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.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)