Elsevier

Remote Sensing of Environment

Volume 204, January 2018, Pages 109-121
Remote Sensing of Environment

Interferometric synthetic aperture radar atmospheric correction using a GPS-based iterative tropospheric decomposition model

https://doi.org/10.1016/j.rse.2017.10.038Get rights and content
Under a Creative Commons license
open access

Highlights

  • A framework is proposed to routinely use GPS for InSAR atmospheric correction.

  • 45–79% improvements of InSAR displacements with both sparse and dense GPS networks.

  • Performance indicators to inform model usability for better quality control.

  • Impact of station spacing on the model performance is evaluated.

Abstract

Atmospheric effects represent one of the major error sources of repeat-pass Interferometric Synthetic Aperture Radar (InSAR), and could mask actual displacements due to tectonic or volcanic deformation. The tropospheric delays vary both vertically and laterally and can be considered as the sum of (i) a vertically stratified component highly correlated with topography and (ii) a turbulent component resulting from turbulent processes in the troposphere varying both in space and time. In this paper, we outline a framework to routinely use pointwise GPS data to reduce tropospheric effects on satellite radar measurements. An Iterative Tropospheric Decomposition (ITD) model is used and further developed to separate tropospheric stratified and turbulent signals and then generate high-resolution correction maps for SAR interferograms. Cross validation is employed to assess the performance of the ITD model and act as an indicator to users of when and where correction is feasible. Tests were carried out to assess the impact of GPS station spacing on the ITD model InSAR correction performance, which provides insights into the trade-off between station spacing and the achievable accuracy. The application of this framework to Sentinel-1A interferograms over the Southern California (USA) and Southern England (UK) regions shows approximately 45–78% of noise reduction even with a sparse (~ 50–80 km station spacing) GPS network and/or with strong and non-random tropospheric turbulence. This is about a 50% greater improvement than previous methods. It is believed that this framework could lead to a generic InSAR atmospheric correction model while incorporating continuous and global tropospheric delay datasets, e.g. numerical weather models.

Keywords

InSAR
GPS
Tropospheric delay
Atmospheric correction
Performance indicator
Station spacing

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