GNSS Reflectometry (GNSS-R) is a novel remote sensing technique for the monitoring of geophysical parameters using reflected GNSS signals from the Earth's surface. Ocean wind speed monitoring is the main objective of the recently launched Cyclone GNSS (CyGNSS), a GNSS-R constellation of eight microsatellites, launched in late 2016. In this study, the capability of deep learning, especially, for an operational wind speed data derivation from the measured Delay-Doppler Maps (DDMs) is characterized. CyGNSSnet is based on convolutional layers for the feature extraction from bistatic radar cross section (BRCS) DDMs, along with fully connected layers for processing ancillary technical and higher-level input parameters. The best architecture is determined on a validation set and is evaluated over a completely blind dataset from a different time span than that of the training data to validate the generality of the model for operational usage. After a data quality control, CyGNSSnet results in an RMSE of 1.36 m/s leading to a significant improvement by 28% in comparison to the officially operational retrieval algorithm. The RMSE is the lowest among those seen in the literature for any conventional or machine learning-based algorithm. The benefits of the convolutional layers, the advantages and weaknesses of the model are discussed. CyGNSSnet offers efficient processing of GNSS-R measurements for high-quality global ocean winds.

GNSS 反射计 (GNSS-R) 是一种新型遥感技术，用于使用来自地球表面的反射 GNSS 信号监测地球物理参数。海洋风速监测是最近发射的 Cyclone GNSS (CyGNSS) 的主要目标，这是一个由八颗微型卫星组成的 GNSS-R 星座，于 2016 年底发射。在这项研究中，深度学习的能力，特别是对于操作风速数据从测量的延迟多普勒图 (DDM) 推导得到的特征。CyGNSSnet 基于用于从双基地雷达截面 (BRCS) DDM 中提取特征的卷积层，以及用于处理辅助技术和更高级别输入参数的全连接层。最佳架构是在验证集上确定的，并在与训练数据不同的时间跨度的完全盲数据集上进行评估，以验证模型在操作使用方面的通用性。经过数据质量控制后，CyGNSSnet 的 RMSE 为 1.36 m/s，与官方操作的检索算法相比，显着提高了 28%。对于任何传统或基于机器学习的算法，RMSE 是文献中最低的。讨论了卷积层的好处，模型的优点和缺点。CyGNSSnet 为高质量的全球海洋风提供 GNSS-R 测量的高效处理。CyGNSSnet 的 RMSE 为 1.36 m/s，与官方操作的检索算法相比，显着提高了 28%。对于任何传统或基于机器学习的算法，RMSE 是文献中最低的。讨论了卷积层的好处，模型的优点和缺点。CyGNSSnet 为高质量的全球海洋风提供 GNSS-R 测量的高效处理。CyGNSSnet 的 RMSE 为 1.36 m/s，与官方操作的检索算法相比，显着提高了 28%。对于任何传统或基于机器学习的算法，RMSE 是文献中最低的。讨论了卷积层的好处，模型的优点和缺点。CyGNSSnet 为高质量的全球海洋风提供 GNSS-R 测量的高效处理。