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Different InSAR algorithms and methods produce velocities and times series that are not identical, even using the same data for the same area. This inconsistency can cause confusion and be a barrier to uptake and widespread use of the data in the commercial sector. With the widespread availability of Sentinel-1 SAR data and a suite of new algorithms in the commercial and academic sectors, it is timely to develop a method for comparison of different results. In this study, we focus on developing and testing an independent and robust methodology for assessment of different InSAR processing results. Our proposed method is adapted from the Terrafirma Process Validation project; we compare geocoded line-of-sight velocities and time series, density and coverage, as well as some qualitative metrics. We use Sentinel-1 data from an area in Glasgow (UK) processed using 4 different approaches modified RapidSAR, SqueeSAR, GAMMA-IPTA and conventional StaMPS. The main areas of ground motion are detected using all approaches, with the average standard deviation of velocity differences for all inter-comparison pairs in all polygons equal to 1.1 mm/yr. Sentinel-1 InSAR therefore provides comparable results that are independent of processing approaches. However, there are considerable differences in some aspects of the results, in particular in their density and coverage. We discuss the reasons for these differences and suggest a framework for validation that could be used in future national or pan-national ground motion services. • We present a new InSAR inter-comparison method for Sentinel-1 applications. • We extract metrics based on point distribution, deformation rate and time series. • The standard deviations of velocity differences are better than 2 mm/yr. • Correlation coefficients of velocities are higher than 0.5 in a deforming area. • Different approaches have significantly different pixel densities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]