时序分析是合成孔径雷达干涉测量(InSAR)的一项关键技术，它被广泛应用于监测广阔地区的地表缓慢形变。这种方法能够提供大范围、大面积的形变监测。其中，StaMPS PS方法凭借适用范围广、开源等优点，受到众多学者的使用。但在时序InSAR中，对流层延迟相位会导致形变监测精度的降低，因此，以合肥市为研究区域，分析了经验模型线性改正、GACOS(generic atmospheric correction online service for InSAR)改正和欧洲中期天气预报中心(ECMWF)最新发布的ERA5 数据集改正，并对比这 3 种方法在时序InSAR反演形变速率中的改正效果。通过计算得到研究区的标准差，进行比较分析和验证。其中线性改正、ERA5 和GACOS改正后的标准差分别降低了 21。71%、16。14%、10。38%。对于合肥区域，这 3 种方法均可减弱对流层延迟的影响，且精度都有所提高，其中线性改正效果最好，适用性更高，ERA5 和GACOS受天气以及地面监测点密度等影响，在该区域改正效果较差。
Evaluation of StaMPS-PS Timing-based InSAR Tropospheric Delay Correction Methods
Time-series analysis is a key technique in Interferometric Synthetic Aperture Radar(In-SAR),which has been widely used to monitor slow surface deformation over large areas.This meth-od has the ability to monitor deformation over a wide range and large area.Among them,the StaMPS PS method has been favored by many scholars due to its advantages of wide application range and open source.However,in time-series InSAR,the delayed phase of the troposphere can lead to the degradation of the deformation monitoring accuracy.Therefore,this paper analyzed the linear correction of empirical model,the GACOS(Generic Atmospheric Correction Online Service for InSAR)correction and the latest release of the European Center for Medium-Range Weather Forecasts(ECMWF)with the city of Hefei as the study area.And compared the correction effects of these three methods in the time-series InSAR inversion of deformation rate.The standard deviations in the study area were obtained by calculation,and the related reasons were analyzed and validated for comparison.The standard deviations of linear correction,ERA5 and GACOS correction were re-duced by 21.71%,16.14%and 10.38%,respectively.For the Hefei region,these three methods were able to attenuate the effect of tropospheric delay and improve accuracy,among which the linear correction had the best effect and higher applicability,while the ERA5 and GACOS were affected by the weather and the density of the ground monitoring points,resulting in a poorer correction effect in this region.
万方数据
维普
