Deep residual fully connected network for GNSS-R wind speed retrieval and its interpretation-Reference-Cited by-同舟云学术

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Deep residual fully connected network for GNSS-R wind speed retrieval and its interpretation

Published:2024-11 Issue: Volume:313 Page:114375
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Du Hao^ORCID,Li Weiqiang^ORCID,Cardellach Estel^ORCID,Ribó Serni^ORCID,Rius Antonio,Nan Yang^ORCID

Publisher

Elsevier BV

Reference62 articles.

1. TensorFlow: a system for large-scale machine learning;Abadi,2016

2. GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet;Asgarimehr;Remote Sens. Environ.,2022

3. A GNSS-R geophysical model function: Machine learning for wind speed retrievals;Asgarimehr;IEEE Geosci. Remote Sens. Lett.,2020

4. Accurate medium-range global weather forecasting with 3D neural networks;Bi;Nature,2023

5. GloWS-Net: A deep learning framework for retrieving global sea surface wind speed using spaceborne GNSS-R data;Bu;Remote Sens.,2023

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