New SAR method sharpens deformation monitoring

By AI, Created 10:46 AM UTC, May 20, 2026, /AGP/ – Researchers in China developed PolJSInSAR, a radar-processing framework that improves phase recovery for ground deformation monitoring in noisy, complex terrain. The method could help make InSAR measurements denser and more reliable for geohazards, infrastructure checks, and land-surface change analysis.

Why it matters: - Better phase recovery can make interferometric synthetic aperture radar, or InSAR, more useful in places where standard methods struggle, including mountains, vegetated slopes and other complex terrain. - Denser deformation maps improve monitoring for landslides, mining subsidence, glacier drift, seismic deformation and infrastructure stability. - The approach could also support geohazard warning and wider land-surface change analysis.

What happened: - Researchers from China University of Geosciences (Beijing), the Aerospace Information Research Institute of the Chinese Academy of Sciences, the University of Chinese Academy of Sciences, the Key Laboratory of Target Cognition and Application Technology, and the Institute of Software, Chinese Academy of Science reported a new phase-linking method in Journal of Remote Sensing on March 4, 2026. - The paper is published at the study DOI. - The team introduced PolJSInSAR, a multipolarimetric multibaseline SAR phase-linking framework that combines statistical pixel screening, scattering-mechanism separation and phase estimation.

The details: - PolJSInSAR adds joint-scatterer processing to multipolarimetric multibaseline phase linking for the first time. - The framework includes PolTSLR, a polarimetric time-series likelihood ratio test that identifies statistically homogeneous pixels more robustly. - The method also uses JSKP, a joint sum of Kronecker product decomposition, to separate dominant scattering mechanisms before phase optimization. - The authors derived phase estimators under both array signal processing and maximum likelihood frameworks. - The method converts full-polarization SAR observations into a lexicographic basis and models neighboring pixels jointly instead of treating each target independently. - JSKP expresses the signal as a polarimetric signature, a joint-scatterer signature and a structure matrix. - In simulations with 3 dB noise, the method recovered scattering information with low errors, including RMSE values of 0.1151 and 0.1274 rad for two simulated mechanisms. - In phase-linking tests, phase RMSE fell to 0.2252 rad, lower than SqueeSAR, JSInSAR, TP and MLE-MPPL. - PolTSLR produced the best homogeneous-pixel identification performance among six tested methods, with an area under the ROC curve of 0.9203. - In 1,000 Monte Carlo experiments, PolJSInSAR stayed closest to the Cramér–Rao lower bound.

Between the lines: - The main technical shift is that PolJSInSAR separates dominant scattering behavior before phase estimation, which can make inversion cleaner and more compact. - That design points to a broader signal-processing framework, not just a single deformation-monitoring tool. - The authors say the decomposition strategy could also be useful for tomographic SAR and polarimetric tree-height measurement. - The study pairs theoretical derivation with simulation and benchmark testing, which strengthens the case that the gains are not limited to one narrow dataset.

What’s next: - The method could be used to produce denser and more reliable deformation monitoring in areas where conventional InSAR is sparse. - Potential applications include geohazard early warning, infrastructure surveillance and wide-area land-motion tracking. - The authors also point to possible extensions for dual-polarization data and other polarimetric SAR tasks. - Future work will likely focus on reducing computational cost, since the matrix decomposition steps are heavier than conventional workflows.

The bottom line: - PolJSInSAR is designed to extract cleaner phase information from difficult SAR scenes, which could expand where high-resolution deformation monitoring is practical.