Abstract
With the advent of the two Sentinel-1 (S1) satellites, Synthetic Aperture Radar (SAR) data with high temporal and spatial resolution is freely available. This provides a promising framework to facilitate detailed investigations of surface instabilities and movements on large scales with high temporal resolution, but also poses substantial processing challenges because of storage and computation requirements. Here we present OSARIS, the ‘Open Source SAR Investigation System’, as a framework to process large stacks of S1 data on High-Performance Computing (HPC) clusters. Based on GMTSAR, shell scripts, and the workload manager Slurm, OSARIS provides an open and modular framework combining parallelization of high-performance C programs, flexibility of processing schemes, convenient configuration, and generation of geocoded stacks of analysis-ready base data, including amplitude, phase, coherence, and unwrapped interferograms. Time series analyses can be conducted by applying automated modules to the data stacks. The capabilities of OSARIS are demonstrated in a case study from the northwestern Tien Shan, Central Asia. After merging of slices, a total of 65 scene pairs were processed from 150 total input scenes. OSARIS processing yields a comprehensive set of interferometric data for each pair, including amplitude, coherence, unwrapped interferometric phase, and line-of-sight displacement (LOSD). The coherence time series exhibits pronounced seasonal variability, with relatively high coherence values prevailing during the summer months in the nival zone. As an example of a time series analysis module, we present OSARIS’ ‘Unstable Coherence Metric’ (UCM) which identifies pixels affected by significant drops from high to low coherence values. Surface changes along moraine ridges, steep slopes, and several gullies during July and August were observed. Measurements of motion provided by LOSD measurements require careful evaluation because interferometric phase unwrapping is prone to errors. Here, OSARIS provides a series of modules to detect and mask unwrapping errors, correct for atmospheric disturbances, and remove large-scale trends. Wall clock processing time for the case study (area ~9,000 km²) was ~9h:47m on a machine with 320 cores and 1536 GB RAM. In total, ~11d:08h:28m were saved through parallelization. OSARIS thus allows to implement S1-based region-wide investigations of surface movement events over multiple years.
Highlights
Surface movement events are abundant in high-mountain environments and further activation is anticipated under sustained climate change, e.g., through thawing permafrost on slopes or unstable moraines in deglaciated settings (Heckmann et al, 2016)
The comparison with results from SNAP and ISCE shows that these GMTSAR-based datasets are of competitive quality
Higher level analyses are facilitated through OSARIS output data structure and module design, fostering the creation of processing chains by applying additional metrics to any of the datasets generated before
Summary
Surface movement events are abundant in high-mountain environments and further activation is anticipated under sustained climate change, e.g., through thawing permafrost on slopes or unstable moraines in deglaciated settings (Heckmann et al, 2016). Synthetic Aperture Radar (SAR) interferometry (InSAR) has proven its large potential to detect and analyze surface movements (e.g., Gabriel et al, 1989; Massonnet and Feigl, 1998); until recently such investigations were typically limited to individual events and regions favorable for obtaining high coherence pairs owing to long temporal baselines and low spatial resolution This situation changed in recent years and with the advent of the European Space Agency’s two Copernicus Sentinel-1 satellites (S1; e.g., Malenovsky et al, 2012; Torres et al, 2012) providing freely available, highquality C-band SAR data with high temporal and spatial resolution (Rucci et al, 2012; Jung et al, 2013; YagüeMartínez et al, 2016). S1’s vast potential to investigate comprehensive and detailed time series is hardly being exploited to date
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