Articles | Volume 19, issue 12
https://doi.org/10.5194/hess-19-4747-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-4747-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
03 Dec 2015
Research article |
| 03 Dec 2015
Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations
F. Alshawaf
CORRESPONDING AUTHOR
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
Geodetic Institute, KIT, 76131 Karlsruhe, Germany
B. Fersch
Institute of Meteorology and Climate Research, Campus Alpin, KIT, 82467 Garmisch-Partenkirchen, Germany
S. Hinz
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, Germany
H. Kunstmann
Institute of Meteorology and Climate Research, Campus Alpin, KIT, 82467 Garmisch-Partenkirchen, Germany
M. Mayer
Geodetic Institute, KIT, 76131 Karlsruhe, Germany
F. J. Meyer
Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
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Cited
19 citations as recorded by crossref.
- Fusion of CMONOC and ERA5 PWV Products Based on Backpropagation Neural Network D. Ren et al. 10.3390/rs14153750
- Precipitable water vapor fusion based on a generalized regression neural network B. Zhang & Y. Yao 10.1007/s00190-021-01482-z
- An Improved Principal Component Analysis Method for the Interpolation of Missing Data in GNSS-Derived PWV Time Series D. Zhu et al. 10.3390/rs15215153
- A GRNN-Based Model for ERA5 PWV Adjustment with GNSS Observations Considering Seasonal and Geographic Variations H. Pang et al. 10.3390/rs16132424
- Precipitable water vapor fusion of MODIS and ERA5 based on convolutional neural network C. Lu et al. 10.1007/s10291-022-01357-6
- Comparison of deterministic, probabilistic and machine learning-based methods for bathymetric surface modeling L. de Andrade et al. 10.1007/s40808-024-02189-8
- Remote sensing of atmospheric water vapor from synthetic aperture radar interferometry: case studies in Shanghai, China L. Chang et al. 10.1117/1.JRS.10.046032
- A New Method for Reconstructing Absolute Water Vapor Maps From Persistent Scatterer InSAR F. Alshawaf 10.1109/TGRS.2020.2969459
- Research on the Fusion of PWV over the Beijing-Tianjin-Hebei Region Based on ERA5 PWV and FY-4A PWV W. Du et al. 10.1016/j.asr.2025.01.025
- A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest J. Li et al. 10.5194/gmd-17-2569-2024
- Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics J. Vaquero-Martínez & M. Antón 10.3390/rs13122287
- A method for estimating high spatial resolution total precipitable water in all-weather condition by fusing satellite near-infrared and microwave observations Q. Sun et al. 10.1016/j.rse.2023.113952
- Review of Works Combining GNSS and InSAR in Europe M. Del Soldato et al. 10.3390/rs13091684
- An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach X. Li & D. Long 10.1016/j.rse.2020.111966
- Fusing Precipitable Water Vapor Data in CHINA at Different Timescales Using an Artificial Neural Network Z. Xiong et al. 10.3390/rs13091720
- Real-time high-resolution tropospheric delay mapping based on GFS forecasts and GNSS C. Lu et al. 10.1007/s10291-024-01722-7
- Precipitable water vapor fusion: an approach based on spherical cap harmonic analysis and Helmert variance component estimation B. Zhang et al. 10.1007/s00190-019-01322-1
- Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations M. Heublein et al. 10.1007/s00190-018-1152-0
- An Improved GNSS and InSAR Fusion Method for Monitoring the 3D Deformation of a Mining Area W. Zhou et al. 10.1109/ACCESS.2021.3129521
19 citations as recorded by crossref.
- Fusion of CMONOC and ERA5 PWV Products Based on Backpropagation Neural Network D. Ren et al. 10.3390/rs14153750
- Precipitable water vapor fusion based on a generalized regression neural network B. Zhang & Y. Yao 10.1007/s00190-021-01482-z
- An Improved Principal Component Analysis Method for the Interpolation of Missing Data in GNSS-Derived PWV Time Series D. Zhu et al. 10.3390/rs15215153
- A GRNN-Based Model for ERA5 PWV Adjustment with GNSS Observations Considering Seasonal and Geographic Variations H. Pang et al. 10.3390/rs16132424
- Precipitable water vapor fusion of MODIS and ERA5 based on convolutional neural network C. Lu et al. 10.1007/s10291-022-01357-6
- Comparison of deterministic, probabilistic and machine learning-based methods for bathymetric surface modeling L. de Andrade et al. 10.1007/s40808-024-02189-8
- Remote sensing of atmospheric water vapor from synthetic aperture radar interferometry: case studies in Shanghai, China L. Chang et al. 10.1117/1.JRS.10.046032
- A New Method for Reconstructing Absolute Water Vapor Maps From Persistent Scatterer InSAR F. Alshawaf 10.1109/TGRS.2020.2969459
- Research on the Fusion of PWV over the Beijing-Tianjin-Hebei Region Based on ERA5 PWV and FY-4A PWV W. Du et al. 10.1016/j.asr.2025.01.025
- A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest J. Li et al. 10.5194/gmd-17-2569-2024
- Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics J. Vaquero-Martínez & M. Antón 10.3390/rs13122287
- A method for estimating high spatial resolution total precipitable water in all-weather condition by fusing satellite near-infrared and microwave observations Q. Sun et al. 10.1016/j.rse.2023.113952
- Review of Works Combining GNSS and InSAR in Europe M. Del Soldato et al. 10.3390/rs13091684
- An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach X. Li & D. Long 10.1016/j.rse.2020.111966
- Fusing Precipitable Water Vapor Data in CHINA at Different Timescales Using an Artificial Neural Network Z. Xiong et al. 10.3390/rs13091720
- Real-time high-resolution tropospheric delay mapping based on GFS forecasts and GNSS C. Lu et al. 10.1007/s10291-024-01722-7
- Precipitable water vapor fusion: an approach based on spherical cap harmonic analysis and Helmert variance component estimation B. Zhang et al. 10.1007/s00190-019-01322-1
- Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations M. Heublein et al. 10.1007/s00190-018-1152-0
- An Improved GNSS and InSAR Fusion Method for Monitoring the 3D Deformation of a Mining Area W. Zhou et al. 10.1109/ACCESS.2021.3129521
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Latest update: 31 Jan 2025
Short summary
This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion data from Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite Systems (GNSS), and the Weather Research and Forecasting (WRF) model. The data fusion approach exploits the redundant and complementary spatial properties of all data sets to provide more accurate and high-resolution maps of water vapor. The comparison with maps from MERIS shows rms values of less than 1 mm.
This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion...
