Time series analysis of InSAR data: Methods and trends

Batuhan Osmanoğlu, Filiz Sunar, Shimon Wdowinski, Enrique Cabral-Cano

Research output: Contribution to journalArticle

94 Scopus citations

Abstract

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal "unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Original languageEnglish (US)
JournalISPRS Journal of Photogrammetry and Remote Sensing
DOIs
StateAccepted/In press - 2015

Keywords

  • InSAR
  • Multi-temporal InSAR
  • Persistent scatterer InSAR
  • Small baselines subset

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Computers in Earth Sciences
  • Engineering (miscellaneous)
  • Geography, Planning and Development
  • Computer Science Applications

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