Improving InSAR geodesy using Global Atmospheric Models

Abstract

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. IntroductionSynthetic aperture radar interferometry (InSAR) has been successfully used to measure ground deformations related to hydrologic, volcanic, and tectonic processes [e.g., Bawden et al., 2001;Beauducel et al., 2000;Massonnet et al., 1992]. Rapid, large-amplitude deformation signals such as coseismic displacement fields [e.g., Simons et al., 2002;Lasserre et al., 2005] or volcano-tectonic episodes [e.g., Pritchard and Simons, 2002;Wright et al., 2004;Doubre and Peltzer, 2007;Grandin et al., 2010] are now routinely measured by InSAR. Still, the detection of low-amplitude, long-wavelength deformation fields such as those due to interseismic strain accumulation or postseismic motion remains challenging because of interferometric decorrelation, inaccurate orbits, and atmospheric propagation delays [e.g., Peltzer et al., 2001;Wright et al., 2001;Ryder et al., 2007;Wen et al., 2012;Jolivet et al., 2012;Grandin et al., 2012;Béjar-Pizarro et al., 2013]. Here we focus on a specific method to mitigate the impact of atmospheric artifacts.Spatiotemporal variations of the refractivity of air can introduce a change in the measured interferometric phase, hereafter called the atmospheric phase screen (APS). This phase change, or phase delay, can be on the order of several centimeters and often overwhelms the deformation signal of interest [Hanssen, 2001]. These phase delays result from the combined effects of turbulent mixing in the atmosphere (hereaft...

Radar remote sensing from space for surface deformation analysis: present and future opportunities from the new SAR sensor generation

“…In particular, an extensive research activity has been already carried out for the latter issue; this also includes possible integration with external information sources (GPS, radiosonde sensors, etc.) and rather sophisticated atmospheric models (Jolivet et al 2012). However, in spite of such extensive research activities, a sufficient consensus on the final filtering solution is still missing and, probably, not easily reachable; this represents a drawback for a real standardisation of advanced DInSAR products or in case of surface deformation map generation immediately after a crisis event, such as a disruptive earthquake.…”

First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin

“…The success of this approach typically relies upon prior knowledge of either the location or spatial wavelength of deformation [62], of which we have neither in the Perth Basin. Predictive methods use external datasets, such as large-scale weather models [58,63] or GPS [64,65], to calculate and remove atmospheric artefacts. There are only two public-domain continuously operating GPS receivers in the Perth Basin (Figure 2), which is insufficient to use the latter approach.…”

“…In a second test, we compared the correlation between pixel displacement and pixel elevation before and after correction using the coefficient of determination R 2 metric [60,63]. Larger values of R 2 are observed for the non-corrected datasets ( Figure 3C,D), suggesting that application of ERA-I has reduced the effects of stratified atmospheric noise and the correlation between displacements and topography.…”

Correcting InSAR Topographically Correlated Tropospheric Delays Using a Power Law Model Based on ERA-Interim Reanalysis

“…Furthermore, weather conditions and sunlight dependency may reduce the quality of the multispectral imagery available for the study region. Following recent advances in the global atmospheric model (GAM), atmospheric models are now regarded as the most promising method of tropospheric correction for InSAR [22][23][24][25][26]. GAM provides atmospheric parameters (pressure, temperature, and humidity) that can be applied to calculate the refractivity equations and determine path delays.…”

“…Several techniques in linear models have been developed to separate the tropospheric signals from residual orbits, deformation signals, or other phase noises, such as removal of a preliminary estimate of the deformation signals [31,32], evaluation of a local phase-topography relationship by spatial band-filtering that is insensitive to deformation signals [33], and spatially variable consideration for a piecewise slope correction over multiple windows [34]. The relationship between phase and topography using such empirical methods, however, depends on the spatial extent of the SAR scene, which sometimes leads to wrong estimates of spatial variation in the tropospheric stratification [26]. A power law model (∆φ trop = K (h 0 − h) α ) developed by Bekaert et al [35] can account for the spatially variable signal in tropospheric delay and can be applied to a region containing topographically correlated deformation.…”