Analyzing an InSAR short-term systematic phase bias with regards to soil moisture and landcover

This work investigates a systematic phase bias affecting Synthetic Aperture Radar interferograms, in particular at short-term, causing biases in displacement velocity estimates that can reach several mm per year ([1]).
The analysis relies on the processing of a stack of Single Look Complex SAR images; in our case, the stack consists in 184 Sentinel-1 images acquired regularly between 2014 and 2018 and covering the Eastern part of Sicily. A reference phase history is derived using the EMI method (Eigen-decomposition-based Maximum-likelihood estimator of Interferometric phase), which takes advantage of the full sample covariance matrix built out of all the SAR acquisitions at a given pixel. This phase history has been shown to be equivalent to a persistent scatterer’s phase history over our region of interest. We use it to calibrate the direct multilooked interferograms built out of consecutive acquisitions. The short-term phase bias signal thus obtained is analyzed in time and space, making use in addition of ASCAT soil moisture variations and landcover information from the CORINE dataset.
We observe that for certain land classes, the high-frequency part of the signal is correlated with soil moisture variations in both dry and wet seasons. The low-pass trend exhibits strongly seasonal variations, with maxima of comparable value in spring (April-May) of each year. Areas with similar landcover types (forests, vegetated areas, agricultural areas) witness similar phase biases behavior, indicating a physical contribution associated with vegetation effects.
By investigating the behavior of the bias, this study contributes towards a future mitigation of this phase error in deformation estimates, or the exploitation of the bias itself as a physically relevant signal.

[1] H. Ansari, F. De Zan and A. Parizzi, "Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3003421.