We present a review of small baseline interferometric synthetic aperture radar (InSAR) time series analysis with a new processing workflow and software implemented in Python, named MintPy (https://github.com/insarlab/MintPy). The time series analysis is formulated as a weighted least squares inversion. The inversion is unbiased for a fully connected network of interferograms without multiple subsets, such as provided by modern SAR satellites with small orbital tube and short revisit time. In the routine workflow, we first invert the interferogram stack for the raw phase time-series, then correct for the deterministic phase components: the tropospheric delay (using global atmospheric models or the delay-elevation ratio), the topographic residual and/or phase ramp, to obtain the noise-reduced displacement time-series.Next, we estimate the average velocity excluding noisy SAR acquisitions, which are identified using an outlier detection method based on the root mean square of the residual phase. The routine workflow includes three new methods to correct or exclude phase-unwrapping errors for two-dimensional algorithms: (i) the bridging method connecting reliable regions with minimum spanning tree bridges (particularly suitable for islands), (ii) the phase closure method exploiting the conservativeness of the integer ambiguity of interferogram triplets (well suited for highly A post print of a published manuscript at Computers and Geosciences 2 redundant networks), and (iii) coherence-based network modification to identify and exclude interferograms with remaining coherent phase-unwrapping errors. We apply the routine workflow to the Galápagos volcanoes using Sentinel-1 and ALOS-1 data, assess the qualities of the essential steps in the workflow and compare the results with independent GPS measurements.We discuss the advantages and limitations of temporal coherence as a reliability measure, evaluate the impact of network redundancy on the precision and reliability of the InSAR measurements and its practical implication for interferometric pairs selection. A comparison with another open-source time series analysis software demonstrates the superior performance of the approach implemented in MintPy in challenging scenarios.

Abstract-For multitemporal analysis of synthetic aperture radar (SAR) images acquired with a terrain observation by progressive scan (TOPS) mode, all acquisitions from a given satellite track must be coregistered to a reference coordinate system with accuracies better than 0.001 of a pixel (assuming full SAR resolution) in the azimuth direction. Such a high accuracy can be achieved through geometric coregistration, using precise satellite orbits and a digital elevation model, followed by a refinement step using a time-series analysis of coregistration errors. These errors represent the misregistration between all TOPS acquisitions relative to the reference coordinate system. We develop a workflow to estimate the time series of azimuth misregistration using a network-based enhanced spectral diversity (NESD) approach, in order to reduce the impact of temporal decorrelation on coregistration. Example time series of misregistration inferred for five tracks of Sentinel-1 TOPS acquisitions indicates a maximum relative azimuth misregistration of less than 0.01 of the full azimuth resolution between the TOPS acquisitions in the studied areas. Standard deviation of the estimated misregistration time series for different stacks varies from 1.1e-3 to 2e-3 of the azimuth resolution, equivalent to 1.6-2.8 cm orbital uncertainty in the azimuth direction. These values fall within the 1-sigma orbital uncertainty of the Sentinel-1 orbits and imply that orbital uncertainty is most likely the main source of the constant azimuth misregistration between different TOPS acquisitions. We propagate the uncertainty of individual misregistration estimated with ESD to the misregistration time series estimated with NESD and investigate the different challenges for operationalizing NESD.Index Terms-Coregistration, interferometric synthetic aperture radar (InSAR), spectral diversity, terrain observation by progressive scan (TOPS).

The 2016 Kumamoto earthquake sequence occurred on the Futagawa–Hinagu fault zone near the Aso volcano on Kyushu island. The sequence was initiated with two major (Mw ≥ 6.0) foreshocks, and the mainshock (Mw = 7.0) occurred 25 h after the second major foreshock. We combine GPS, strong motion, synthetic aperture radar images, and surface offset data in a joint inversion to resolve the kinematic rupture process of the mainshock and coseismic displacement of the foreshocks. The joint inversion results reveal a unilateral rupture process for the mainshock involving sequential rupture of four major asperities. The slip area of the foreshocks and mainshock and the aftershock loci form a detailed complementary pattern. The mainshock rupture terminates near the rim of the caldera, leaving a ~10 km long gap of aftershocks. This area is characterized by high temperature and low shear wave velocity, density, and resistivity, which may be related to the partially melted geothermal condition. Ductile material property near the volcano may act as a “material barrier” to the dynamic rupture. Topographic weight of the caldera increases compressional normal stress on the fault plane, which may behave as a “stress barrier.” Long‐term seismic hazard and deformation behaviors related to these two types of barriers are discussed in terms of the associated frictional mechanism. Significant postseismic creeps observed near the volcano area indicates a velocity strengthening frictional behavior near the rupture termination, which confirms that the “material barrier” mechanism is likely the dominant rupture termination mechanism.

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We present a mathematical formulation for the phase due to the errors in digital elevation models (DEMs) in synthetic aperture radar (SAR) interferometry (InSAR) time series obtained by the small baseline (SB) or the small baseline subset method. We show that the effect of the DEM error in the estimated displacement is proportional to the perpendicular baseline history of the set of SAR acquisitions. This effect at a given epoch is proportional to the perpendicular baseline between the SAR acquisition at that epoch and the reference acquisition. Therefore, the DEM error can significantly affect the time-series results even if SB interferograms are used. We propose a new method for DEM error correction of InSAR time series, which operates in the time domain after inversion of the network of interferograms for the displacement time series. This is in contrast to the method of Berardino (2002) in which the DEM error is estimated in the interferogram domain. We show the effectiveness of this method using simulated InSAR data. We apply the new method to Fernandina volcano in the Galapagos Islands and show that the proposed DEM error correction improves the estimated displacement significantly.