21 citations as recorded by crossref.

1. A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network Y. Wang et al. https://doi.org/10.3390/rs12040617
2. Enhancing GNSS water vapor tomography using machine learning-generated virtual tropospheric delays N. Huang et al. https://doi.org/10.1016/j.asr.2026.04.084
3. Wide-Area Retrieval of Water Vapor Field Using an Improved Node Parameterization Tomography B. Chen et al. https://doi.org/10.1109/LGRS.2023.3293824
4. A GPS water vapour tomography method based on a genetic algorithm F. Yang et al. https://doi.org/10.5194/amt-13-355-2020
5. TOMOREF Operator for Assimilation of GNSS Tomography Wet Refractivity Fields in WRF DA System E. Trzcina et al. https://doi.org/10.1029/2020JD032451
6. Adaptive Node Parameterization for Dynamic Determination of Boundaries and Nodes of GNSS Tomographic Models N. Ding et al. https://doi.org/10.1002/2017JD027748
7. Reconstruction of Wet Refractivity Field Using an Improved Parameterized Tropospheric Tomographic Technique B. Chen et al. https://doi.org/10.3390/rs12183034
8. A new integrated method of GNSS and MODIS measurements for tropospheric water vapor tomography W. Zhang et al. https://doi.org/10.1007/s10291-021-01114-1
9. Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution E. Trzcina et al. https://doi.org/10.1007/s00190-022-01691-0
10. GNSS ground-based tomography: state-of-the-art and technological challenges S. Saxena & R. Dwivedi https://doi.org/10.1080/01431161.2023.2247526
11. An improved tropospheric tomography method based on the dynamic node parametrized algorithm W. Zhang https://doi.org/10.13168/AGG.2020.0014
12. An Adaptive Non-Uniform Vertical Stratification Method for Troposphere Water Vapor Tomography H. Wang et al. https://doi.org/10.3390/rs13193818
13. LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography S. Xiong et al. https://doi.org/10.3390/rs13163056
14. A new hybrid observation GNSS tomography method combining the real and virtual inverted signals W. Zhang et al. https://doi.org/10.1007/s00190-021-01576-8
15. A new method for tropospheric tomography using GNSS and Fengyun-4A data M. Zhang et al. https://doi.org/10.1016/j.atmosres.2022.106460
16. GNSS-RS Tomography: Retrieval of Tropospheric Water Vapor Fields Using GNSS and RS Observations W. Zhang et al. https://doi.org/10.1109/TGRS.2021.3077083
17. A new parameterized approach for ionospheric tomography B. Chen et al. https://doi.org/10.1007/s10291-019-0893-4
18. Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm N. Ding et al. https://doi.org/10.3390/rs12172744
19. Spatiotemporal analysis of precipitable water vapor using ANFIS and comparison against voxel-based tomography and radiosonde M. Ghaffari Razin & S. Inyurt https://doi.org/10.1007/s10291-021-01184-1
20. A functional modelling approach for reconstructing 3 and 4 dimensional wet refractivity fields in the lower atmosphere using GNSS measurements E. Forootan et al. https://doi.org/10.1016/j.asr.2021.08.012
21. A new approach for GNSS tomography from a few GNSS stations N. Ding et al. https://doi.org/10.5194/amt-11-3511-2018