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A GNSS-R Geophysical Model Function: Machine Learning for Wind Speed Retrievals

  • Published:2020-08 Issue:8 Volume:17 Page:1333-1337
  • ISSN:1545-598X
  • Container-title:IEEE Geoscience and Remote Sensing Letters
  • language:
  • Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Asgarimehr MiladORCID,Zhelavskaya IrinaORCID,Foti GiuseppeORCID,Reich SebastianORCID,Wickert JensORCID

Funder

Research Network for Geosciences in Berlin and Potsdam

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Cited by 37 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep residual fully connected network for GNSS-R wind speed retrieval and its interpretation;Remote Sensing of Environment;2024-11

2. Evaluating Feature Impact on Ocean Wind Speed Predictions: An Application of Explainable AI to GNSS Reflectometry Data;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

3. Challenges and Future Applications for IRNSS with Machine Learning Algorithms;2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI);2024-03-14

4. A New Vegetation Observable Derived from Spaceborne GNSS-R and Its Application to Vegetation Water Content Retrieval;Remote Sensing;2024-03-06

5. Modeling the Influence of Precipitation on L-Band SMAP Observations of Ocean Surfaces Through Machine Learning Approach;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
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