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Liangke Huang

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Articles

A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval

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By Lilong Liu, Fei Guo, Liangke Huang, Fade Chen
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Assessing the Performance of Water Vapor Products from ERA5 and MERRA-2 during Heavy Rainfall in the Guangxi Region of China

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By Ning Huang, Shiyang Fu, Biyan Chen, Liangke Huang
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Machine Learning-Based Calibrated Model for Forecast Vienna Mapping Function 3 Zenith Wet Delay

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By Feijuan Li, Junyu Li, Lilong Liu, Liangke Huang
| MDPI
4. Assessment of the Calibrated Model 4.1. Global Accuracies The XZWD, VMF3-FC, and GPT3-derived ZWD were compared with RS ZWD at 492 sites in 2022 for validation. The results are presented in , and the values within square brackets are the maximum and minimum. The bias and RMSE of the GPT3 model are −0.56 and 3.99 cm. The VMF3-FC exhibits superior ZWD accuracy compared to the GPT3 model, with a bias of −0.41 cm and an RMSE of 1.75 cm.
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