Application of Neural Network to GNSS-R Wind Speed Retrieval

This paper applies a machine learning (ML) algorithm based on the multi-hidden layer neural network (MHL-NN) for ocean surface wind speed estimation using global navigation satellite system (GNSS) reflection measurements. Unlike conventional wind speed retrieval methods that often depend on limited scalar delay-Doppler map (DDM) observables, the proposed MHL-NN makes use of information captured by the entire DDM. Both simulated and real data sets are used to train and evaluate the performance of the MHL-NN and compare it to a conventional wind speed retrieval method and other prevailing ML algorithms. The results show that the MHL-NN algorithm outperforms the other methods in terms of the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the wind speed estimation.

• Publication year

  2019

• Venue

  IEEE Transactions on Geoscience and Remote Sensing

• Publication date

  2019-08-15

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/TGRS.2019.2929002
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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