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Hybrid CNN-LSTM Deep Learning for Track-Wise GNSS-R Ocean Wind Speed Retrieval

Arabi, Sima; Asgarimehr, Milad; Kada, Martin; Wickert, Jens

The NASA Cyclone GNSS (CYGNSS) mission provides one Delay Doppler Map (DDM) per second along observational tracks. To account for spatiotemporal correlations within adjacent DDMs in a track, a deep hybrid CNN-LSTM model is proposed for wind speed prediction. The model combines convolutional and pooling layers to extract features from DDMs in one track, which are then processed by LSTM as a sequence of data. This method leads to a test RMSE of 1.84 m/s. The track-wise processing approach outperforms the architectures that process the DMMs individually, namely based on Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), and a network based solely on fully connected (FC) layers, as well as the official retrieval algorithm of the CYGNSS mission with RMSEs of 1.92 m/s, 1.92 m/s, 1.93 m/s, and 1.90 m/s respectively. Expanding on the CNN-LSTM model, the CNN-LSTM+ model is proposed with additional FC layers parallel with convolutional and pooling layers to process ancillary data. It achieves a notable reduction in test RMSE to 1.49 m/s, demonstrating successful implementation. This highlights the significant potential of track-wise processing of GNSS-R data, capturing spatiotemporal correlations between DDMs along a track. The hybrid deep learning model processing the data sequentially in one track learns these dependencies effectively, improving the accuracy of wind speed predictions.
Published in: Remote Sensing, 10.3390/rs15174169, MDPI