Subsidence through space and time in the lake Mead area : Insights from cross-platform ERS/Envisat interferometry.

Marie-Pierre Doin(1) , Olivier Cavalie(1) , and Cecile Lasserre(1)

(1) Ecole Normale Supérieure, 24 rue Lhomond, 75231 Paris Cedex 05, France


Interferograms from the archived SAR data set of ERS-1 and ERS-2 satellites have been calculated and inverted to retrieve the temporal and spatial subsidence around lake Mead between 1992 and 2001 (see Cavalie et al., this session). It was shown that the ground motion evolution can be closely associated with the load/unload of lake Mead water level fluctuations on a elastic or viscoelastic surface. From 2000 to 2004, the lake level fell drastically, leading a regional uplift of a few centimers. In order to record this uplift, we perform a large number of cross-platform ERS/Envisat interferograms using the JPL/Caltech Roipac software. We discuss here the additional steps included in the data treatment, that allow to retrieve a good spatial distribution of the exploitable phase in the scene. The resulting ERS/Envisat interferograms, and Envisat/Envisat interferograms are then included in the inversion to obtain the spatial ground subsidence from 1992 to 2005.

Perpendicular baselines chosen for ERS/Envisat interferograms stand between 1300 m and 2200 m, according to the spectral shift principle (Gatelli et al., 1994). To increase the interferogram phase coherence, the SAR synthesis is performed with varying range frequency filtering and an optimal azimuth frequency filtering (Colesanti et al., 2004). The best common band filter in range that improves the local interferogram coherence varies in sign and amplitude across the scene, depending on the local look angle. We therefore combine the results of the various filters to obtain an interferogram with a coherency as good as possible.

The rate of fringes in the obtained ERS/Envisat interferograms is extremely high due to the small elevation ambiguities (large baselines) and because orbital fringes do not everywhere compensate frequency difference fringes. We thus remove a first model of orbital fringes, frequency difference fringes, and topographic fringes, using the SRTM DEM. This process strongly reduces the fringe pattern. However, the SRTM DEM correction also introduces noise, that must be filtered out in a next step by strong spatial filtering of the differential interferogram. In mountainous areas, the rate of topographic fringes is too large in comparison to the pixel size, and the DEM accuracy is not sufficient to correct the interferogram. Finally, we retrieve the differential interferogram phase only in flat or moderately steeping areas.

To continue improving the spatial coverage of retrieved interferometric phase, we perform linear combinations of wrapped Envisat/ERS differential interferograms and unwrapped ERS/ERS interferograms, and use the data redundancy to select the phase with the best coherence. The residual large scale pattern of fringes can then be removed to flatten the interferogram on the scene sides, and spatial unwrapping is performed by bridging manually non adjacent patches. Unwrapped and flattened differential interferograms are then included in the inversion process described by Cavalie et al., this session.



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