Interferometric synthetic aperture radar (InSAR) provides capability to detect surface deformation. Between two SAR images, in addition to ground surface deformations, changes may happen in the surface parameters such as soil moisture, vegetation layer water content, and so on. Despite deformation, the other changes may not be of interest for a common InSAR analysis and an ordinary InSAR analysis rarely takes into account their influences on InSAR phase and coherence. The effect of the changes on InSAR phase and coherence can potentially impede accurate estimation of ground surface deformation but also can open new window into soil moisture retrieval and vegetation layer properties estimation.

So far, numerous InSAR processing methods such as persistent scatterers (PS), and short baseline subset (SBAS) approaches have been introduced. Regardless of the processing methodology, however, temporal decorrelation is a major obstacle for all InSAR applications especially over vegetated areas and dynamic environments. Temporal coherence is usually modeled as a univariate exponential function of temporal baseline. Here, I introduce a new temporal decorrelation model that considers changes in surface backscattering by utilizing the relative change in SAR intensity between two images as a proxy for the change in surface scattering parameters.

The phase of interferograms generated from single-looked pixels are rather noisy due to strong effects of decorrelations and noises. One way to deal with this problem is using multi-looked interferograms. Another approach is exploring methods such as SqueeSAR, and the component extraction and selection SAR (CAESAR), which have been developed to extend Persistent Scatterers Interferometry (PSI) analysis. Multi-looking, however, leads to non-zero phase triplet. I analyze the influence of the statistical properties of intensity and phase of single-looked pixels on the phase and coherence of multi-looked pixels.

Potentially, modeling soil moisture influence on InSAR phase measurements and SAR intensity changes provides a means to compensate soil moisture induced InSAR phase artifacts and also to retrieve surface soil moisture. I present a new approach and a comprehensive model to estimate soil moisture induced SAR intensity and InSAR phase changes. The model can not only provide improved estimation of soil moisture induced intensity and phase changes but also potentially be used to infer soil structure.

Degree Date

Spring 5-16-2020

Document Type


Degree Name



Earth Sciences


Zhong Lu

Subject Area

Physical Sciences

Number of Pages