Statistical comparison of InSAR tropospheric correction techniques

a r t i c l e i n f o Keywords: Atmosphere Tropospheric noise Corrections Phase-based Spectrometers Weather models InSAR State-of-the-art TRAIN Correcting for tropospheric delays is one of the largest challenges facing the interferometric synthetic aperture radar (InSAR) community. Spatial and temporal variations in temperature, pressure, and relative humidity create tropospheric signals in InSAR data, masking smaller surface displacements due to tectonic or volcanic deformation. Correction methods using weather model data, GNSS and/or spectrometer data have been applied in the past, but are often limited by the spatial and temporal resolution of the auxiliary data. Alternatively a correction can be estimated from the interferometric phase by assuming a linear or a power-law relationship between the phase and topography. Typically the challenge lies in separating deformation from tropospheric phase signals. In this study we performed a statistical comparison of the state-of-the-art tropospheric corrections estimated from the MERIS and MODIS spectrometers, a low and high spatial-resolution weather model (ERA-I and WRF), and both the conventional linear and new power-law empirical methods. Our test-regions include Southern Mexico, Italy, and El Hierro. We find spectrometers give the largest reduction in tropospheric signal, but are limited to cloud-free and daylight acquisitions. We find a ~10–20% RMSE increase with increasing cloud cover consistent across methods. None of the other tropospheric correction methods consistently reduced tropospheric signals over different regions and times. We have released a new software package called TRAIN (Toolbox for Reducing Atmospheric InSAR Noise), which includes all these state-of-the-art correction methods. We recommend future developments should aim towards combining the different correction methods in an optimal manner. Interferometric Synthetic Aperture Radar (InSAR) is a geodetic tool that is well suited to the observation of crustal deformation processes. However, the use of InSAR to measure small magnitude and long wavelength deformation signals, such as interseismic slip (e. is severely limited by atmospheric contamination of the InSAR data. Separating deformation from atmospheric signals , introduced by the variation of atmospheric properties in space and time, remains one of the major challenges for InSAR (Hooper et al., 2013). Atmospheric delays are typically split into ionospheric and tropo-spheric terms. Ionospheric effects are caused by variations in free electrons along the travel path, resulting in a phase advance of the radar signal that becomes more significant for larger wavelengths, such as for P and L-band SAR (e.g. Gray, Mattar, & Sofko, 2000). Tropo-spheric effects are caused by …