Abstract
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 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.
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•Two phase-unwrapping error correction methods based on bridging and phase closure.•A new workflow for InSAR time series analysis with post-inversion phase correction.•Applications to Sentinel-1 and ALOS-1 data on Galápagos volcanoes, Ecuador.•An open-source Python toolbox (MintPy) with examples in Jupyter Notebook.•Compare four InSAR time-series approaches in MintPy and GIAnT.
