GNSS-R Global Sea Surface Wind Speed Retrieval Based on Deep Learning

Global navigation satellite system reflectometry (GNSS-R) is a burgeoning remote sensing observation technology that can retrieve global sea surface wind speeds using satellite signals reflected from the sea surface. The improvement of data quality and the accumulation of data volume of this technology provides data support for constructing interdisciplinary-based retrieval models. This article constructs a hybrid deep neural network model based on deep learning for wind speed retrieval, which can receive and perform feature mining on the entire delay waveform while simultaneously supporting multiple auxiliary features input and achieving joint fitting. Then a bias correction method based on cumulative distribution function (CDF) matching is introduced to mitigate bias, especially at high wind speeds. We verify the contribution of different attribute features in wind speed retrieval by designing a feature ablation analysis. The fluctuation variation of the retrieval accuracy in the time dimension and the retrieval results distribution in space are compared and analyzed. The root mean square error (RMSE) of retrieval results is 1.486 m/s and can reach 1.399 m/s under the 94.25% wind speed condition. After bias correction based on CDF matching, the retrieval accuracy at high wind speed is improved by 7.19%. Besides, this model also has good temporal stability and can reproduce large-scale wind fields while effectively mitigating retrieval bias on a global scale, showing great potential for operational applications.

Automated summary

This article introduces a hybrid deep neural network model based on deep learning for wind speed retrieval using global navigation satellite system reflectometry (GNSS-R) technology. A bias correction method based on cumulative distribution function (CDF) matching is also introduced. The verification shows that the retrieval accuracy of the model is improved at high wind speeds and demonstrates the ability to effectively mitigate retrieval bias on a global scale.