星载合成孔径雷达图像反演海面风场方法综述

合成孔径雷达作为微波雷达,具有全天候、全天时、高分辨率对海面成像的能力,能实现多波段、多极化、多视角的观测海面,可以提供大范围、高精度的实时动态海面信息,对其进行深入研究有着重要的意义。基于合成孔径雷达图像,分别以风向和风速反演为主线,对国内外海面风场反演的工作进行了详细的总结。     

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。     

X波段航海雷达资料反演海面风场进展研究

对X波段航海雷达资料反演海面风场的研究进展作一综述。首先介绍了X波段航海雷达资料反演海面风场的基本原理;然后对基于梯度算法的风向反演、基于神经网络算法的风速反演和基于光流法的风矢量反演进行了全面叙述;最后对未来研究前景进行了展望。     

星载激光雷达卫星海面风场反演算法研究

The principal purpose of this paper is to extract entire sea surface wind＇s information from spaceborne lidar, and particularly to utilize a appropriate algorithm for removing the interference information due to white caps and subsurface water. Wind speeds are obtained through empirical relationship with sea surface mean square slopes. Wind directions are derived from relationship between wind speeds and wind directions im plied in CMOD5n geophysical models function （GMF）. Whitecaps backscattering signals were distinguished with the help of lidar depolarization ratio measurements and rectified by whitecaps coverage equation. Subsurface water backscattering signals were corrected by means of inverse distance weighted （IDW） from neighborhood non-singular data with optimal subsurface water backscattering calibration parameters. To verify the algorithm reliably, it selected NDBC＇s TAO buoy-laying area as survey region in camparison with buoys＇ wind field data and METOP satellite ASCAT of 25 km single orbit wind field data after temporal-spa tial matching. Validation results showed that the retrieval algorithm works well in terms of root mean square error （RMSE） less than 2m/s and wind direction＇s RMSE less than 21 degree.     

利用多波段卫星数据进行浅海水深反演方法研究

以遥感反演水深的基本原理为基础,利用我国南海永暑礁景区的TM数据和实测水深资料,通过TM多波段数据辐射校正、图像与海图地理配推、底质类型分区、潮汐改正和实测水深数据与相应的图像辐射值回归分析,建立了浅海水深反演模型,并进行了浅海岛礁水深的实际计算,总标准误差为2．14m。对我国南海30m以浅岛礁水深地形研究有很好的应用价值。     

用圆中数滤波器排除卫星散射计风场反演中的风向模糊

由卫星散射计测量得到的归一化雷达截面积σ^0值以及这些σ^0值关联海面风速风向的经验模型函数可以反演海面的风矢量。但是对应一分辨面元上测量的σ^0值并不是唯一的风矢量解与之对应,选择一个风矢量解来表示真风矢量的处理过程就叫模糊排除或称消去伪解。本文引入一种圆中数滤波技术,在由模拟的ERS－1散射计数数据反演风场中,不附加任何其他信息的情况下,圆中数滤波器改进风向模糊排除方法,很好地重现模拟的海面真风矢量。     

合成孔径雷达反演黄海海面风场

基于后向散射系数反演高空间分辨率海面风场,采用谱方法确定风向,并利用CMOD4模式函数反演风速。以ERS-2 SAR黄海区域图像为例,反演海面风场,并将反演结果同QuikSCAT散射计对比,比较吻合,证明该方法在黄海区域的可行性。     

卫星SAR图像的海洋大气边界层特征参数的反演

针对中国南海ERS-2／SAR三维对流涡旋图像,分别利用Young的协方差方法和惯性子域方法,反演了对流海洋大气边界层高度、海洋大气边界层垂直对流尺度速度、表面浮力通量、Obukhov长度、稳定性校正后的海面风速和海面风速稳定性校正因子。结果表明,2种反演方法的结果符合较好,在一定程度上说明利用协方差方法和惯性子域方法从卫星SAR图像反演海洋大气边界层参数的有效性。     

一种新的海洋风场反演中风向模糊排除方法研究

本文给出了一种从ＳＡＳＳ测量的后向散射强度手数据中反演出大尺度海洋风场的新方法。计算结果与Ｐｅｔｅｈｅｒｙｃｈ等利用ＳＡＳＳ表面风分析的结果比较在风向上是吻合的，在风速上本文所得结果更宇海面赵实风速。上述结果说明本文所采用的多解排除对两波束散射计探测海面风场的反演是成功的。     

多普勒雷达风场反演研究及应用

利用VAP方法,利用舟山新一代天气雷达基数据资料,对2008年7月2日和2009年6月20日二次过程进行风场反演,结合新一代天气雷达反射率因子资料,从风场动力结构剖析强对流天气入海后变化的主要原因,掌握强对流天气入海后的发展加强和减弱消散的趋势性变化,提高强对流天气预报准确率.进一步的利用浙江省自动测风站网资料和反演风场资料进行对比分析,结果表明:反演风场的风向基本和事实相符;风速和实测资料的风速存在差异,并指出了产生误差的主要原因.     

HY-2B卫星载荷联合观测海面阵风的一种反演方法

国内外对海上阵风的研究并不多,且大多集中在阵风预报和应用研究方面,对于海洋阵风数据的获取技术未见文献系统论述。本文利用HY-2B卫星雷达高度计观测的后向散射系数,结合校正微波辐射计观测的亮度温度信息,提出联合反演阵风风速的方法。两个遥感载荷联合反演得到的阵风风速与2019–2021年美国国家浮标数据中心（NDBC）浮标数据进行真实性检验,结果显示：阵风风速均方根误差（RMSE）为0.98 m/s,相关系数为0.82;基于本方法利用国外同类卫星Jason-3得到的阵风风速与2016–2018年NDBC浮标数据的RMSE为0.96 m/s,相关系数为0.88。本文在HY-2B卫星雷达高度计海面风速观测的基础上,纳入同一卫星平台校正微波辐射计的同步观测信息联合实现了海面阵风的观测,数据的比对结果证明文中方法具有较高的观测精度。同时,该方法对于具有相同观测体制的国内外卫星也适用。     

背景场对变分方法海面风速反演结果的影响

利用前期提出的合成孔径雷达反演海面风场变分方法新解法,开展背景场对合成孔径雷达反演海面风场变分方法风速反演结果影响的模拟试验分析。模拟试验表明,变分方法有效解决了直接反演方法的两大缺陷:一是当背景场风速较大的时候,合成孔径雷达后向散射系数对风速不敏感,直接导致较大的风速反演误差;二是当背景场风向存在误差时,将导致后向散射系数更大的误差,进而导致风速反演结果更大的误差。进一步试验表明,当背景场风速较大时,变分方法风速误差低于直接反演方法风速误差。而当背景场风向误差较小时,直接反演方法风速误差比变分方法风速误差小。     

全极化合成孔径雷达近岸风场反演研究

Coastal winds are strongly influenced by topology and discontinuity between land and sea surfaces. Wind assessment from remote sensing in such a complex area remains a challenge. Space-borne scatterometer does not provide any information about the coastal wind field, as the coarse spatial resolution hampers the radar backscattering. Synthetic aperture radar （SAR） with a high spatial resolution and all-weather observation abilities has become one of the most important tools for ocean wind retrieval, especially in the coastal area. Conventional methods of wind field retrieval from SAR, however, require wind direction as initial information, such as the wind direction from numerical weather prediction models （NWP）, which may not match the time of SAR image acquiring. Fortunately, the polarimetric observations of SAR enable independent wind retrieval from SAR images alone. In order to accurately measure coastal wind fields, this paper proposes a new method of using co-polarization backscattering coefficients from polarimetric SAR observations up to polarimetric correlation backscattering coefficients, which are acquired from the conjugate product of co-polarization backscatter and cross-polarization backscatter. Co-polarization backscattering coefficients and polarimetric correlation backscattering coefficients are obtained form Radarsat-2 single-look complex （SLC） data.The maximum likelihood estimation is used to gain the initial results followed by the coarse spatial filtering and fine spatial filtering. Wind direction accuracy of the final inversion results is 10.67 with a wind speed accuracy of 0.32 m/s. Unlike previous methods, the methods described in this article utilize the SAR data itself to obtain the wind vectors and do not need external wind directional information. High spatial resolution and high accuracy are the most important features of the method described herein since the use of full polarimetric observations contains more information about the space measured.This article is a useful addition to the work of independent SAR wind retrieval. The experimental results herein show that it is feasible to employ the co-polarimetric backscattering coefficients and the polarimetric correlation backscattering coefficients for coastal wind field retrieval.     

基于理论的气旋条件下双极化C波段合成孔径雷达波参数检测

Theoretical-based ocean wave retrieval algorithms are applied by inverting a synthetic aperture radar(SAR)intensity spectrum into a wave spectrum, that has been developed based on a SAR wave mapping mechanism. In our previous studies, it was shown that the wave retrieval algorithm, named the parameterized first-guess spectrum method(PFSM), works for C-band and X-band SAR at low to moderate sea states. In this work, we investigate the performance of the PFSM algorithm when it is applied for dual-polarization c-band sentinel-1(S-1) SAR acquired in extra wide-swath(EW) and interferometric wide-swath(IW) mode under cyclonic conditions.Strong winds are retrieved from six vertical-horizontal(VH) polarization S-1 SAR images using the c-band crosspolarization coupled-parameters ocean(C-3 PO) model and then wave parameters are obtained from the image at the vertical-vertical(VV) polarization channel. significant wave height(SWH) and mean wave period(MWP) are compared with simulations from the WAVEWATCH-III(WW3) model. The validation shows a 0.69 m root mean square error(RMSE) of SWH with a –0.01 m bias and a 0.62 s RMSE of MWP with a –0.17 s bias. Although the PFSM algorithm relies on a good quality SAR spectrum, this study confirms the applicability for wave retrieval from an S-1 SAR image. Moreover, it is found that the retrieved results have less accuracy on the right sector of cyclone eyes where swell directly affects strong wind-sea, while the PFSM algorithm works well on the left and rear sectors of cyclone eyes where the interaction of wind-sea and swell is relatively poor.     

Atmospheric frontal gravity waves observed in satellite SAR images of the Bohai Sea and Huanghai Sea

In the satellite synthetic aperture radar (SAR) images of the Bohai Sea and Huanghai Sea, the authors observe sea surface imprints of wave-like patterns with an average wavelength of 3.8 km. Comparing SAR observations with sea surface wind fields and surface weather maps, the authors find that the occurrence of the wave-like phenomena is associated with the passing of atmospheric front. The authors define the waves as atmospheric frontal gravity waves. The dynamical parameters of the wave packets are derived from statistics of 9 satellite SAR images obtained from 2002 to 2008. A two-dimensional linear physical wave model is used to analyze the generation mechanism of the waves. The atmospheric frontal wave induced wind variation across the frontal wave packet is compared with wind retrievals from the SAR images. The CMOD-5 (C-band scatterometer ocean geophysical model function) is used for SAR wind retrievals VV (transmitted vertical and received vertical) for ENVISAT and HH (transmitted horizontally and received horizontally) for RADARSAT-1. A reasonable agreement between the analytical solution and the SAR observation is reached. This new SAR frontal wave observation adds to the school of SAR observations of sea surface imprints of AGWs including island lee waves, coastal lee waves, and upstream Atmospheric Gravity Waves (AGW).     

基于人工神经网络的高频雷达风速反演

风速是重要的海洋状态参数之一,对海面风速的准确提取是实现海洋环境监测和沿海工程应用的重要保证。目前,作为新兴海洋环境监测设备,高频雷达在风速提取方面仍然存在挑战。本文提出了一种基于人工神经网络的风速提取方法,利用历史浮标测量海态数据训练风速提取网络,实现风速与有效波高、波周期、风向及时间因素之间的非线性映射。测试结果表明了这一网络在时间和空间上的稳定性;进而将已训练的网络应用到便携式高频地波雷达OSMAR-S的风速反演中,得到的风速与浮标测量风速间的相关系数达到0.849,均方根误差为2.11 m/s。这一结果明显优于常规由浪高反演风速的SMB方法,验证了该方法在高频雷达风速反演中的可行性。     

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.     

合成孔径雷达(SAR)图像的真实值定标与校验

随着合成孔径雷达（ＳＡＲ）的应用研究程度的深入，要求较为精确地掌握目标体的雷达后向散射特征，这样必须对ＳＡＲ图象进行真实值的定标与校验。本文基于基本散射理论对ＳＡＲ系统的增益进行分解，指出各个相关参数的获取方法，最后给出定标和数据处理流程图     

机载雷达立体影像定位方法的比较及改进

由于合成孔径雷达(SAR)具有全天候的成像能力,在地质、生态、水文、海冰和测绘等诸多领域有着广泛的应用。主要研究了雷达立体影像对获取地面三维坐标的方法,分析了目前所广泛使用的三种立体定位模型的优缺点,为利用雷达立体影像对获取数字高程模型提供了选择依据,并对其中的零距离-多普勒模型进行了改进,最后利用机载雷达影像数据对此算法进行了实验研究,取得了较好的实验结果。