InSAR has long been one of the most powerful tools in the geodetic toolbox. By comparing the phase of radar signals reflected from the same patch of ground on different satellite passes, the technique can detect displacements of just a few millimeters, whether caused by earthquakes, landslides, groundwater extraction, or the collapse of abandoned mine workings. The European Space Agency’s Sentinel-1 constellation, with its free and open data policy, six-to-twelve-day revisit cycle and stable global coverage, has made the method more accessible than ever. Yet the sheer volume of data has become a bottleneck: monitoring tens of thousands of square kilometers over several years requires processing terabyte-scale stacks of imagery, and conventional serial workflows can take weeks on a single workstation while straining storage and memory far beyond what most laboratories possess.

The innovation of the new framework lies in its choice of the basic processing unit. A standard Sentinel-1 single-look complex product, containing both VV and VH polarization channels, weighs in at roughly 8 gigabytes. But each product is actually assembled from dozens of bursts, the fundamental acquisition units of the satellite’s Interferometric Wide Swath mode, divided across three sub-swaths. A single VV-polarized burst represents only about one fifty-fourth of a standard product, less than 0.15 gigabytes. Crucially, bursts do not suffer from the along-track spatial offsets that plague full SLC products between acquisition dates. By treating each burst as an independent task, the researchers turned a monolithic, serial job into thousands of small, self-contained computations that can be dispatched across multiple devices simultaneously.

The mathematics of the parallelization are straightforward but consequential. If a study area contains n bursts and each forms m interferometric pairs over time, a conventional workflow constrained to a single node can run at most n parallel tasks, while the multi-node mode of the new framework reaches m times n, with every interferometric pair co-registered, unwrapped and geocoded independently. When computing resources are plentiful, the multi-node mode maximizes throughput; when they are scarce, a single-node mode keeps computational demand in check. The team implemented the workflow primarily with the ISCE and MintPy software packages, setting a temporal baseline threshold of 36 days to avoid decorrelation over Jining’s extensively cultivated, low-coherence landscape.

Independent sub-tasks, however, produce fragments that must be stitched back together, and this is where a second key contribution comes in. Adjacent bursts overlap by roughly 1.5 kilometers and neighboring sub-swaths by about 2 kilometers, but deformation values in those overlaps often disagree because of differing reference points and the uneven propagation of phase-unwrapping errors. The researchers built a correction and mosaicking workflow that resamples each dataset onto a common grid, fits the deformation-rate differences along the long edge of each overlap with a quadratic polynomial, and removes the resulting surface as a systematic bias. A distance-weighted blending scheme then smooths residual discrepancies across the seam. The quadratic form was chosen deliberately: it captures the slowly varying nonlinear patterns typical of unwrapping errors without the overfitting risk of higher-order models, and it is applied in only one direction because the strip-like overlap zones are far too narrow for stable two-dimensional fitting.

The payoff is visible in the numbers. Before correction, deformation-rate differences between two adjacent bursts showed a root mean square error of 12.01 millimeters per year, with visible jumps of more than 10 millimeters across burst seams; after quadratic detrending and weighted blending, the RMSE fell to 6.14 millimeters per year and the discontinuities vanished. A conventional workflow also cannot recover anything at all where data are missing: when one burst over Jining had no acquisition on 13 December 2021, standard time-series InSAR would either lose that entire date or force analysts to discard every other burst from it. The burst-based method simply processes what exists, achieving full coverage of the city.

Computational benchmarks underscore the practical significance. On a modest machine with an Intel Core i5-11400 processor, 6 cores and 12 threads, and 48 gigabytes of memory, the burst-based approach processed an interferometric pair far faster than the conventional full-scene method, which also required vastly more memory. The authors project that parallel processing of twelve interferometric pairs on a single node would demand nearly 200 gigabytes of RAM conventionally, versus under 8 gigabytes with the new method. Individual burst tasks were successfully completed on machines with as little as 8 gigabytes of RAM and 256 gigabytes of storage. Just 149 decompressed Sentinel-1 scenes exceed the storage of most mainstream PCs, so the difference between feasible and impossible hinges entirely on how the data are partitioned.

To validate accuracy, the team ran a conventional full-scene analysis over most of Jining under identical parameters and compared the two velocity fields. The correlation coefficient was 0.877, with a root mean square error of 6.53 millimeters per year, and profile curves agreed well in low-to-moderate deformation ranges, with deviations appearing only where deformation gradients are steep, likely from pixel-level resampling errors during geocoding. The method does have a documented limit: in a test on the magnitude 6.8 earthquake that struck Tingri County, China, in January 2025, burst-wise phase unwrapping failed where extreme, abrupt deformation broke phase continuity within a single burst, whereas full-scene unwrapping exploited surrounding low-gradient areas as connectivity constraints. The authors caution that block-based strategies should be applied judiciously where large, high-gradient deformation is expected.

Applied to Jining, the framework delivered a striking picture of a city slowly sinking. Using Sentinel-1A imagery from 149 acquisitions between 2019 and 2023 along Track 142, the analysis classified the surface into stable, moderately and severely deformed zones, and found that Rencheng and Yanzhou Districts bear the heaviest subsidence burdens, while Yutai and Sishui Counties remain essentially stable. When deformation was cross-referenced with the boundaries of the Jining, Yanzhou and Juye coalfields, the correlation was unmistakable: moderately deformed area made up 3.456 percent of mining zones but only 0.039 percent of non-mining land, roughly an 89-fold difference, and severely deformed area was about 222 times more prevalent in mining zones. Coal extraction, not any natural process, dominates the deformation field. At the Yangying Coal Mine, which ceased operations in February 2016, the surface has since stabilized, showing how subsidence tracks the life cycle of mining.

The consequences reach into everyday infrastructure. Drawing on building footprints and road vectors, the researchers found that 98.18 percent of buildings sit in stable ground while 1.79 percent experience moderate deformation and 0.01 percent severe deformation; along roads the corresponding figures were 96.21, 3.74 and 0.05 percent, and water ponding in badly subsided mining areas means the true severe fraction is higher than measured. Time-series analysis at two representative points revealed characteristic evolution: at K1 in Liangshan County, subsidence progressed from steady settlement through rapid sinking before gradually stabilizing, while at K2 in Rencheng District, within the Yunhe Coal Mine area, an initial linear phase has given way to accelerating subsidence that was still ongoing at the end of the study period. Field surveys confirmed the human cost, documenting cracks in buildings around K1 and fractures in roads near K2. The authors argue that continuous, affordable monitoring of this kind is essential for guiding mitigation, and their framework, by collapsing the hardware barrier from supercomputer to desktop, could bring millimeter-scale deformation surveillance within reach of small research groups, local agencies and individual practitioners worldwide.

Subject of Research: A distributed parallel processing framework for Sentinel-1 wide-area time-series InSAR deformation monitoring, applied to mining subsidence in Jining City

Article Title: A distributed parallel processing framework for sentinel-1 wide-area time series InSAR: Application in Jining City

Article References: A distributed parallel processing framework for sentinel-1 wide-area time series InSAR: Application in Jining City. (n.d.). https://doi.org/10.1007/s12665-026-13147-1

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13147-1

Keywords: InSAR, Sentinel-1, parallel processing, surface deformation, mining subsidence, phase unwrapping, remote sensing, geohazard monitoring, Jining City, data mosaicking, time-series analysis, infrastructure safety

The magnitude of the intrusion is modest by volcanic standards. Stamps compares the volume to about 400 Olympic-size swimming pools, an amount that is not in itself a cause for alarm. The magma, according to the team’s numerical modeling, does not appear to be migrating closer to the crater, and the reservoir it is entering already existed prior to the event. Yet the significance of the finding extends well beyond the volume of rock involved. It demonstrates that the monitoring infrastructure installed on the volcano can detect subtle, transient deformation signals and, more importantly, that the team can interpret those signals with sufficient confidence to attribute them to specific subsurface processes. That capability lies at the heart of any credible early-warning effort for a volcano that alternates between gentle and violent behavior.

The detection itself was made possible by a ground-based geodetic network that Stamps’ group began building a decade ago. In 2016, the team installed six Global Navigation Satellite System instruments on the flanks and surroundings of Ol Doinyo Lengai. These continuously operating stations track the horizontal and vertical motions of the ground surface to a precision of about one millimeter, a level of sensitivity that allows researchers to distinguish genuine deformation of the volcanic edifice from noise introduced by atmospheric effects or equipment drift. The GNSS network is complemented by two broadband seismic stations that record earthquake activity across a wide range of frequencies, and the continuous instrumentation is supplemented by episodic benchmark measurements taken during field campaigns.

The combination proved decisive in 2024. When the GNSS stations recorded an uplift signal around the volcano, the team turned to numerical modeling to determine what subsurface source could reproduce the observed pattern of surface movement. By fitting deformation models to the geodetic data, the researchers concluded with a high degree of certainty that the signal was generated by an influx of magma into an already existing magma reservoir roughly three kilometers underground. The result validated both the sensitivity of the network and the interpretive framework the team has developed over more than ten years of observation at the site. The work was published on September 18, 2026, in Frontiers in Earth Science, with the DOI 10.3389/feart.2026.1881885.

Ol Doinyo Lengai occupies a singular position in volcano science. Its carbonatite lavas erupt at temperatures far lower than the silicate lavas produced by virtually every other volcano on the planet, and during quiet periods the volcano maintains an active lava lake that bubbles and flows effusively within its summit crater. This effusive behavior, however, is only half of the volcano’s personality. On average, Ol Doinyo Lengai produces an explosive eruption every 10 to 15 years, and these explosive episodes can pose serious hazards to the communities that live on and around its slopes. Stamps notes that the team expects another explosive eruption within their lifetime, and the central motivation of the research program is to give residents and authorities enough lead time to prepare and respond to evacuation decisions made by the Tanzania Geological Survey.

Understanding what distinguishes a benign magma intrusion from a precursor to violence is the crux of the forecasting problem, and the 2024 event provided a valuable calibration point. According to Stamps, the last time the volcano erupted explosively, the event was preceded by a magnitude 5.9 earthquake. That historical sequence suggests a set of warning indicators that the team now watches for: a significant earthquake followed by observable changes in surface deformation, and, most critically, evidence that magma is moving from a deeper storage zone to a shallower one over time. Such upward migration indicates that magma is ascending the volcanic conduit, and it is precisely the kind of progression that would elevate concern. The 2024 intrusion, by contrast, showed magma entering an existing reservoir without any sign of ascent toward the crater, which is why the team assessed it as low risk.

One of the most stubborn uncertainties in volcanology is timing. Some volcanoes erupt explosively every time they erupt, making their behavior relatively predictable once unrest is detected. Ol Doinyo Lengai is more complicated, alternating between effusive and explosive eruptions in a pattern that is not fully understood. A key reason the team continues to monitor the volcano intensively is to constrain this particular volcano’s time delay, the interval between detectable precursory activity and an actual explosive eruption. Every additional episode of recorded deformation, seismicity, and magma transport adds to a decade-long baseline of observations that gradually reveals how the volcano’s subsurface system transitions from storage to ascent to eruption. The longer and richer the record becomes, the more skillfully the team can match patterns of surface observation to processes occurring underground.

The monitoring effort at Ol Doinyo Lengai is also notable for its ground-based character. Satellite-based observations, including those contributed through NASA, provide valuable context and broad spatial coverage of deformation across the region. But Stamps emphasizes that her team is currently the only group conducting continuous ground-based GNSS monitoring and consistent ground-based seismic monitoring at the volcano. Ground instruments offer continuous temporal sampling and millimeter-level precision that orbital observations alone cannot match, particularly for small, short-lived deformation episodes like the 2024 bulge, which might be missed or ambiguously characterized by intermittent satellite acquisitions. The redundancy and complementarity of the two approaches strengthen the overall early-warning picture.

The practical payoff of this work accrues directly to the people who live in the shadow of the volcano. More than a decade of observations has given the team a substantially better understanding of which underground magma movements correspond to which surface signals, and that empirical link is the foundation of any effort to anticipate when the volcano is edging closer to an explosive eruption. For residents of the surrounding area, the difference between a well-instrumented volcano and a poorly instrumented one can be measured in the time available to act. The Tanzania Geological Survey, which holds formal responsibility for evacuation decisions, now has access to a continuously refreshed, quantitatively interpreted stream of deformation and seismicity data from one of the rift’s most active and most unpredictable volcanoes.

The 2024 bulge may have been modest, equivalent to 400 swimming pools of magma settling into a reservoir it already knew, but as a test of scientific and operational readiness it was passed convincingly. A transient deformation signal was detected in near real time, modeled rigorously, attributed to a specific source at a specific depth, and correctly evaluated as non-threatening. Each such episode sharpens the interpretive tools that will be needed when the volcano’s behavior changes in more consequential ways. Given the volcano’s average recurrence interval of 10 to 15 years between explosive eruptions, the question is not whether the next one will come but whether the warning signs will be recognized in time. On the evidence of this study, the instruments are in place, the baseline record is deepening, and the science of translating millimeters of ground motion into meaningful forecasts is steadily maturing at one of Earth’s most remarkable volcanoes.

Subject of Research: Magma intrusion and surface deformation monitoring at Ol Doinyo Lengai volcano in Tanzania

Article Title: Interpreting a volcano’s ‘bulges’ and predicting the next explosive eruption

Article References: Interpreting a volcano’s ‘bulges’ and predicting the next explosive eruption. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Ol Doinyo Lengai, volcano monitoring, magma intrusion, GNSS, Tanzania, explosive eruption, East African Rift, surface deformation, carbonatite lava, seismic monitoring, early warning system, Frontiers in Earth Science

The research, conducted by Nihal Tekin Ünlütürk of Erciyes University and Uğur Doğan of Yıldız Technical University, evaluated the effect of tropospheric corrections derived from Global Navigation Satellite System (GNSS) observations against those produced by the Generic Atmospheric Correction Online Service for InSAR, widely known as GACOS. Tropospheric delay is one of the dominant error sources in InSAR processing, and if left uncorrected it can distort the interpretation of earthquake-induced deformation fields—the very patterns scientists rely on to infer fault slip, rupture extent, and seismic hazard. The team’s central question was deceptively simple: which correction approach actually improves the displacement maps, and by how much?

To find out, the researchers processed both ascending and descending Sentinel-1A radar images from the European Space Agency’s Copernicus mission using the Sentinel Application Platform (SNAP) within a standard Differential InSAR (DInSAR) workflow. This involved generating interferometric pairs spanning the earthquake, applying phase filtering to suppress noise, and performing phase unwrapping with the Statistical-Cost, Network-Flow Algorithm for Phase Unwrapping, or SNAPHU—a computational step that recovers continuous displacement values from the wrapped fringes that radar phase measurements naturally produce. Working with both ascending and descending orbits matters because each viewing geometry samples the ground differently along the satellite line-of-sight, providing complementary constraints on the three-dimensional deformation field.

The GNSS-based correction strategy exploited Turkey’s dense TUSAGA-Active continuous GNSS network, whose data were provided by the General Directorate of Mapping. Because GNSS signals also pass through the troposphere, each station continuously estimates the zenith tropospheric delay—the total delay a signal experiences traveling vertically through the atmosphere. By regressing these delays against station elevation, the researchers derived parameters that allowed them to predict the topography-correlated component of the atmospheric delay across the entire interferogram, interpolating between stations and removing the estimated delay from the radar phase. GACOS, by contrast, is an online service that generates correction maps by combining global weather model data with an iterative tropospheric decomposition model, separating the stratified, elevation-dependent part of the delay from turbulent components.

The results are a sobering reminder that atmospheric correction is not a solved problem. Both GNSS- and GACOS-based corrections clearly influenced the spatial distribution of the line-of-sight (LOS) displacement fields, reshaping the apparent deformation patterns in ways that could alter scientific conclusions. Yet their effects were not spatially uniform across the study area. In some regions the corrections agreed closely; in others, the corrected products diverged noticeably, leaving analysts with genuinely different pictures of how the ground moved. The study found that neither correction strategy produced a spatially uniform improvement across all interferometric pairs—a finding that challenges the common assumption that applying any atmospheric correction is automatically better than applying none.

The local discrepancies between the corrected products, the authors suggest, likely stem from a combination of factors: regional atmospheric variability, the rugged topography of the East Anatolian Fault Zone, the acquisition geometry of the satellite passes, and the uneven distribution of GNSS stations. Eastern Anatolia is a region of sharp elevation contrasts, where stratified tropospheric delay can change rapidly over short horizontal distances. Where GNSS stations are sparse, interpolation of zenith delays becomes uncertain, and turbulent water vapor variations—which are poorly correlated with elevation—can dominate. Weather-model-based corrections like GACOS, meanwhile, are limited by the spatial and temporal resolution of the underlying meteorological data, which may not capture localized convective moisture features at the moment of a satellite overpass.

Rather than declaring a single winner, the study advocates a more rigorous, diagnostic approach. The researchers emphasize the need to carefully evaluate atmospheric correction performance using spatial statistics, profile comparisons across the deformation field, and displacement-difference analyses between corrected and uncorrected products. In other words, the choice of correction should be treated as a hypothesis to be tested for each interferogram and each tectonic setting, not a routine checkbox. This methodological caution has practical consequences: deformation signals from the Elazığ–Sivrice rupture feed into models of fault slip and earthquake source parameters, and atmospheric artifacts of comparable magnitude could bias estimates of how much the fault moved and where.

The 2020 Elazığ–Sivrice earthquake itself makes a compelling case study. The event struck the East Anatolian Fault Zone, a major strike-slip boundary accommodating the westward extrusion of the Anatolian plate between the Arabian and Eurasian plates. Previous studies have used InSAR and GNSS data to characterize its rupture behavior, and the region’s tectonic importance has motivated extensive geodetic monitoring. The fault zone’s combination of strong topographic relief, semi-arid climate with pronounced seasonal atmospheric variation, and a growing but still uneven GNSS infrastructure makes it an ideal proving ground for atmospheric correction techniques that must ultimately work worldwide.

The broader significance of the study lies in its framing of GNSS- and GACOS-based corrections as complementary rather than competing tools. Because they draw on fundamentally different information sources—direct physical measurements of atmospheric delay at ground stations versus model-derived estimates from meteorological data—their residuals behave differently. Comparing them side by side provides a way to identify atmospheric contributions embedded in interferometric phase and to assess the uncertainty of InSAR-derived earthquake deformation fields. For hazard assessment, volcano monitoring, and land subsidence studies, where InSAR increasingly underpins operational decision-making, this kind of uncertainty quantification is becoming essential.

The work also fits into a vibrant international research effort. Recent studies have compared GNSS local observations with global weather-based models over tropical volcanoes, applied GNSS spatial interpolation for atmospheric correction in New Zealand, and used neural networks trained on GNSS delays for terrain-challenging regions. The Turkish study adds a critical data point from an active continental strike-slip setting, demonstrating that the performance hierarchy of correction methods is context-dependent. As Sentinel-1 and future radar missions deliver ever more frequent global coverage, and as GNSS networks densify, the message from Elazığ–Sivrice is clear: the atmosphere will always leave its fingerprint on radar interferograms, and taming it demands both better correction products and smarter validation of when, where, and how much those products actually help.

Subject of Research: Comparative assessment of GNSS- and GACOS-based tropospheric corrections for InSAR-derived surface deformation from the 2020 Elazığ–Sivrice earthquake

Article Title: Comparative assessment of GNSS- and GACOS-based tropospheric corrections for InSAR-derived surface deformation: a case study of the 2020 Elazığ–Sivrice earthquake

Article References: Ünlütürk, N. T., & Doğan, U. (2026). Comparative assessment of GNSS- and GACOS-based tropospheric corrections for InSAR-derived surface deformation: a case study of the 2020 Elazığ–Sivrice earthquake. Earth Science Informatics, 19(10), Article 182. https://doi.org/10.1007/s12145-026-02236-1

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02236-1

Keywords: InSAR, GNSS, GACOS, tropospheric correction, surface deformation, Elazığ–Sivrice earthquake, Sentinel-1A, DInSAR, East Anatolian Fault Zone, SNAPHU, atmospheric delay, Earth Science Informatics