced to 1.8 rad) corrections. The derived deformation velocity reveals regional long-wavelength deformation pattern that agrees well with GNSS measurements (correlation coefficient is 0.67). These results confirm that the proposed approach can effectively correct medium-to-long-wavelength tropospheric delays in interferogram and is suitable for measuring large-scale deformation signals.

Key words: InSAR, global navigation satellite system, tropospheric delays, least squares collocation, variance component estimation

中图分类号:

P227

陈海禄, 沈云中. GNSS辅助下的InSAR对流层延迟垂直分层与湍流分量联合改正模型[J]. 测绘学报, 2025, 54(10): 1786-1797.

Hailu CHEN, Yunzhong SHEN. GNSS-assisted InSAR tropospheric delay correction model incorporating vertical stratification and turbulent components[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(10): 1786-1797.

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高度/m	类型
[0，200）	低海拔
[200，800）	中海拔
[800，∞）	高海拔

GNSS站点间距/km	GInSAR/cm	LSC-GInSAR/cm	本文方法/cm
65.1	3.4	2.5	1.7
44.5	2.3	1.9	1.3
32.8	2.5	1.8	1.1
21.7	2.3	1.3	0.7