基于SAR海浪方向谱的中国海海浪特性研究

基于Hasselmanns提出的SAR反演海浪方向谱的MPI算法,对2003-2012年间的中国海Envisat ASAR波模式数据进行了海浪方向谱反演。统计由反演的海浪方向谱得到的海浪有效波高数据,依据海浪浪级的划分,分析了中国海海浪浪级的分布特点,获得一些有参考价值的结果:(1)中国海3-4级海况的年出现概率最高,达85%;(2)中国海累月不同浪级的海浪出现概率分布符合高斯分布函数:f(x)=a*exp(-((x-1-b)/c)^2);(3)分析上述高斯分布函数的拟合系数a、b、c,发现其分布也有一定规律性。同时应用反演得到的海浪有效波高、平均波周期、平均波向等数据,分析了中国海的海浪时空分布特性,得到一些可供参考的结果。     

基于准线性近似方法的SAR图像海浪参数反演研究

本文根据相干斑噪声的时间快变特征和非海浪纹理现象的时间缓变特征，基于交叉谱提出了一种对相干斑噪声和大尺度非海浪纹理的抑制的方法，进而结合SAR图像谱和海浪谱之间的准线性映射关系，基于SAR数据对海浪参数进行了反演。在反演过程中，首先仿真分析了不同海况下准线性近似法的海浪反演能力，结果表明：风浪引起的方位向截断效应会显著影响反演精度，因此该方法在低风速时的涌浪反演精度更高。通过将基于Sentinel-1卫星2020年的波模式SAR数据的反演结果与欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECMWF)提供的再分析数据进行对比，发现高海况海浪有效波高反演结果明显偏低，而且该反演误差与风速、方位向截断波长之间存在显著相关性。为了提高有效波高的反演精度，本文进一步给出了海浪有效波高反演误差与风速、方位向截断波长之间的经验校正函数模型，结果显示，通过该模型修正后的海浪有效波高反演结果与ECMWF数据和浮标测量数据具有良好一致性。     

ASAR波模式数据反演参数误差与海浪条纹清晰度的相关性分析

欧洲环境卫星-高级合成孔径雷达(EnvironmentalSatellite-AdvancedSyntheticAperture Radar,Envisat-ASAR)波模式数据提供了全球风、浪要素信息,在海浪模式预报与同化方面有重要作用。该数据合成孔径雷达(SyntheticApertureRadar,SAR)图像普遍存在海浪条纹清晰度不同的现象,但是否影响数据精度尚无定论。本文通过比较2010年NODC (the National Oceanographic Date Center)浮标观测数据和波模式数据,发现经过官方修正后的海浪参数反而具有更大误差。进而通过对比不同条纹清晰度的SAR图像反演参数误差,揭示了ASAR产品海浪参数与浮标测量值之间的误差与海浪条纹清晰度的关系。结果表明:海浪条纹清晰的SAR图像的主波波长和主波周期的反演误差更小,而条纹不清晰SAR图像的有效波高和风速的反演误差更小。通过分析海浪参数对海浪条纹清晰度的敏感性,证实了有效波高和方位向截断波长对SAR图像条纹清晰度的响应最好,波陡次之,与卫星飞行方位角和入射角无关。因此,在反演和修正SAR波模式数据时,考虑图像的条纹清晰度,将会有效提高反演数据的精度。该研究可为高分三号等卫星的波模式数据波浪要素反演精度的提升提供有价值的参考。     

X-波段雷达近海海浪频谱反演的神经网络模型

X-波段雷达作为国内海浪观测的一种新工具,在海浪频谱获取和有效波高反演方面仍存在较多问题.本文利用非线性回归方法,将现场实测浮标数据频谱和雷达一维图像谱分别与标准频谱模型进行拟合,发现浮标频谱和一维图像谱具有标准频谱的特征,能够较准确地获取相应的谱参数.提出了建立由雷达一维图像谱参数反演海浪频谱参数的神经网络模型,同时在模型中加入影像序列信噪比,进而反演有效波高,并将反演结果与现场实测数据和传统算法(建立影像序列信噪比与有效波高之间的线性回归方程)进行了对比,结果表明,获取谱参数的误差和反演有效波高的平均误差在20％以内,而传统算法计算有效波高平均误差在20％以上.     

基于同步风场的机载SAR海浪参数反演方法

针对机载合成孔径雷达(SAR)对海探测特点,采用多入射角法从SAR数据本身得到与海浪参数反演区域时空匹配的同步海面风速和风向,并结合线性变换关系,计算得到海浪初猜谱对应的仿真SAR图像谱,将仿真SAR图像谱和观测SAR图像谱输入代价函数中进行迭代运算,通过非线性方程的解算得到最适海浪谱;采用交叉谱法去除海浪传播180°方向模糊,最终得到海浪参数。论文提出的基于同步风场的机载SAR海浪参数反演方法,充分利用了机载SAR海洋环境探测的优势,解决了传统SAR海浪参数反演中初猜谱构造依赖外部风场的问题,机载同步飞行试验的海浪参数反演结果与浮标观测值的有效波高、波向的均方根误差分别为0.23 m和13.23°,验证了该方法的有效性,可为机载SAR海浪参数反演业务化提供支持。     

对ENVISAT ASAR level2算法固有误差的分析

欧洲空间局的ENVISAT ASAR level 2算法是从合成孔径雷达(SAR)单视复图像反演涌浪方向谱的算法.该算法假设双峰海浪谱的SAR图像交叉谱是涌浪的图像交叉谱和风浪的图像交叉谱之和.实际上双峰海浪谱的SAR图像交叉谱中还有一个混合项,正是该混合项导致ENVI-SAT ASAR level 2算法有固有误差.利用遥感仿真的方法分析了不同海况条件下该算法的这一固有误差,结果表明,只有在有效波高较小、或风浪的成分较少、或双峰海浪的传播方向较靠近SAR距离向、或波长较长时固有误差才较小,ENVISAT ASAR level 2算法对海浪谱的反演才较为适用.     

机载波谱仪海浪谱反演方法及其验证

为了验证波谱仪反演二维海浪谱的功能,根据海浪波谱仪的信号形成机制,总结了机载波谱仪反演海浪的流程。利用机载波谱仪回波数据,通过自相关和互相关两种功率谱估计方法,反演了二维海浪谱。最后通过与浮标测量的二维海浪谱进行对比,验证了该机载波谱仪探测二维海浪谱的有效性。结果表明,无论采用自相关函数还是互相关函数进行功率谱估计,得到的主波波长和有效波高与实际二维海浪谱基本一致。互相关函数法得到的交叉谱能去除180°模糊现象,其在计算有效波高时相对于自相关函数会稍微偏小。在计算斜率方差时可以采用5°~12°入射角范围的后向散射系数进行公式拟合,因此定标与否并不影响最后的二维海浪谱结果,未来星载波谱仪只有靠多波束联合才能实现。     

波谱仪调制功率谱估计算法性能的仿真研究

基于星载波谱仪海浪方向谱探测原理,仿真了不同海况、风速下的海浪波谱仪接收信号,并采用周期图法、Welch法、AR模型法以及最小方差法共4种不同的调制谱估计方法反演出海浪谱,比较各种调制谱估计方法的海浪方向谱反演性能。仿真结果表明:对于一定方位向下的一维海浪谱反演,不同调制谱估计方法反演海浪谱性能优劣没有绝对的顺序。对于二维海浪谱反演,在成长中海浪条件下周期图法反演性能最差,其他3种方法的反演性能没有绝对优劣顺序;对于成熟风浪,最小方差法在积分能量误差、有效波高误差两个指标上的反演性能最好,在主波波向、主波波长误差上,周期图法反演性能最差,其他3种方法没有绝对优劣顺序。在涌浪条件下,AR模型法反演性能优于其他3种方法。在不同海况下,随着波谱仪中心入射角的下降,反演性能会下降。基于这些仿真结果,本文推荐最小方差法作为充分成长海浪的海浪方向谱反演的调制谱估计方法,AR模型法作为涌浪海浪方向谱反演的调制谱估计方法。     

基于SAR图像速度聚束调制的海浪反演研究

本文首先对合成孔径雷达（SAR）海浪成像中的3种调制（倾斜调制、流体力学调制与速度聚束调制）的影响进行了对比分析,结果显示:速度聚束调制对SAR图像的影响最为显著。另外,由于SAR图像中固有相干斑噪声的存在,较低波数范围的噪声难以滤除或抑制,利用经典MPI方法反演海浪谱会造成低波数范围谱值偏大。基于此,本文借鉴经典MPI海浪谱反演算法,建立了基于速度聚束调制的海浪方位向斜率谱和有效波高的反演算法。通过将经典MPI方法、同极化调制法及本文算法等3种海浪反演方法所得有效波高与浮标数据进行比较,结果显示:本文方法反演得到的海浪有效波高与浮标数据获得的有效波高之间的均方误差为0.79 m,为3种方法中最小。     

海浪成长状态对TOPEX/Poseidon高度计风速反演的影响

以墨西哥湾同步高度计、浮标资料为例,研究了海浪成长状态对高度计风速反演的影响。同步的高度计风速和浮标风速比较显示,在墨西哥湾地区,海浪成长状态对高度计风速反演有较大影响。在考虑海浪成长状态影响的条件下,利用谱模型反演高度计风速,取得了较好的效果。与目前TOPEX/Poseidon高度计风速反演业务化算法相比,在海浪未充分成长条件下,考虑海浪成长状态影响后,根据谱模型反演获得的风速与浮标风速之间的均方根误差减小了30%,平均误差减小了83%。在利用谱模型算法反演高度计风速时,谱模型中的波龄因子(表示海浪成长状态)可以根据高度计测得的有效波高和风速获得,因此该方法具有广泛的适用性。     

一种基于Envisat ASAR波模式数据的有效波高反演算法

The main objective of this paper is to propose a newly developed ocean Significant Wave Height(SWH) retrieval method from Envisat Advanced Synthetic Aperture Radar(ASAR) imagery. A series of wave mode imagery from January, April and May of 2011 are collocated with ERA-Interim reanalysis SWH data. Based on the matched datasets, a simplified empirical relationship between 22 types of SAR imagery parameters and SWH products is developed with the Genetic Algorithms Partial Least-Squares(GA-PLS) model. Two major features of the backscattering coefficient σ_0 and the frequency parameter S_(10) are chosen as the optimal training feature subset of SWH retrieval by using cross validation. In addition, we also present a comparison of the retrieval results of the simplified empirical relationship with the collocated ERA-Interim data. The results show that the assessment index of the correlation coefficient, the bias, the root-mean-square error of cross validation(RMSECV) and the scattering index(SI) are 0.78, 0.07 m, 0.76 m and 0.5, respectively. In addition, the comparison of the retrieved SWH data between our simplifying model and the Jason-2 radar altimeter data is proposed in our study.Moreover, we also make a comparison of the retrieval of SWH data between our developed model and the wellknown CWAVE_ENV model. The results show that satisfying retrieval results are acquired in the low-moderate sea state, but major bias appears in the high sea state, especially for SWH5 m.     

使用参数化初猜测谱算法提取VV极化Sentinel-1 SAR图像的海浪要素并与ECMWF再分析数据以及浮标数据进行验证对比

The purpose is to study the accuracy of ocean wave parameters retrieved from C-band VV-polarization Sentinel-1Synthetic Aperture Radar(SAR) images, including both significant wave height(SWH) and mean wave period(MWP), which are both calculated from a SAR-derived wave spectrum. The wind direction from in situ buoys is used and then the wind speed is retrieved by using a new C-band geophysical model function(GMF) model,denoted as C-SARMOD. Continuously, an algorithm parameterized first-guess spectra method(PFSM) is employed to retrieve the SWH and the MWP by using the SAR-derived wind speed. Forty–five VV-polarization Sentinel-1 SAR images are collected, which cover the in situ buoys around US coastal waters. A total of 52 subscenes are selected from those images. The retrieval results are compared with the measurements from in situ buoys. The comparison performs good for a wind retrieval, showing a 1.6 m/s standard deviation(STD) of the wind speed, while a 0.54 m STD of the SWH and a 2.14 s STD of the MWP are exhibited with an acceptable error.Additional 50 images taken in China's seas were also implemented by using the algorithm PFSM, showing a 0.67 m STD of the SWH and a 2.21 s STD of the MWP compared with European Centre for Medium-range Weather Forecasts(ECMWF) reanalysis grids wave data. The results indicate that the algorithm PFSM works for the wave retrieval from VV-polarization Sentinel-1 SAR image through SAR-derived wind speed by using the new GMF C-SARMOD.     

基于SVM回归模型的SAR图像有效波高估测

A new method for estimating significant wave height(SWH) from advanced synthetic aperture radar(ASAR) wave mode data based on a support vector machine(SVM) regression model is presented. The model is established based on a nonlinear relationship between σ0, the variance of the normalized SAR image, SAR image spectrum spectral decomposition parameters and ocean wave SWH. The feature parameters of the SAR images are the input parameters of the SVM regression model, and the SWH provided by the European Centre for Medium-range Weather Forecasts(ECMWF) is the output parameter. On the basis of ASAR matching data set, a particle swarm optimization(PSO) algorithm is used to optimize the input kernel parameters of the SVM regression model and to establish the SVM model. The SWH estimation results yielded by this model are compared with the ECMWF reanalysis data and the buoy data. The RMSE values of the SWH are 0.34 and 0.48 m, and the correlation coefficient is 0.94 and 0.81, respectively. The results show that the SVM regression model is an effective method for estimating the SWH from the SAR data. The advantage of this model is that SAR data may serve as an independent data source for retrieving the SWH, which can avoid the complicated solution process associated with wave spectra.     

Directional spectrum analysis and statistics obtained from ERS-1 SAR wave images

This work examines ERS-1 (the first European Remote Sensing Satellite) SAR (Synthetic Aperture Radar) water surface wave images over Hualien of Taiwan, indicating that the variation of SAR signals in space domain is similar to in situ wave data's in time domain. Some statistical properties of SAR data are investigated. The Rayleigh distribution function closely corresponds with the histogram of wave heights, but the Gaussian one cannot for water surface displacements. Evidence reveals that SAR wave signals do not respond well to actual ocean waves effectively. As wave spectral analysis of available SAR data reveals, the appropriate sample size of SAR wave image, sampling average, and moving average should be taken carefully to accurately confirm directional power spectra. Moreover, SAR spectra are compared with in situ ones, confirming that peak frequencies correlate well and wave directions approximately agree with each other. Some differences between both spectral shapes remain somewhat unclear and require further study. Nevertheless, in this study, ERS-1 SAR power spectra verified the feasibility of deriving an appropriate dominant wave direction and peak frequency.     

Quality evaluation and calibration of the SWIM significant wave height product with buoy data

The significant wave height (SWH) is one of the main parameters that describe wave characteristics and is widely used in wave research fields. Wave parameters measured by radar are influenced by the offshore distance and sea state. Validation and calibration are of great significance for radar data applications. The nadir beam of surface wave investigation and monitoring (SWIM) detects the global-ocean-surface SWH. To determine the product quality of SWIM SWH, this paper carried out time-space matching between SWIM and buoy data. The data qualities were evaluated under different offshore distances and sea states. An improved calibration method was proposed based on sea state segmentation, which considered the distribution of the point collocation numbers in various sea states. The results indicate that (1) the SWIM SWH accuracy at offshore distances greater than 50 km is higher than that at distances less than 50 km, with an root mean squared error (RMSE) of 0.244 4 m, scatter index (SI) of 0.115 6 and relative error (RE) of 9.97% at distances greater than 50 km and those of 0.446 0 m, 0.223 0 and 18.66% at distances less than 50 km. (2) SWIM SWH qualities are better in moderate and rough sea states with RMSEs of 0.284 8 m and 0.316 9 m but are worse in slight and very rough sea states. (3) The effect of the improved calibration method is superior to the traditional method in each sea state and overall data, and the RMSE of SWIM SWH is reduced from the raw 0.313 5 m to 0.285 9 m by the traditional method and 0.198 2 m by the improved method. The influence of spatiotemporal window selection on data quality evaluation was analyzed in this paper. This paper provides references for SWIM SWH product applications.     

时空窗口对HY-2有效波高产品检验影响模拟研究

时空窗口的选择是卫星高度计有效波高产品检验的主要影响因素。采用Monte Carlo(MC)数学模拟的方法 ,研究了时空窗口对HY-2高度计有效波高检验的影响,并采用现场浮标测量数据验证了MC模拟的可靠性。MC模拟结果表明,采用浮标测量数据对HY-2高度计有效波高检验时,必须分海况选取对应的最优空间窗口进行,并给出不同海况下的最优的时空窗口。对于高海况需采用小的空间窗口,在1 m,2 m,3 m,4 m有效波高的海况下,其理想的时空窗口为0 min,117 km,30 km,18 km和13 km。     

Oil-Spill Monitoring in the Coastal Waters of Hong Kong and Vicinity

In this study, we applied the edge-detection method of oil-spill monitoring to extract oil-spill features observed by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) images over the coastal waters of Hong Kong and vicinity in northern South China Sea. Two examples in 2007 and 2008 over the coastal waters of the study area show that oil spills can be successfully detected by ASAR images at wind speeds around 4～ 6 m/s independent of wind direction. The study also shows that it could be helpful for evaluating the potential impacts of oil spills on the coastal environment in Hong Kong and vicinity.     

Evaluation of ENVISAT ASAR data for sea surface wind retrieval in Hong Kong coastal waters of China

The C-band wind speed retrieval models, CMOD4, CMOD - IFR2, and CMOD5 were applied to retrieval of sea surface wind speeds from ENVISAT （European environmental satellite） ASAR （advanced synthetic aperture radar） data in the coastal waters near Hong Kong during a period from October 2005 to July 2007. The retrieved wind speeds are evaluated by comparing with buoy measurements and the QuikSCAT （quick scatterometer） wind products. The results show that the CMOD4 model gives the best performance at wind speeds lower than 15 m/s. The correlation coefficients with buoy and QuikSCAT winds are 0.781 and 0.896, respectively. The root mean square errors are the same 1.74 m/s. Namely, the CMOD4 model is the best one for sea surface wind speed retrieval from ASAR data in the coastal waters near Hong Kong.     

A semiempirical algorithm for SAR wave height retrieval and its validation using Envisat ASAR wave mode data

Many synthetic aperture radar(SAR) wave height retrieval algorithms have been developed.However,the wave height retrievals from most existing methods either depend on other input as the first guess or are restricted to the long wave regime.A semiempirical algorithm is presented,which has the objective to estimate the wave height from SAR imagery without any prior knowledge.The proposed novel algorithm was developed based on the theoretical SAR ocean wave imaging mechanism and the empirical relation between two types of wave period.The dependency of the proposed model on radar incident and wave direction was analyzed.For Envisat advanced synthetic aperture radar(ASAR) wave mode data,the model can be reduced to the simple form with two input parameters,i.e.,the cutoff wavelength and peak wavelength of ocean wave,which can be retrieved from SAR imagery without any prior knowledge of wind or wave.Using Envisat ASAR wave mode data and the collocated buoy measurements from NDBC,the semiempirical algorithm is validated and compared with the Envisat ASAR level 2 products.The root-mean-square-error(RMSE) and scatter index(SI) in respect to the in situ measurements are 0.52 m and 19% respectively.Validation results indicate that,for Envisat ASAR wave mode data,the proposed method works well.