A Physics-Based Decorrelation Phase Covariance Model for Effective Decorrelation Noise Reduction in Interferogram Stacks


Here we present a physics-based decorrelation phase covariance model and discuss its role in effective decorrelation noise reduction in interferogram stacks. We test our model in both Cascadia - a rapidly decorrelating region, and Death Valley - a slowly decorrelating region, with observations collected by Sentinel-1. We find that in Cascadia, including redundant interferograms in the stack reduces phase variance from 0.28 rad 2 to 0.04 rad 2 , while in Death Valley, both redundant and independent interferogram stacking yield phase variances of 0.10 rad 2 . Both observations are consistent with predictions from our model. Comparing with three existing decorrelation phase covariance models, our proposed model matches observations with the smallest average discrepancy between theory and observations - 0.017 rad 2 in Cascadia and 0.066 rad 2 in Death Valley.

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
Yujie Zheng
Yujie Zheng
Assistant Professor

My research focuses on developing and applying new techniques to analyze a combination of geodetic observations to better understanding changes of the Earth’s surface related to natural and anthropogenic processes.