TY - JOUR
T1 - Time series analysis of InSAR data
T2 - Methods and trends
AU - Osmanoğlu, Batuhan
AU - Sunar, Filiz
AU - Wdowinski, Shimon
AU - Cabral-Cano, Enrique
N1 - Funding Information:
The authors would like to thank ESA for making the Envisat data over Mexico City available through research grants. Part of this research was funded by NASA Grant NNX12AK23G . Authors would like to thank Penelope Lopez-Quiroz, and Gabriela Siles, for sharing their results and assistance. We kindly acknowledge TRE Canada Inc. for providing the SqueeSAR™ results on Mexico City.
Publisher Copyright:
© 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
PY - 2016/5/1
Y1 - 2016/5/1
N2 - 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.
AB - 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.
KW - InSAR.
KW - Multi-temporal InSAR.
KW - Persistent scatterer InSAR.
KW - Small baselines subset
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U2 - 10.1016/j.isprsjprs.2015.10.003
DO - 10.1016/j.isprsjprs.2015.10.003
M3 - Short survey
AN - SCOPUS:84949591355
VL - 115
SP - 90
EP - 102
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -