Estimating sea surface swell height using a hybrid model combining CNN, ConvLSTM, and FCN based on spaceborne GNSS-R data from the CYGNSS mission
Author:
Funder
The Yunnan Fundamental Research Projects
The Innovative Training Plan Program for College Students of Yunnan Province
The National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10291-024-01671-1.pdf
Reference26 articles.
1. Alattabi ZR, Cahl D, Voulgaris G (2019) Swell and wind wave retrieval using a single very high frequency (VHF) radar. J Atmos Ocean Technol 36:987–1013. https://doi.org/10.1175/jtech-d-18-0166.1
2. Al-Attabi ZR, Voulgaris G, Conley DC (2021) Evaluation and validation of HF radar swell and wind-wave retrieval method. J Atmos Ocean Technol 38(10):1747–1775. https://doi.org/10.1175/JTECH-D-20-0186.1
3. Altiparmaki O, Kleinherenbrink M, Naeije M, Slobbe C, Visser P (2022) Sar altimetry data as a new source for swell monitoring. Geophys Res Lett 49(7):e2021GL096224. https://doi.org/10.1029/2021GL096224
4. Asgarimehr M, Arnold C, Weigel T, Ruf C, Wickert J (2022) GNSS reflectometry global ocean wind speed using deep learning: development and assessment of CyGNSSnet. Remote Sens Environ 269:112801. https://doi.org/10.1016/j.rse.2021.112801
5. Bu J, Yu K (2022a) A new integrated method of CYGNSS DDMA and LES measurements for significant wave height estimation. IEEE Geosci Remote Sens Lett 19:1505605. https://doi.org/10.1109/LGRS.2022.3198131
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