Abstract
A recent development in Interferometric Synthetic Aperture Radar (InSAR) technology is integrating multiple SAR satellite data to dynamically extract ground features. This paper addresses two relevant challenges: identification of common ground targets from different SAR datasets in space, and concatenation of time series when dealing with temporal dynamics. To address the first challenge, we describe the geolocation uncertainty of InSAR measurements as a three-dimensional error ellipsoid. The points, among InSAR measurements, which have error ellipsoids with a positive cross volume are identified as tie-point pairs representing common ground objects from multiple SAR datasets. The cross volumes are calculated using Monte Carlo methods and serve as weights to achieve the equivalent deformation time series. To address the second challenge, the deformation time series model for each tie-point pair is estimated using probabilistic methods, where potential deformation models are efficiently tested and evaluated. As an application, we integrated two Radarsat-2 datasets in Standard and Extra-Fine modes to map the subsidence of the west of the Netherlands between 2010 and 2017. We identified 18128 tie-point pairs, 5 intersection types of error ellipsoids, 5 deformation models, and constructed their long-term deformation time series. The detected maximum mean subsidence velocity in Line-Of-Sight direction is up to 15 mmyr-1. We conclude that our method removes limitations that exist in single-viewing-geometry SAR when integrating multiple SAR data. In particular, the proposed time-series modeling method is useful to achieve a long-term deformation time series of multiple datasets.
Highlights
Interferometric Synthetic Aperture Radar (InSAR) is a reliable geodetic technique for mapping surface and monitoring its changes
An InSAR time series analysis is conducted by a standard PSI method, using e.g. the Delft implementation of PS Interferometry (DePSI) (Kampes, 2006; Van Lei jen, 2014)
To evaluate sub-optimal models against the detected most probable model, and identify the best model with the minimum posterior variance and a lower parameter number, we propose the optimal method to obtain the final deformation models upon the maximum test ratio method
Summary
Interferometric Synthetic Aperture Radar (InSAR) is a reliable geodetic technique for mapping surface and monitoring its changes. Merely using a linear model to concatenate InSAR deformation time series is not appropriate To overcome these challenges, this research proposes a data postprocessing method to precisely integrate InSAR deformation time se ries in time and space. This research proposes a data postprocessing method to precisely integrate InSAR deformation time se ries in time and space It demonstrates this method using medium and high resolution (Radarsat-2) SAR data.
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More From: ISPRS Journal of Photogrammetry and Remote Sensing
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