本文为荷兰代尔夫特大学（作者：M. (Martijn) D. Kwant）的硕士论文，共139页。

北海以其海洋活动变化而闻名，这些活动需要海洋数据来保证安全作业。海洋表面是用二维波谱来描述的。这种海洋波谱可以使用浮标、波浪模型或遥感技术进行估计。众所周知，波浪浮标和数值模型存在误差，这就强调了其他观测技术的重要性。本文的解决方案是利用Sentinel-1合成孔径雷达的遥感技术。

SAR在海洋上的成像机制主要是基于细微风浪的共振Bragg散射。长波将通过一系列过程调制返回的Bragg散射：倾斜调制（从不同的局部入射角）、水动力调制（由海浪的轨迹速度引起）和速度聚束（由波的相对运动引起，与卫星运动相比）。这就引出了本文的主要问题，即在多大程度上可以从Sentinel-1 SAR图像中测量出北海的低频涌浪。

从SAR图像中提取海浪数据的方法包括两个步骤：计算不同子目标之间的互谱和使用调制传递函数（MTF）估计海浪谱。本文首先对Sentinel-1 SAR图像进行互谱计算。Sentinel-1数据的预处理包括三个步骤。首先，通过将像素值转换为辐射的后向散射，对雷达图像进行标定。第二，卫星天线电子控制的天线方向图在一个称为deramping的过程中被去除。最后通过解调，将SAR图像的频谱峰值搬移到零多普勒频率附近。在SAR数据处理过程中，最重要的一步是对图像进行频域分割，生成多个时间分离的子视点。研究文献表明，计算子视点间的互谱可以解决波的方向问题。

在北海共分析了11个案例研究，在葡萄牙分析了3个案例研究。利用海浪浮标测量和海洋SAR模拟图像的互谱对Sentinel-1 SAR互谱进行了验证。总共有6个北海案例研究显示了一个积极的结果，其中一个涌浪峰是可见的，峰值与来自波浪浮标的光谱数据相匹配。来自葡萄牙和北海的SAR图像互谱显示了距离方向的延长，这是由矩形像素平滑造成的。葡萄牙海岸的案例研究展示了最好的结果，SAR互谱与浮标测量结果吻合得很好。

进一步的改进包括消除互谱中的非线性影响，在准线性涌浪谱上绘制波浪浮标测量值，并计算完整的MTF。本文研究表明，利用Sentinel-1合成孔径雷达测量北海低频波是可行的。SAR图像有助于更好地了解涌浪的运动，并为海洋活动提供更多信息。结合波浪浮标数据、Sentinel-1 SAR数据和OCEANSAR数据，可以使用这种最先进的遥感技术测量涌浪。

The North Sea is known for its marine activities, which need ocean data for safe operations. The ocean surface is described using the two-dimensional wave spectrum. This ocean wave spectrum can be estimated using wave buoys, wave models or remote sensing techniques. Wave buoys and numerical models are known to contain inaccuracies, which stresses the importance of other observation techniques. The solution of this thesis is using a remote sensing technique from Sentinel-1: Synthetic Aperture Radar.

The main imaging mechanism of SAR over the oceans is through resonant Bragg scattering with capillary wind-driven waves. Long ocean waves will modulate the returned Bragg scatter through a number of processes: tilt modulation (from a different local incidence angle), hydrodynamic modulation (resulting from the orbital velocities of ocean waves) and velocity bunching (caused by the relative motion of waves, compared to the motion of the satellite). This brings us to the main problem of this thesis, to what extent we can measure low-frequency swell waves from Sentinel-1 SAR images in the North Sea.

The method to derive wave data from SAR images consists of two steps: calculating the cross-spectrum between different sub-looks and estimating the ocean wave spectrum using a Modulation Transfer Function (MTF). In this thesis the first step, calculating the cross-spectrum was performed on Sentinel-1 SAR images. Pre-processing of the Sentinel-1 data consisted of three steps. First, the radar images were calibrated by converting pixel values to radiometric backscatter. Second, the antenna pattern from electronic steering of the satellite antenna was removed in a process called deramping. At last the SAR image were centered around the zero Doppler frequency by shifting the peak of the spectrum through demodulation. The most important step during processing of the SAR data was splitting the image in Frequency domain to create multiple time-separated sub-looks. Literature showed that calculating the cross-spectrum between sub-looks makes it possible to resolve the direction of the waves.

In total 11 case studies were analyzed in the North sea and 3 case studies in Portugal. Sentinel-1 SAR cross-spectra were verified and validated using wave buoy measurements and cross-spectra from simulated SAR images from OCEANSAR. In total, 6 North Sea case studies showed a positive result, where a swell peak was visible and the peak matched spectral data from wave buoys. SAR image cross spectra from Portugal, but also from the North Sea showed an elongation in range direction. This was caused by smoothing of the rectangular shaped pixels. Case studies from the Portuguese coast showed the best results, where SAR cross-spectra agreed well with buoy measurements.

Further improvements include the removal of non-linear contribution from cross-spectra, mapping of wave buoy measurements on the quasi-linear swell spectrum and calculating the full MTF. This thesis showed it is possible to measure low-frequency waves in the North Sea using Sentinel-1 Synthetic Aperture Radar. SAR images lead to a better understanding of the movements of swells and provide additional information for marine activities. A combination of wave buoy data, Sentinel-1 SAR data and OCEANSAR data showed measuring swell waves is possible with this state-of-the-art remote sensing technique.

1.       引言

2. 雷达

3. 材料与方法

4. 结果

5. 结论

6. 建议

附录A 辅助信息与方法

附录B 案例研究

附录C 水动力条件

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