ERS-2 SAR反演海洋风矢量的研究

SAR(Synthetic Aperture Radar)反演海洋风矢量是当今微波遥感领域非常有意义的前沿课题.本文首先介绍了星载SAR估算海面风向、风速的基本原理和三种主流反演算法,接着给出反演的流程图以及重要步骤.然后,以2002年5月7日香港地区ERS-2 SAR海洋图像为例,对经典的SWDA(SAR Wind Direction Algorithm)-谱分析方法加以改进,求得具有180°模糊度的风向,并用香港天文台气象浮标实测数据消除了风向不确定性.最后,利用CMOD4GMF(Geophysical Model Function,地球物理模式函数)计算得到海面上10m高的风速.与气象浮标站实测资料相比,利用ERS-2 SAR图像获取的海面风向、风速的精度均较高.这一结果表明:如果对SAR预先进行ADC(Analog toDigital Converter)改正以及精确校准,结合改进的SWDA和CMOD4,可以获得高精度的风矢量.

基于遗传神经网络算法的海面风速反演新方法

针对海面运动的复杂性、海面电磁散射理论模型的局限性以及利用SAR图像反演海面风速存在的非线性现象,基于遗传神经网络的方法,以业务化的CMOD4模式函数数据为基础,采用Fletcher-Reeves算法的变梯度反向传播算法,建立一种SAR风速反演的新模型。试验结果表明,利用遗传神经网络方法反演海面风速是可行的,当随机误差小于10%时,模型的抗噪能力较强,风速反演的精度较为理想。比较不同风速下的反演结果可以发现,在中、小风速的情况下,模型的抗噪能力较强,模型学习拟合和预测检验的精度相对较高;在大风速的情况下,模型的反演能力有待于进一步提高。

基于神经网络方法的C波段和Ku波段统一地球物理模型

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。

遥感资料反演海面风场的新方法研究

本文对应用SAR(Synthetic Aperture Radar)图像反演海面风场方法进行了阐述,并对三种反演模式进行仿真总结。首先介绍SAR图像风场反演的的物理机制,接着介绍风场反演的地球物理模式函数(Geophysical Model Function,GMF),并对CMOD4、CMOD-IFR2和CMOD5三种模式进行仿真,得到雷达后向散射系数和风速、风向、入射角、极化方式的关系。特别对两个较新的极化比模式进行了介绍和分析。此外对当前应用美国CALIPSO激光雷达卫星资料验证海面风场的方法进行了阐述和探索。

A unified C-band and Ku-band geophysical model function determined by neural network approach

The geophysical model function （GMF） describes the relationship between backscattering and sea surface wind, so that wind vec- tors 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 Ku-band （NN-GMF）. Empirical GMF CMOIM 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 backscat- tering measurement. The NN-GMF employs radio frequency as an additional parameter, so it can be applied for both C-band and Ku-band. Analyses show that the %predicted by the NN-GMF is comparable with the σpredicted by CMOIM and QSCAT-1. Also the wind vectors retrieved from the NN-GMF and empirical GMF CMOIM 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.

A Method for Sea Surface Wind Field Retrieval from SAR Image Mode Data

To retrieve wind field from SAR images, the development for surface wind field retrieval from SAR images based on the improvement of new inversion model is present. Geophysical Model Functions(GMFs) have been widely applied for wind field retrieval from SAR images. Among them CMOD4 has a good performance under low and moderate wind conditions. Although CMOD5 is developed recently with a more fundamental basis, it has ambiguity of wind speed and a shape gradient of normalized radar cross section under low wind speed condition. This study proposes a method of wind field retrieval from SAR image by combining CMOD5 and CMOD4 Five VV-polarisation RADARSAT2 SAR images are implemented for validation and the retrieval results by a combination method(CMOD5 and CMOD4) together with CMOD4 GMF are compared with QuikSCAT wind data. The root-mean-square error(RMSE) of wind speed is 0.75 m s-1 with correlation coefficient 0.84 using the combination method and the RMSE of wind speed is 1.01 m s-1 with correlation coefficient 0.72 using CMOD4 GMF alone for those cases. The proposed method can be applied to SAR image for avoiding the internal defect in CMOD5 under low wind speed condition.

Deriving Changjiang coastal zone wind from C-band SAR and its application to salinity simulation

Wind plays an important role in hydrodynamic processes such as the expansion of Changjiang(Yangtze) River Diluted Water(CDW), and shelf circulation in the Changjiang estuary. Thus, it is essential to include wind in the numerical simulation of these phenomena. Synthetic aperture radar(SAR) with high resolution and wide spatial coverage is valuable for measuring spatially inhomogeneous ocean surface wind fields. We have collected 87 ERS-2 SAR images with wind-induced streaks that cover the Changjiang coastal area, to verify and improve the validity of wind direction retrieval using the 2D fast Fourier transform method. We then used these wind directions as inputs to derive SAR wind speeds using the C-band model. To demonstrate the applicability of the algorithms, we validated the SAR-retrieved wind fields using QuikSCAT measurements and the atmospheric Weather Research Forecasting model. In general, we found good agreement between the datasets, indicating the reliability and applicability of SARretrieved algorithms under different atmospheric conditions. We investigated the main error sources of this process, and conducted sensitivity analyses to estimate the wind speed errors caused by the effect of speckle, uncertainties in wind direction, and inaccuracies in the normalized radar cross section. Finally, we used the SAR-retrieved wind fields to simulate the salinity distribution off the Changjiang estuary. The findings of this study will be valuable for wind resource assessment and the development of future numerical ocean models based on SAR images.

基于高斯曲线拟合-FFT方法的南海风场反演

海面风场是海洋学的基本参量,获取海面风场对了解海洋的物理过程以及海洋与大气之间的相互作用至关重要。宽阔的海域面积及复杂的海面状况通常使南海海面上的风场信息很难被及时获取。ENVISAT ASAR是一种全天候全天时监测海面的微波雷达传感器,可实时获取海面风场数据。本文基于已有ASAR数据对南海海面风场进行反演实验,首先将结合高斯曲线拟合的FFT风向反演方法应用于南海风向反演,并参考Cross-Calibrated Multi-Platform(CCMP)风场数据去除180o方向模糊获得海面风向。然后,将高斯曲线拟合-FFT风向与传统的峰值-FFT风向进行对比,最后将准确率较高的高斯曲线拟合-FFT风向分别输入CMOD4模型和CMOD5模型获得海面风速大小。实验结果与CCMP参考数据的比较结果表明,在风条纹不明显的情况下,利用结合高斯曲线的FFT风向反演方法和CMOD4模型风速反演方法可有效地进行南海海面风场反演。该成果对利用SAR数据实时获取南海大面积海面风场信息,尤其是观测点缺乏海域的风场信息,具有重要的指导意义。