Neutral atmospheric delay in interferometric synthetic aperture radar applications: Statistical description and mitigation

Abstract

[1] Variations in the refractive index of the atmosphere cause variations in satellite-based interferometric synthetic aperture radar (InSAR) observations. We can mitigate tropospheric effects by averaging N-independent interferograms. Because the neutral atmosphere is uncorrelated at timescales longer than 1 day, using this technique statistically reduces the variance, s 2 , of the noise by a factor of N. Using zenith neutral atmospheric delays from Global Positioning System (GPS) data from the Southern California Integrated GPS Network, we find that the average variance depends on the distance between observations, L, and height difference, H, as s = c L a + kH with estimated values for c, a, and k of about 2.5, 0.5, and 4.8, respectively, where s is in mm and L and H are in km. We expect that the value of a is largely site-independent but the value of c will depend on the water vapor variability of the area of interest. This model is valid over a range of L between approximately 10 and 800 km. Height differences between 0 and 3 km have been used in this analysis. For distances of 100 and 10 km with negligible height differences, s is estimated to be approximately 25 and 8 mm, respectively. For a given orbit revisit time and image archive duration, we calculate the number and duration (assumed constant) of interferograms required to achieve a desired sensitivity to deformation rate at a given length scale. Assuming neutral atmosphere is the dominant source of noise, a 30°look angle, and an image revisit time of 7 days, detection of a deformation rate of 1 mm yr À1 over distances of 10 km requires about 2.2 years of continuous observations. Given our results, we suggest a data covariance structure to use when using InSAR data to constrain geophysical models.

Rapid strain accumulation on the Ashkabad fault (Turkmenistan) from atmosphere‐corrected InSAR

“…Stacking N short-baseline and long-timespan interferograms is a common technique [e.g., Zebker et al, 1997;Williams et al, 1998;Wright et al, 2001;Emardson et al, 2003] [16] An established empirical method for estimating stratified atmospheric delay is to find the best-fitting linear or exponential relationship between phase and elevation for each interferogram [e.g., Cavalié et al, 2007;Elliott et al, 2008]. However, this method assumes that the same phase/elevation relationship applies across all regions of the interferogram.…”

Constraining crustal velocity fields with InSAR for Eastern Turkey: Limits to the block‐like behavior of Eastern Anatolia

“…Kruskal's algorithm is run on a pixel-by-pixel basis, so each pixel in the final rate map depends on different combinations of interferograms, and not all interferograms are required to be coherent for a given pixel. The inversion is weighted using a temporal variance-covariance matrix (VCM), which accounts for both the variance of individual interferograms and the covariance between interferograms that share a common acquisition date [Emardson et al, 2003]. The elements of the temporal VCM are the following:…”

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

“…The short-wavelength (<10 km) turbulent component is attributed to lateral changes in the distribution of water vapour over short time intervals, which results in spatially-and temporally-random phase patterns. Whilst these turbulent features may be reduced by temporal averaging [59], the stratified component is likely to have a seasonal dependence that can bias deformation estimates [58] and should therefore be estimated and removed.…”