Improving GNSS-R Sea Surface Wind Speed Retrieval from FY-3E Satellite Using Multi-task Learning and Physical Information
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Publisher
Springer Nature Singapore
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https://link.springer.com/content/pdf/10.1007/978-981-99-8076-5_26
Reference21 articles.
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3. Chu, X., et al.: Multimodal deep learning for heterogeneous GNSS-R data fusion and ocean wind speed retrieval. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 13, 5971–5981 (2020). https://doi.org/10.1109/JSTARS.2020.3010879
4. Egido, A., Delas, M., Garcia, M., Caparrini, M.: Non-space applications of GNSS-R: from research to operational services. examples of water and land monitoring systems. In: IEEE International Geoscience & Remote Sensing Symposium, IGARSS 2009, 12–17 July 2009, University of Cape Town, Cape Town, South Africa, Proceedings, pp. 170–173. IEEE (2009). https://doi.org/10.1109/IGARSS.2009.5418033
5. Foti, G., et al.: Spaceborne GNSS reflectometry for ocean winds: first results from the UK TechDemosat-1 mission. Geophys. Res. Lett. 42(13), 5435–5441 (2015)
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