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Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations

Heublein, Marion and Alshawaf, Fadwa and Erdnüß, Bastian and Zhu, Xiao Xiang and Hinz, Stefan (2018) Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations. Journal of Geodesy, pp. 1-21. Springer. ISSN 0949-7714.

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Official URL: https://link.springer.com/article/10.1007/s00190-018-1152-0


In this work, the reconstruction quality of an approach for neutrospheric water vapor tomography based on Slant Wet Delays (SWDs) obtained from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) is investigated. The novelties of this approach are (1) the use of both absolute GNSS and absolute InSAR SWDs for tomography and (2) the solution of the tomographic system by means of compressive sensing (CS). The tomographic reconstruction is performed based on (i) a synthetic SWD dataset generated using wet refractivity information from the Weather Research and Forecasting (WRF) model and (ii) a real dataset using GNSS and InSAR SWDs. Thus, the validation of the achieved results focuses (i) on a comparison of the refractivity estimates with the input WRF refractivities and (ii) on radiosonde profiles. In case of the synthetic dataset, the results show that the CS approach yields a more accurate and more precise solution than least squares (LSQ). In addition, the benefit of adding synthetic InSAR SWDs into the tomographic system is analyzed. When applying CS, adding synthetic InSAR SWDs into the tomographic system improves the solution both in magnitude and in scattering. When solving the tomographic system by means of LSQ, no clear behavior is observed. In case of the real dataset, the estimated refractivities of both methodologies show a consistent behavior although the LSQ and CS solution strategies differ.

Item URL in elib:https://elib.dlr.de/120284/
Document Type:Article
Title:Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hinz, Stefanstefan.hinz (at) ipf.uni-karlsruhe.deUNSPECIFIED
Journal or Publication Title:Journal of Geodesy
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 1-21
Keywords:Global navigation satellite system (GNSS), GNSS tomography, SAR interferometry (InSAR), Water vapor tomography, Compressive sensing, Least squares
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Häberle, Matthias
Deposited On:13 Jun 2018 13:39
Last Modified:13 Jun 2018 13:39

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