Modeling of Sea State Conditions for Improvement of Cygnss L2 Wind Speed Retrievals-Reference-Cited by-同舟云学术

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Modeling of Sea State Conditions for Improvement of Cygnss L2 Wind Speed Retrievals

Published:2018-07 Issue: Volume: Page:
ISSN:
Container-title:IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Wang Tianlin,Zavorotny Valery U.,Johnson Joel,Ruf Christopher,Yi Yuchan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8496405/8517275/08518686.pdf?arnumber=8518686

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Combining ERA5 data and CYGNSS observations for the joint retrieval of global significant wave height of ocean swell and wind wave: a deep convolutional neural network approach;Journal of Geodesy;2023-08

2. Uncertainty assessment and an improved CYGNSS cyclonic wind speed retrieval model for cyclones over North Indian Ocean;Journal of Earth System Science;2023-01-21

3. Level-1 Calibration Assessment of Spire’s LEMUR-2 GNSS-R Ocean Normalized Bistatic Radar Cross Section Estimates;IEEE Geoscience and Remote Sensing Letters;2023

4. GNSS-R Global Sea Surface Wind Speed Retrieval Based on Deep Learning;IEEE Transactions on Geoscience and Remote Sensing;2023

5. Information fusion for GNSS-R wind speed retrieval using statistically modified convolutional neural network;Remote Sensing of Environment;2022-04

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