Fadwa Alshawaf

Constructing accurate maps of atmospheric water vapor by combining interferometric synthetic aperture radar and GNSS observations

Over the past 20years, repeat-pass spaceborne interferometric synthetic aperture radar (InSAR) has been widely used as a geodetic technique to generate maps of the Earths topography and to measure the Earths surface deformation. In this paper, we present a new approach to exploit microwave data from InSAR, particularly Persistent Scatterer InSAR (PSI), and Global Navigation Satellite Systems (GNSS) to derive maps of the absolute water vapor content in the Earths atmosphere. Atmospheric water vapor results in a phase shift in the InSAR interferogram, which if successfully separated from other phase components provides valuable information about its distribution. PSI produces precipitable water vapor (PWV) difference maps of a high spatial density, which can be inverted using the least squares method to retrieve PWV maps at each SAR acquisition time. These maps do not contain the absolute (total) PWV along the signal path but only a part of it. The components eliminated by forming interferograms or phase filtering during PSI data processing are reconstructed using GNSS phase observations. The approach is applied to build maps of absolute PWV by combining data from InSAR and GNSS over the region of Upper Rhine Graben in Germany and France. For validation, we compared the derived PWV maps with PWV maps measured by the optical sensor MEdium-Resolution Imaging Spectrometer. The results show strong spatial correlation with values of uncertainty of less than 1.5mm. Continuous grids of PWV are then produced by applying the kriging geostatistical interpolation technique that exploits the spatial correlations between the PWV observations.

Accurate Estimation of Atmospheric Water Vapor Using GNSS Observations and Surface Meteorological Data

Remote sensing data have been increasingly used to measure the content of water vapor in the atmosphere and to characterize its temporal and spatial variations. In this paper, we use observations from Global Navigation Satellite System(s) (GNSS) to estimate time series of precipitable water vapor (PWV) by applying the technique of precise point positioning. For an accurate quantification of the absolute PWV, it is necessary to combine the GNSS observations with meteorological data measured directly or inferred at the GNSS site. In addition, measurements of the surface temperature are used to calculate the empirical constant required to convert the GNSS-based delay into water vapor. Our results show strong agreement between the total precipitable water estimated based on GNSS observations and that measured by the sensor MEdium Resolution Imaging Spectrometer with a mean RMS value of 0.98 mm. In a similar way, we compared the GNSS-based total PWV estimates with those produced by the Weather Research and Forecasting (WRF) Modeling System. We found that the WRF model simulations agree well with the GNSS estimates with a mean RMS value of 0.97 mm.

Compressive sensing for neutrospheric water vapor tomography using GNSS and InSAR observations

This paper presents the innovative Compressive Sensing (CS) concept for tomographic reconstruction of 3D neutrospheric water vapor fields using data from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). The Precipitable Water Vapor (PWV) input data are derived from simulations of the Weather Research and Forecasting modeling system. We apply a Compressive Sensing based approach for tomographic inversion. Using the Cosine transform, a sparse representation of the water vapor field is obtained. The new aspects of this work include both the combination of GNSS and InSAR data for water vapor tomography and the sophisticated CS estimation: The combination of GNSS and InSAR data shows a significant improvement in 3D water vapor reconstruction; and the CS estimation produces better results than a traditional Tikhonov regulari-zation with l2 norm penalty term.

Analysis of atmospheric signals in spaceborne InSAR - toward water vapor mapping based on multiple sources

The dominant error source for short wavelength spaceborne radar signals is due to water vapor present in the neutral atmosphere (neutrosphere). This distortion signal is characterized by high variations in time and space, and can be exploited as a valuable source for quantifying the water vapor content of the Earths atmosphere. Available water vapor measurements provided by Envisat Medium Resolution Imaging Spectrometer (MERIS) and simulations from numerical weather prediction models are still limited in observing rapid fluctuations of water vapor. Therefore, we are investigating Interferometric Synthetic Aperture Radar (InSAR) for water vapor mapping. In this paper, water vapor maps derived from Persistent Scatterer InSAR (PSI), MERIS, and the Weather Research and Forecasting (WRF) model are presented with comparative analyses.

Integration of InSAR and GNSS Observations for the Determination of Atmospheric Water Vapour

High spatially and temporally variable atmospheric water vapour causes an unknown delay in microwave signals transmitted by space-borne sensors. This delay is considered as a major limitation in Interferometric Synthetic Aperture Radar (InSAR) applications as well as high-precision applications of Global Navigation Satellite Systems (GNSS). On the other hand, the delay could be quantified to derive atmospheric parameters such as water vapour. Temporal variability of water vapour is well estimated from ongoing GNSS measurements, while InSAR provides information about the spatial variations of water vapour. This project aims at assimilating InSAR phase observations and spatially-sparse GNSS measurements for the determination of atmospheric water vapour. In this contribution GNSS-based water vapour calculations and assessment of different strategies are presented. Work in progress is also reported including some preliminary results.

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

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.

Towards a rigorous fusion of GNSS and InSAR observations for the purpose of water vapor retrieval

In the framework of the rigorous fusion of GNSS and InSAR observations, the presented work carries out at a straightforward comparison of the wet delay, caused by water vapor, derived from GNSS and InSAR. The contributions of the two sensors to the wet delay are compared in the line of sight towards the SAR satellite. Comparisons of GNSS observations with the satellite-directed InSAR data show that only a partial component of the wet delay remains after the interferogram formation.