基于神经网络方法的C波段和Ku波段统一地球物理模型

A unified C-band and Ku-band GMF determined by using neural network approach

通过地球物理模型建立后向散射系数与海面风矢量的关系,可将散射计从不同方位角测得的风矢量单元后向散射系数反演得到风矢量,因此地球物理模型在风速反演中起着至关重要的作用。使用神经网络方法,利用C波段经验模型CMOD4和Ku波段经验模型QSCAT—1仿真数据建立了形式统一的C波段和Ku波段地球物理模型。新模型将电磁波频率作为模型的参数之一,使新模型不再局限于特定的传感器,并使C波段与Ku波段具有统一的形式。分析表明,由新模型建立的后向散射系数与海面风矢量的关系同经验模型具有很好的可比性。利用新模型反演的风速与CMOD4和QSCAT—1模型反演的风速具有很好的一致性,说明新模型在具有统一简洁形式的同时也兼有与经验统计模型相同的有效性。

The geophysical model function （GMF） describes the relationship between backscattering and sea surface wind, so that wind vectors can be retrieved from backscattering measurement. The GMF plays an important role in ocean wind vector retrievals, its performance will directly influence the accuracy of the retrieved wind vector. Neural network （NN） approach is used to develop a unified GMF for C-band and Kuband （NN-GMF）. Empirical GMF CMOD4 and QSCAT-1 are used to generate the simulated training data-set, and Gaussian noise at a signal noise ratio of 30 dB is added to the data-set to simulate the noise in the backscattering measurement. The NN--GMF employs radio frequency as an additional parameter, so it can be applied for both C-band and Ku band. Analysis shows that the normalized backscattering coefficient predicted by the NN-GMF is comparable with the normalized backscattering coefficient predicted by CMOD4 and QSCAT-1. Also the wind vectors retrieved from the NN-GMF and empirical GMF CMOD4 and Qscat-1 are comparable, indicating that the NN-GMF is as effective as the empirical GMF, and has the advantages of the universal form.