Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed (SSWS) from HH-polarized Sentinel-1 (S1) SAR images. The Polarization Ratio (PR) models combined with the CMOD5.N Geophysical Model Function (GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HH-polarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation (BP) neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error (RMSE) and scatter index (SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%, respectively, while compared to the ASCAT dataset the three parameters of training set are −0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization.

同时使用常规的检索和神经网络方法从HH极化的Sentinel-1（S1）SAR图像中检索海面风速（SSWS）。极化比（PR）模型与CMOD5.N地球物理模型函数（GMF）结合在一起用于从HH极化SAR数据中进行SSWS检索。我们比较了基于以前的HH极化C波段SAR数据开发的不同PR模型在S1 SAR数据中的应用。还验证了最近提出的CMODH，即直接从HH极化的S1数据中检索SSWS。结果表明，CMODH模型的性能优于使用PR模型的结果。我们提出了一种基于向后传播（BP）神经网络的神经网络方法，以从S1 HH极化数据中检索SSWS。与使用常规方法获得的结果相比，使用BP神经网络模型检索的SSWS与浮标测量结果和ASCAT数据集更好地吻合。与浮标测量相比，通过BP神经网络模型获取的风速偏差，均方根误差（RMSE）和散射指数（SI）分别为0.10 m / s，1.38 m / s和19.85％，而相比对于ASCAT数据集，训练集的三个参数分别为-0.01 m / s，1.33 m / s和15.10％。建议BP神经网络模型在从HH极化获取的Sentinel-1图像中检索SSWS方面具有潜在的应用。BP神经网络模型获取的风速均方根误差（RMSE）和散度指数（SI）分别为0.10 m / s，1.38 m / s和19.85％，而与ASCAT数据集相比，训练的三个参数设定分别为-0.01 m / s，1.33 m / s和15.10％。建议BP神经网络模型在从HH极化获取的Sentinel-1图像中检索SSWS方面具有潜在的应用。BP神经网络模型获取的风速均方根误差（RMSE）和散度指数（SI）分别为0.10 m / s，1.38 m / s和19.85％，而与ASCAT数据集相比，训练的三个参数设定分别为-0.01 m / s，1.33 m / s和15.10％。建议BP神经网络模型在从HH极化获取的Sentinel-1图像中检索SSWS方面具有潜在的应用。