GloWS-Net: A Deep Learning Framework for Retrieving Global Sea Surface Wind Speed Using Spaceborne GNSS-R Data

Abstract

Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) is a new remote sensing technology that uses GNSS signals reflected from the Earth’s surface to estimate geophysical parameters. Because of its unique advantages such as high temporal and spatial resolutions, low observation cost, wide coverage and all-weather operation, it has been widely used in land and ocean remote sensing fields. Ocean wind monitoring is the main objective of the recently launched Cyclone GNSS (CYGNSS). In previous studies, wind speed was usually retrieved using features extracted from delay-Doppler maps (DDMs) and empirical geophysical model functions (GMFs). However, it is a challenge to employ the GMF method if using multiple sea state parameters as model input. Therefore, in this article, we propose an improved deep learning network framework to retrieve global sea surface wind speed using spaceborne GNSS-R data, named GloWS-Net. GloWS-Net considers the fusion of auxiliary information including ocean swell significant wave height (SWH), sea surface rainfall and wave direction to build an end-to-end wind speed retrieval model. In order to verify the improvement of the proposed model, ERA5 and Cross-Calibrated Multi-Platform (CCMP) wind data were used as reference for extensive testing to evaluate the wind speed retrieval performance of the GloWS-Net model and previous models (i.e., GMF, fully connected network (FCN) and convolutional neural network (CNN)). The results show that, when using ERA5 winds as ground truth, the root mean square error (RMSE) of the proposed GloWS-Net model is 23.98% better than that of the MVE method. Although the GloWS-Net model and the FCN model have similar RMSE (1.92 m/s), the mean absolute percentage error (MAPE) of the former is improved by 16.56%; when using CCMP winds as ground truth, the RMSE of the proposed GloWS-Net model is 2.16 m/s, which is 20.27% better than the MVE method. Compared with the FCN model, the MAPE is improved by 17.75%. Meanwhile, the GloWS-Net outperforms the FCN, traditional CNN, modified CNN (MCNN) and CyGNSSnet models in global wind speed retrieval especially at high wind speeds.

Joint Retrieval of Sea Surface Rainfall Intensity, Wind Speed, and Wave Height Based on Spaceborne GNSS-R: A Case Study of the Oceans near China

“…In neural network learning, the more parameters a model has, the stronger its expressive power is In addition to determining the rainfall intensity on the sea surface, this study also retrieves the sea surface wind speed. Therefore, we propose a deep convolutional neural network model incorporating an attention mechanism (AM-DCNN) for wind speed retrieval, which is an improved version of the GloWS-Net model proposed by Bu et al [31], that is, the attention mechanism is introduced into the GloWS-Net framework. In neural network learning, the more parameters a model has, the stronger its expressive power is and the greater the amount of information stored in the model, which can lead to the issues of information overload.…”

Flying Target Detection Technology Based on GNSS Multipath Signals

“…Furthermore, in recent years, national navigation satellite systems have been developed rapidly, the number of global navigation satellites has become more abundant, and remote sensing technology using GNSS signals has become increasingly advanced. At present, this technology has realized engineering applications in the fields of sea surface altitude measurement [ 8 , 9 ], effective wave height measurement at sea level [ 10 , 11 ], the remote sensing of wind fields at sea level [ 12 , 13 , 14 , 15 ], the remote sensing of seawater salinity [ 16 , 17 , 18 ], and tidal detection [ 19 , 20 , 21 ]. In land surface remote sensing, numerous breakthroughs have also been made for measuring quantities such as soil moisture [ 22 , 23 , 24 ], snow thickness [ 25 ], and vegetation cover [ 26 ].…”

Wind Profile Retrieval Based on LSTM Algorithm and Mobile Observation of Brightness Temperature over the Tibetan Plateau

“…With the rapid development of artificial intelligence technologies, the direct retrieval of wind field information from MR observational data is feasible. Bu et al [27] employed an enhanced deep learning network to inverse global sea surface wind speed (WS) from GNSS-R data, although the continuity and spatial correlation of the wind field were not taken into account. Shi et al [28] and Ouyed et al [29] considering the characteristics of the wind field, established a field-to-field sea surface wind field inversion model based on deep learning.…”