Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network

Author:

Li Xiaohui,Yang Dongkai,Yang Jingsong,Zheng Gang,Han Guoqi,Nan Yang,Li Weiqiang

Funder

Shandong Province Department of Science and Technology

Government of Spain Ministry of Science and Innovation

State Key Laboratory of Satellite Ocean Environment Dynamics

Chinese Academy of Sciences

Zhejiang Province Natural Science Foundation

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference65 articles.

1. An algorithm for detecting coherence in cyclone global navigation satellite system mission level-1 delay-Doppler maps;Al-Khaldi;IEEE Trans. Geosci. Remote Sens. Early Access,2020

2. Dual-polarization GNSS-R interference pattern technique for soil moisture mapping;Alonso Arroyo;IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens.,2014

3. Sea ice detection using U.K. TDS-1 GNSS-R data;Alonso-Arroyo;IEEE Trans. Geosci. Remote Sens.,2017

4. Hurricane tracking using Multi-GNSS-R and deep learning;Alshaye,2020

5. A GNSS-R geophysical model function: machine learning for wind speed retrievals;Asgarimehr;IEEE Geosci. Remote Sens. Lett.,2019

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