星载散射计海浪参数提取方法研究
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摘要
海浪是重要的海洋动力环境参数之一,迄今为止,合成孔径雷达是唯一测量海浪谱的传感器,然而,由于它存在方位波数的截止和能量短缺,限制了其应用。卫星高度计可以测量全球有效波高,但其二维空间分辨率低,其应用也受到了限制。因此,发展一个能够全天候全天时测量海浪参数的有效方法迫在眉睫。
本论文希望利用散射计提取波浪参数,主要研究工作如下:
1、利用二尺度理论模拟了涌浪对C-波段后向散射系数的影响,并与前人在L-波段和Ku-波段模拟的结果进行了比较,验证了涌浪对雷达后向散射截面的影响随雷达频率增加而减小,同样也随入射角增大而减小。
2、根据浮标和散射计匹配的数据发现,当海浪参数(有效波高和波陡)和雷达入射角给定后,雷达后向散射截面同方位角的关系显示二阶余弦函数关系,为此建立了一种BP神经网络波浪参数反演模式。
3、分别发展了ERS卫星和QuikSCAT卫星散射计资料反演海浪参数的方法,利用NDBC浮标数据分别对它们反演的波浪参数进行了验证。并发现风浪占优势时反演有效波高的结果好于涌浪占优势;C-波段反演有效波高的结果要好于Ku波段,且在Ku波段,水平极化好于垂直极化;尤其波陡反演的效果好于有效波高的反演结果,且不受海况影响。
Ocean wave is one of the most important Ocean dynamic parameters. Up to now, the synthetic aperture radar is the only sensor to be used to measure ocean wave spectrum. However, because of the azimuthal wave cutoff and lack of power, it can not get the global high resolution ocean wave spectrum and its application is limited. The significant wave height (H1/3) can be measured globally by satellite radar altimeter. But, as its probe band of airspace is small and the two dimensional spatial resolution is too low, its applications are also limited. Therefore, it is imminent to develop an all-weather day measurement of wave parameters.
In this dissertation, a method to retrieve the wave parameters from the scatterometer data will be developed. The main works are as follows, The effect of swell on radar backscattering cross sections (RCS) of the sea surface on C-band is simulated using two-scale scattering model. It with previous Stephen’s works is shown that the effect of swell on the RCS decreases when radar frequency increases or radar incidence angle increases.
It is shown that the relationship between the azimuth angle and the radar backscatter cross-section is a significant second-order cosine function when ocean wave parameters (significant wave height and wave steepness) and radar incidence angle are given according to buoy and scatterometer data. So we develop a back-propagation (BP) neural network algorithm to retrieve the ocean wave parameters from the scatterometer data.
An algorithm to retrieve the ocean wave parameters from the ERS-1/2 and QuikSCAT scatterometer data is developed, respectively. A validation is given using NDBC Buoy data and it shows that the ocean wave parameters retrieved from scatterometer data for primary wind wave are better than those for primary swell, those for C band are better than those for Ku band and those for HH polarization are better than those for VV polarization on Ku band. Especially, wave steepness retrieved from scatterometer data is better than significant wave height and it is independent of the sea state.
本论文希望利用散射计提取波浪参数,主要研究工作如下:
1、利用二尺度理论模拟了涌浪对C-波段后向散射系数的影响,并与前人在L-波段和Ku-波段模拟的结果进行了比较,验证了涌浪对雷达后向散射截面的影响随雷达频率增加而减小,同样也随入射角增大而减小。
2、根据浮标和散射计匹配的数据发现,当海浪参数(有效波高和波陡)和雷达入射角给定后,雷达后向散射截面同方位角的关系显示二阶余弦函数关系,为此建立了一种BP神经网络波浪参数反演模式。
3、分别发展了ERS卫星和QuikSCAT卫星散射计资料反演海浪参数的方法,利用NDBC浮标数据分别对它们反演的波浪参数进行了验证。并发现风浪占优势时反演有效波高的结果好于涌浪占优势;C-波段反演有效波高的结果要好于Ku波段,且在Ku波段,水平极化好于垂直极化;尤其波陡反演的效果好于有效波高的反演结果,且不受海况影响。
Ocean wave is one of the most important Ocean dynamic parameters. Up to now, the synthetic aperture radar is the only sensor to be used to measure ocean wave spectrum. However, because of the azimuthal wave cutoff and lack of power, it can not get the global high resolution ocean wave spectrum and its application is limited. The significant wave height (H1/3) can be measured globally by satellite radar altimeter. But, as its probe band of airspace is small and the two dimensional spatial resolution is too low, its applications are also limited. Therefore, it is imminent to develop an all-weather day measurement of wave parameters.
In this dissertation, a method to retrieve the wave parameters from the scatterometer data will be developed. The main works are as follows, The effect of swell on radar backscattering cross sections (RCS) of the sea surface on C-band is simulated using two-scale scattering model. It with previous Stephen’s works is shown that the effect of swell on the RCS decreases when radar frequency increases or radar incidence angle increases.
It is shown that the relationship between the azimuth angle and the radar backscatter cross-section is a significant second-order cosine function when ocean wave parameters (significant wave height and wave steepness) and radar incidence angle are given according to buoy and scatterometer data. So we develop a back-propagation (BP) neural network algorithm to retrieve the ocean wave parameters from the scatterometer data.
An algorithm to retrieve the ocean wave parameters from the ERS-1/2 and QuikSCAT scatterometer data is developed, respectively. A validation is given using NDBC Buoy data and it shows that the ocean wave parameters retrieved from scatterometer data for primary wind wave are better than those for primary swell, those for C band are better than those for Ku band and those for HH polarization are better than those for VV polarization on Ku band. Especially, wave steepness retrieved from scatterometer data is better than significant wave height and it is independent of the sea state.
引文
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