Repeat-Pass Interferometric Speckle

The Gaussian speckle model for homogeneously distributed targets is commonly assumed to apply in repeat pass radar interferometric analyses, for instance, in deformation estimation. This is despite widespread evidence from snapshot intensity observations indicating deviations from Gaussianity, as many natural land surfaces are intrinsically heterogeneous. The concern is that neglecting heterogeneity will deteriorate the phase estimates and induce underestimation of the uncertainty. Here, we introduce and theoretically characterize compound models that extend the Gaussian speckle model for repeat-pass stacks by representing heterogeneity in intensity and phase. In two L-band repeat-pass data sets, we find pervasive deviations from Gaussianity. Our estimates suggest that the heterogeneity in intensity is largely due to time-invariant, rather than dynamic, texture. Deviations from Gaussianity associated with phase heterogeneity are generally less pronounced. One notable exception with large estimated phase heterogeneity occurs over a permafrost wetland, where degrading ice wedges induce subsidence that is variable on the resolution scale. For deformation analyses, accounting for heterogeneity has, on average, a moderate impact on the phase estimates and the estimated phase uncertainty, which increases by 10% on average. However, in intrinsically heterogeneous areas, such as the permafrost wetland, the accuracy of the phase estimate can realistically improve by up to 20%, and the predicted phase uncertainty increases by 30%. The improvements in phase estimation accuracy and in the quality of the uncertainty estimates when accounting for heterogeneous speckle can, on occasion, make a notable difference for subtle or small-scale deformation.

Automated summary

The study found pervasive deviations from Gaussianity in repeat-pass radar interferometric data, especially in areas with naturally heterogeneous surfaces. Permanent texture has a significant impact on intensity heterogeneity related to phase heterogeneity. Accounting for heterogeneity has a moderate impact on phase estimates and estimated uncertainty in deformation analyses, but can improve phase estimation accuracy and uncertainty estimates in inherently heterogeneous areas.