GF-3交叉极化数据的海面风速反演研究

Research on Inversion of Sea Surface Wind Speed fromGF-3 Cross Polarization Data

单极化合成孔径雷达(SAR)图像在海面风场反演应用中具有复杂的业务化模型,运用SAR交叉极化数据反演海面风速成为当前研究热点。采用我国自主发射的C波段SAR卫星高分三号全极化SAR图像数据,以太平洋、大西洋等远洋海域为重点研究区域,分析交叉极化后向散射强度与海面风速、相对风向及雷达入射角的关系,建立多元线性回归模型和BP神经网络模型,并采用ECMWF风场再分析数据对模型结果进行验证。实验结果表明,建立的回归模型有效验证了交叉极化后向散射强度与风速、入射角呈线性相关,相较于同极化SAR反演海面风场,该模型不依赖于外部风向的输入,简化了风速反演模型。BP神经网络模型训练样本集拟合R值优于70%,且有效预测了交叉极化风速。

The inversion of the ocean surface wind field with single-polarized Synthetic Aperture Radar(SAR)data has a complex operational model.The use of cross polarization SAR images to invert the ocean surface wind has become a hot topic in research.Taking the waters of the Pacific and the Atlantic as the research objects,this paper uses the polarized SAR image data from the GF-3 satellite,which is the C-band SAR satellite launched independently by China,to analyze the relationship between cross polarization backscattering intensity and ocean surface wind speed,relative wind direction and radar incident angle.A multiple linear regression model and a BP neural network model are established,and their results are verified by using wind field reanalysis data of ECMWF.Experimental results show that the established regression model demonstrates the linear correlation between the cross polarization backscattering intensity and the wind speed and incident angle.Compared with the inversion of sea surface wind field based on co-polarization SAR,this inversion process does not depend on the input of the external wind direction,and simplifies the wind speed inversion model.The R value in the fit of training sample set of the BP neural network model exceeds 70%,which means the model effectively predicts the cross polarization wind speed.