49 citations as recorded by crossref.

1. Retrieval of Tropospheric Refractivity Profiles Using Slant Tropospheric Delays Derived From a Single Ground‐Based Global Navigation Satellite System Station P. Xia et al. https://doi.org/10.1029/2019EA000562
2. Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing M. Heublein et al. https://doi.org/10.5194/angeo-38-179-2020
3. Improving the Wet Refractivity Estimation Using the Extremely Learning Machine (ELM) Technique E. Forootan et al. https://doi.org/10.3390/atmos14010112
4. An Adaptive Non-Uniform Vertical Stratification Method for Troposphere Water Vapor Tomography H. Wang et al. https://doi.org/10.3390/rs13193818
5. An Improved Tropospheric Tomographic Model Based on Artificial Neural Network M. Zhang et al. https://doi.org/10.1109/JSTARS.2023.3278302
6. A Two-Step Projected Iterative Algorithm for Tropospheric Water Vapor Tomography S. Liu et al. https://doi.org/10.1109/JSTARS.2022.3192437
7. GNSS ground-based tomography: state-of-the-art and technological challenges S. Saxena & R. Dwivedi https://doi.org/10.1080/01431161.2023.2247526
8. Enhanced ground-based GNSS tomography for accurate water vapor retrieval X. Pengfei et al. https://doi.org/10.1016/j.jastp.2025.106709
9. A GPS water vapour tomography method based on a genetic algorithm F. Yang et al. https://doi.org/10.5194/amt-13-355-2020
10. A Tropospheric Tomography Method with a Novel Height Factor Model Including Two Parts: Isotropic and Anisotropic Height Factors W. Zhang et al. https://doi.org/10.3390/rs12111848
11. Retrieval of refractivity fields from GNSS tropospheric delays: theoretical and data-based evaluation of collocation methods and comparisons with GNSS tomography E. Shehaj et al. https://doi.org/10.1007/s00190-024-01903-9
12. Troposphere Water Vapour Tomography: A Horizontal Parameterised Approach Q. Zhao et al. https://doi.org/10.3390/rs10081241
13. Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations M. Heublein et al. https://doi.org/10.1007/s00190-018-1152-0
14. A method for predicting short‐time changes in fine particulate matter (PM2.5) mass concentration based on the global navigation satellite system zenith tropospheric delay M. Guo et al. https://doi.org/10.1002/met.1866
15. A Method to Improve the Distribution of Observations in GNSS Water Vapor Tomography F. Yang et al. https://doi.org/10.3390/s18082526
16. An Optimal Weighting Method of Global Positioning System (GPS) Troposphere Tomography J. Guo et al. https://doi.org/10.1109/JSTARS.2016.2546316
17. Enhanced Troposphere Tomography: Integration of GNSS and Remote Sensing Data With Optimal Vertical Constraints S. Izanlou et al. https://doi.org/10.1109/JSTARS.2024.3354884
18. An improved troposphere tomographic approach considering the signals coming from the side face of the tomographic area Q. Zhao & Y. Yao https://doi.org/10.5194/angeo-35-87-2017
19. Monitoring urban heat island intensity based on GNSS tomography technique P. Xia et al. https://doi.org/10.1007/s00190-023-01804-3
20. An improved ridge estimation (IRE) method for troposphere water vapor tomography Q. Zhao et al. https://doi.org/10.1016/j.jastp.2020.105366
21. 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
22. Adaptive Node Parameterization for Dynamic Determination of Boundaries and Nodes of GNSS Tomographic Models N. Ding et al. https://doi.org/10.1002/2017JD027748
23. Optimization of GPS water vapor tomography technique with radiosonde and COSMIC historical data S. Ye et al. https://doi.org/10.5194/angeo-34-789-2016
24. Key Technologies for GNSS Water Vapor Retrieval and Their Applications in Marine Environmental Monitoring P. Wang et al. https://doi.org/10.1109/JSTARS.2026.3717583
25. 4D tomographic reconstruction of the tropospheric wet refractivity using the concept of virtual reference station, case study: northwest of Iran Z. Adavi & M. Mashhadi-Hossainali https://doi.org/10.1007/s00703-014-0342-4
26. A new troposphere tomography algorithm with a truncation factor model (TFM) for GNSS networks Q. Zhao et al. https://doi.org/10.1007/s10291-019-0855-x
27. A new approach for GNSS tomography from a few GNSS stations N. Ding et al. https://doi.org/10.5194/amt-11-3511-2018
28. Exploration and analysis of the factors influencing GNSS PWV for nowcasting applications M. Guo et al. https://doi.org/10.1016/j.asr.2021.02.018
29. LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography S. Xiong et al. https://doi.org/10.3390/rs13163056
30. 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
31. Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm N. Ding et al. https://doi.org/10.3390/rs12172744
32. Atmospheric Water Vapor Variability over Houston: Continuous GNSS Tomography in the Year of Hurricane Harvey (2017) P. Mateus et al. https://doi.org/10.3390/rs16173205
33. Assessing the performance of troposphere tomographic modeling using multi-source water vapor data during Hong Kong's rainy season from May to October 2013 B. Chen & Z. Liu https://doi.org/10.5194/amt-9-5249-2016
34. Development of a hybrid tomography model based on principal component analysis of the atmospheric dynamics and GPS tracking data E. Sadeghi et al. https://doi.org/10.1007/s10291-022-01264-w
35. 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
36. A Novel Tropospheric Tomographic Method Using GNSS and Remote Sensing Signals Based on Machine Learning Techniques M. Zhang et al. https://doi.org/10.1109/TGRS.2026.3692708
37. Assessing water vapor tomography in Hong Kong with improved vertical and horizontal constraints P. Xia et al. https://doi.org/10.5194/angeo-36-969-2018
38. INTOMO operator for GNSS multi-source tomography based on 3D ray tracing technique A. Cegla et al. https://doi.org/10.1007/s00190-024-01915-5
39. Establishing a high-precision real-time ZTD model of China with GPS and ERA5 historical data and its application in PPP P. Xia et al. https://doi.org/10.1007/s10291-022-01338-9
40. A troposphere tomography method considering the weighting of input information Q. Zhao et al. https://doi.org/10.5194/angeo-35-1327-2017
41. Near real-time water vapor tomography using ground-based GPS and meteorological data: long-term experiment in Hong Kong P. Jiang et al. https://doi.org/10.5194/angeo-32-911-2014
42. Application of the GNSS-R in tomographic sounding of the Earth atmosphere M. Jaberi Shafei & M. Mashhadi-Hossainali https://doi.org/10.1016/j.asr.2018.04.003
43. An improved GNSS remote sensing technique for 3D distribution of tropospheric water vapor A. Long et al. https://doi.org/10.1002/met.2136
44. Use of Tropospheric Delay in GNSS-Based Climate Monitoring—A Review A. Maciejewska https://doi.org/10.3390/rs17091501
45. Long-range Single Baseline RTK GNSS Positioning for Land Cadastral Survey Mapping B. Bramanto et al. https://doi.org/10.1051/e3sconf/20199401022
46. A virtual-signal method for enhancing the efficacy of GNSS tropospheric tomography using artificial neural network technique M. Zhang et al. https://doi.org/10.1007/s10291-025-01935-4
47. 4D-tomographic reconstruction of water vapor using the hybrid regularization technique with application to the North West of Iran Z. Adavi & M. Mashhadi-Hossainali https://doi.org/10.1016/j.asr.2015.01.025
48. Reconstruction of Wet Refractivity Field Using an Improved Parameterized Tropospheric Tomographic Technique B. Chen et al. https://doi.org/10.3390/rs12183034
49. DeepTomo: An Explainable Physics-Informed Deep Learning Framework for Troposphere Tomography, Toward AI-Based GNSS Data Assimilation A. Haji-Aghajany et al. https://doi.org/10.1109/TGRS.2026.3714111