InSAR theory and techniques

Recent advances in satellite remote sensing have enabled continued global monitoring of our changing Earth with finer spatiotemporal resolution than could ever be achieved before. With exponential growth in data volume, a significant challenge in earth science research is to efficiently and accurately utilize these data to solve the world’s enormous resource and environmental issues.

The central goal of my research is to promote the use of modern geodetic datasets – primarily interferometric synthetic aperture radar (InSAR) measurements to study Earth’s surface changes due to natural and anthropological processes. I am interested in advancing theories and developing algorithms to produce InSAR-based displacement products that have higher resolution, accuracy, and shorter latency.

In the past, my collaboraters and I have worked on developing geocoded SAR InSAR processing algorithm; developing covariance matrix for InSAR decorrelation noise and investigating InSAR closure phase and its impacts on InSAR time-series.

In the future, I am interested in combining statistical models, machine learning methods, and change detection algorithms to facilitate the production of analysis-ready data in a live system. I am particularly excited about InSAR’s ability to measure small signals in noisy environments, an ability that is greatly enhanced with the modern constellation of SAR satellites.