Conference Agenda

The closure phase, constructed by a circular summation of three interferometric phases, each obtained from multilooking a SAR interferogram, consists of a geophysical component and phase noise, often exhibits non-zero values. These non-conservative closure phases challenge the validity of the implicit phase consistency assumption in SAR interferometry. This assumption relies on a geometric interpretation of the interferometric phase, where the expected values of the three interferometric phases are redundant, given that their sum, the closure phase, is equal to zero. Quantifying the statistical significance of non-zero closure phases across different land cover types and examining their correlation with geophysical variations are essential for assessing their effect on interferometric phases and, consequently, improving the accuracy of InSAR applications, such as deformation analysis.

We conducted an extensive spatiotemporal statistical analysis using Sentinel-1 acquisitions over the Iberian Peninsula, a region encompasses diverse land cover types and spans multiple climate zones. The results indicate that the non-zero closure phases are statistically significant. Our case study over agricultural fields revealed a clear geophysical signature strongly associated with vegetation phenology[1]. Two primary mechanisms have been proposed to explain the observed signature: variations in dielectric properties and the line-of-sight motion induced by vegetation growth. This C-band study has highlighted the potential of using closure phase observations to detect variations in vegetation water content. However, Sentinel-1 observations are limited in their ability to exploit the physical processes underlying the closure phase signals. The temporal sparsity of orbital passes and the lack of vertical resolution of contributing scatterers restrict the extent to which closure phases can be investigated and exploited.

Analyzing closure phase observations derived from Biomass P-band tomographic data will enhance the understanding of closure phase signatures, as these observations provide different temporal baselines, greater penetration depth, and the ability to resolve the vertical distribution of scatterers. This study aims to statistically quantify P-band closure phase signatures across diverse land cover types, including bare soil, croplands, forests, and glaciers, using multilooked interferograms. We begin by estimating the standard deviation of the closure phases under the null hypothesis that they originate solely from phase noise, followed by an assessment of the statistical significance of geophysical closure phases across sub-regions categorized by different land cover and climate conditions. Subsequently, we evaluate the mean values and percentiles of closure phases to investigate their spatiotemporal variability. When possible, we compare these results with Sentinel-1, depending on data availability and acquisition location.

Our statistical analysis results obtained from multi-sensor observations will enhance the interpretation of interferometric phase signals and provide valuable insights for explicitly accounting for closure phases in SAR stack processing. These findings will also support the development of novel techniques for bio-geophysical parameter retrieval, complementing traditional radar observables such as backscatter and interferometric coherence.

References

[1] Yan Yuan, Marcel Kleinherenbrink, and Paco López-Dekker. On crop growth and insar closure phases. IEEE Transactions on Geoscience and Remote Sensing, 2024.