Improving Wet Refractivity Estimation via GNSS Tropospheric Tomography: A Case Study in the Northwest and North of Iran

8 citations as recorded by crossref.

1. Improving Empirical Pressure Estimation Over Mainland China and Surrounding Regions Using Machine Learning L. Cao et al. https://doi.org/10.1109/JSTARS.2026.3735256
2. A multi-temporal local spatio-temporal kriging approach for high-resolution estimation of atmospheric precipitable water vapor using GNSS and Sentinel-3 observations A. Tayfehrostami & Y. Amerian https://doi.org/10.1016/j.jastp.2026.106910
3. An uncertainty-aware machine learning framework for calibrating GNSS radio occultation precipitable water vapor against radiosonde observations over Iran F. Hedayati et al. https://doi.org/10.1016/j.asr.2026.10.002
4. A novel PWV gradient-based method for typhoon inversion exemplified by Typhoon Wipha (2025) in Hong Kong Q. Kong et al. https://doi.org/10.1016/j.asr.2026.08.057
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7. A Structural Knowledge-Guided Deep Learning for Retrieving Precipitable Water Vapor From Satellite Remote Sensing Observations X. Yao et al. https://doi.org/10.1109/TGRS.2026.3735969
8. The High-Accuracy InSAR PWV Retrieval Methods Based on Machine-Learning Fusing Multisource Data Y. Zhao et al. https://doi.org/10.1109/JSTARS.2026.3736095