@inproceedings{cef32318184a4d92b7002331e3f3890b,
title = "Exploring the Potential of Machine Learning-Assisted Spaceborne GNSS-R Measurements for Ocean Eddy Detection",
abstract = "This paper explores the potential of Global Navigation Satellite System Reflectometry (GNSS-R) technology for detecting ocean eddies through the retrieval of Sea Surface Height (SSH) data. The study utilizes a Specular Point (SP)-based Convolutional Neural Network (CNN) tailored for processing and analyzing spaceborne GNSS-R data to retrieve SSH. Additionally, it calculates the global Absolute Dynamic Topography (ADT) and determines the Root Mean Square Error (RMSE). By employing a gridded global map, the ADT achieves an RMSE of 0.46 meters for a day. Theoretically, with the Nyquist-Shannon sampling theorem, this demonstrates GNSS-R's potential for detecting ocean eddies with amplitudes exceeding approximately 1.0 meter. Results indicate that GNSS-R could effectively contribute to global oceanographic studies and provide valuable insights into ocean eddy detections.",
keywords = "GNSS-R, Machine learning, Ocean eddy, Remote sensing, Sea Surface Height",
author = "Jin Xing and Dongkai Yang and Zhibo Zhang and Yu Long and Pengyu Yang and Feng Wang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024 ; Conference date: 20-08-2024 Through 23-08-2024",
year = "2024",
doi = "10.1109/IBCAST61650.2024.10877211",
language = "英语",
series = "Proceedings of 2024 21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "169--172",
booktitle = "Proceedings of 2024 21st International Bhurban Conference on Applied Sciences and Technology, IBCAST 2024",
address = "美国",
}