More Than Just Noise: Effects of Vegetation and Agriculture on InSAR Time Series Analysis
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Interferometric synthetic aperture radar (InSAR) is a geodetic technique that is often used to study ground displacement, but it is also affected by surface properties such as vegetation and soil moisture. This poses a challenge for InSAR research over agricultural areas due to rapid temporal variability and biases associated with different stages of the agricultural cycle. Radar interactions with crops also differ based on radar wavelength and polarization, so the observed biases depend on the satellite platform used by the researcher. In this dissertation, I quantify the apparent deformation that can be introduced into InSAR time series by microwave interactions within crops. First, I analyze a two year C-band Sentinel-1 time series over the San Joaquin Valley, CA. I isolate the effects of crops on InSAR phase by separating pixels based on crop and land cover type. I find that certain crops, such as cotton, are associated with an average bias of ~2-4 cm/yr of apparent subsidence in InSAR time series analysis, with even higher rates during some parts of the phenological cycle. I also compare this C-band Sentinel-1 dataset with contemporaneous L-band ALOS-2 data to examine differences in how each wavelength interacts with the same set of crop types. I find that the largest differences between the C-band and L-band response are within orchard-type crops (e.g., almonds, pistachios). Finally, I compare both co- and cross-polarized SAR with an expanded dataset to include crops grown to the south, in the Imperial Valley, CA. The fact that the co- and cross-polarized images are acquired simultaneously means that considerations such as the effect of tropospheric water vapor can be disregarded, simplifying our analysis. Since signals associated with ground uplift or subsidence should appear the same in both co- and cross-polarized interferograms, I am able to attribute any differences to scattering properties associated with vegetation structure and soil moisture. I demonstrate an empirical correction for the InSAR phase associated with each crop type, and show how this correction would impact the inferred subsidence in both the San Joaquin and Imperial Valleys.