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海洋微波散射模型相比于以经验统计建立的地球物理模式函数具有不受特定微波频率限制的优势。组合布拉格散射模型和几何光学模型形成了复合雷达后向散射模型。利用南海北部气象浮标2014年海面风速风向实测值作为散射模型输入，分别比较了复合雷达后向散射模型与RADARSAT-2卫星C波段SAR、HY-2A卫星Ku波段微波散射计的海面后向散射系数，偏差分别为(?0.22±1.88) dB (SAR)、(0.33±2.71) dB (散射计VV极化）和(?1.35±2.88) dB (散射计HH极化)；以美国浮标数据中心(NDBC)浮标2011年10月1日至2014年9月30日共3年的海面风速、风向实测值作为散射模型输入，分别比较了复合雷达后向散射模型与Jason-2、HY-2A卫星Ku波段高度计海面后向散射系数，偏差分别为(1.01±1.15) dB和(1.12±1.29) dB。中等入射角和垂直入射下的卫星传感器后向散射系数观测值与复合雷达后向散射模型模拟值比较，具有不同的偏差，但具有相同的海面风速检验精度，均方根误差小于1.71 m/s。结果表明，复合雷达后向散射模型可模拟计算星载SAR、散射计和高度计观测条件下的海面雷达后向散射系数，且与CMOD5、NSCAT-2、高度计业务化海面风速反演的地球物理模式函数的计算结果具有一致性；复合雷达后向散射模型可用于微波遥感器的定标与检验、海面雷达后向散射的模拟。 相似文献
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利用散射计测量海面后向散射系数, 并通过地球物理模型函数(geophysical model function, GMF)反演得到海面风场。目前散射计风场反演所采用的GMF一般只考虑雷达极化方式、雷达入射角、风速和相对风向对海面后向散射系数的影响, 而相关研究表明海表温度(sea surface temperature, SST)对Ku波段散射计风场反演具有不可忽略的影响。文章利用海洋二号A卫星散射计(Haiyang-2A Scatterometer, HY2A-SCAT)后向散射系数观测值、欧洲中期天气预报中心(European Center for Medium-Range Weather Forecasts, ECMWF )再分析风矢量和SST数据, 采用人工神经网络方法, 建立起一种SST相关的GMF (TNGMF)。对TNGMF进行分析后发现, 海面后向散射系数随着SST的增加而增加, 并且其增加幅度与雷达极化方式、风速有关。为了对比, 文章使用相同数据集和相同方法建立了不包含SST的GMF (NGMF), 将美国国家航天航空局散射计-2 (National Aeronautics and Space Administration Scatterometer-2, NSCAT2) GMF、TNGMF和NGMF分别用于HY2A-SCAT风场反演实验。试验结果表明, 采用NSCAT2 GMF、NGMF反演得到的风速在低温时系统性偏小, 在高温时系统性偏大; 而TNGMF可较好地纠正SST对风速偏差均值的影响, 从而提高反演风场质量。 相似文献
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合成孔径雷达反演黄海海面风场 总被引：1，自引：0，他引：1
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。 相似文献
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《海洋预报》2021,(2)
选取成像于中国东海和南海海域的102景RADARSAT-2(RS-2)精细四极化SAR原始影像以及ERA-Interim风场,在各极化通道下利用相应风速反演模型开展风速反演的研究。CMOD5地球物理模式函数对VV极化SAR数据反演风速效果最好,可以获取海面高精度风速数据。对于HH和交叉极化SAR影像,利用RS-2 SAR影像和ERA-Interim资料对现有风速反演模型进行改进,并提出了用于HH极化风速反演的PR_CS极化比模型和用于交叉极化风速反演的CP_CS模型。结果显示:基于两种模型的SAR反演风速与ERA-Interim风速具有良好的一致性,利用PR_CS模型的SAR反演风速与参考风速的均方根误差为1.54 m/s,利用CP_CS模型的SAR反演风速与ERA-Interim风速的均方根误差分别为1.43 m/s(VH极化)和1.51 m/s(HV极化)。 相似文献
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粗糙海面L 和C 双波段的代价函数多参量遥感反演分析 总被引：1，自引：0，他引：1
利用代价函数(cost function)方法,通过分析粗糙海面L和C双波段多极化遥感亮温对海表盐度、温度、风速和有效波高等参数的敏感性以及L和C双波段多极化的代价函数收敛特性,建立了反演海表盐度、温度、风速和有效波高等多参数的L和C双波段多极化代价函数模式。双波段遥感模式分析结果表明:(1)对于双参数的联合反演,L和C双波段垂直极化代价函数联合反演海表盐度和温度可以获得较好的反演结果。(2)L波段垂直极化和C波段水平极化代价函数联合反演海表盐度和风速较好。(3)对于三参数联合反演,L波段垂直极化和C波段的双极化联合反演盐度、温度和风速的精度较高。(4)L波段亮温对有效波高的敏感性较低(C波段经验模式不含有效波高),使得有效波高反演误差较大,L和C波段经验模式不适合反演有效波高参数。另外,为了定量分析L和C双波段代价函数的多参量遥感反演结果,采用加性噪音模拟亮温方法,对上述L和C双波段多极化模式的盐度、温度和风速等多参数联合反演误差进行了分析,均得出较好的结果。结论表明L和C双波段代价函数联合反演多参量可以明显提高参量反演精度,为粗糙海表面多参量的反演提供了新的方法和途径。 相似文献
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为了解各向异性随机粗糙海面的微波双站散射机制及其特性，本文利用解析近似的积分方程模型以及一种改进的半经验海浪谱模型实现了对各向异性随机粗糙海面的全极化微波散射仿真模拟，并与卫星观测数据、经验的地球物理模式函数及已有的解析近似散射模型仿真结果进行了对比，验证了仿真结果的可行性和准确性。利用该模型分析了入射波频率、入射角、极化方式、海面风速及风向等参数对各向异性海面双站散射的影响。模拟结果表明，在不同的入射角、散射角及方位角等观测几何条件下，海面不同波段的双站散射表现出不同的空间散射特性，且对风速、风向等海面动力学参数表现出不同的敏感性，以L波段为例，海面向后半球双站散射在各个极化方式下都对风速较为敏感，而在同极化方式下，其对风向的响应在中低风速和高风速条件下相反，整体而言，低风速下海面双站散射对风向更为敏感。这表明对于海面动力参数的反演，双站散射可以提供比传统单站雷达后向散射更丰富的物理信息。本文探讨了各向异性海面微波双站散射特性，为基于主动式及分布式微波传感器的海洋动力参数遥感反演提供了理论分析基础。 相似文献
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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. 相似文献
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The geophysical model function (GMF) describes the relationship between a backscattering and a sea surface wind, and enables a wind vector retrieval from backscattering measurements. It is clear that t... 相似文献
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基于SAR图像雨团足印的海面风向提取方法 总被引：1，自引：1，他引：0
利用地球物理模式函数进行SAR海面风速反演时，需以风向作为地球物理模式函数的输入。本文应用了一种利用SAR图像上雨团足印顺风一侧比逆风一侧明亮的图像特征的海面风向提取方法，以进行海面风速反演。4景RADARSAT-2卫星SAR示例数据风向提取结果相对于ASCAT散射计的风向均方根误差满足不大于16°。分别以本文方法提取的风向和ASCAT散射计风向作为输入，利用地球物理模式函数CMOD5进行海面风速的SAR反演，两者的风速反演结果基本一致，其均方根误差差值不超过0.3 m/s。本文利用SAR图像雨团足印信息的风向提取方法准确可靠，可应用于SAR海面风速反演。 相似文献
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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. 相似文献
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Evaluation of ENVISAT ASAR data for sea surface wind retrieval in Hong Kong coastal waters of China 总被引：2，自引：1，他引：1
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. 相似文献
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Conventional retrieval and neural network methods are used simultaneously to retrieve sea surface wind speed(SSWS) from HH-polarized Sentinel-1(S1) SAR images. The Polarization Ratio(PR) models combined with the CMOD5.N Geophysical Model Function(GMF) is used for SSWS retrieval from the HH-polarized SAR data. We compared different PR models developed based on previous C-band SAR data in HH-polarization for their applications to the S1 SAR data. The recently proposed CMODH, i.e., retrieving SSWS directly from the HHpolarized S1 data is also validated. The results indicate that the CMODH model performs better than results achieved using the PR models. We proposed a neural network method based on the backward propagation(BP)neural network to retrieve SSWS from the S1 HH-polarized data. The SSWS retrieved using the BP neural network model agrees better with the buoy measurements and ASCAT dataset than the results achieved using the conventional methods. Compared to the buoy measurements, the bias, root mean square error(RMSE) and scatter index(SI) of wind speed retrieved by the BP neural network model are 0.10 m/s, 1.38 m/s and 19.85%,respectively, while compared to the ASCAT dataset the three parameters of training set are –0.01 m/s, 1.33 m/s and 15.10%, respectively. It is suggested that the BP neural network model has a potential application in retrieving SSWS from Sentinel-1 images acquired at HH-polarization. 相似文献
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QIN Bangyong ZHOU Xuan ZHANG Honglei YANG Xiaofeng LU Rong YU Yang SHI Lijian 《海洋学报(英文版)》2014,33(7):83-88
Rain effect and lack of in situ validation data are two main causes of tropical cyclone wind retrieval errors. The National Oceanic and Atmospheric Administration＇s Climate Prediction Center Morphing technique （CMORPH） rain rate is introduced to a match-up dataset and then put into a rain correction model to remove rain effects on ＂Jason-1＂ normalized radar cross section （NRCS）; Hurricane Research Division （HRD） wind sPeed, which integrates all available surface weather observations, is used to substitute in situ data for establishing this relationship with ＂Jason-l＂ NRCS. Then, an improved ＂Jason-l＂ wind retrieval algorithm under tropical cyclone conditions is proposed. Seven tropical cyclones from 2003 to 2010 are studied to validate the new algorithm. The experimental results indicate that the standard deviation of this algorithm at C-band and Ku-band is 1.99 and 2.75 m/s respectively, which is better than the existing algorithms. In addition, the C-band algorithm is more suitable for sea surface wind retrieval than Ku-band under tropical cyclone conditions. 相似文献
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本文选取142幅RADARSAT-2全极化合成孔径雷达（SAR）影像，在没有入射角输入的情况下，首先利用C-2PO模型进行海面风速反演。随后，将同一时空下的ASCAT散射计风向作为初始风向，提取相应雷达入射角，利用地球物理模式函数（GMF） CMOD5.N对142幅SAR影像进行风速计算。反演结果与美国国家资料浮标中心海洋浮标风速数据对比，结果显示:CMOD5.N GMF和C-2PO模型均可反演出较高精确度的海面风速，其均方根误差分别为1.68 m/s和1.74 m/s。此外，研究发现，在低风速段，CMOD5.N GMF的风速反演精度要明显优于C-2PO模型。针对这一现象，本文以SAR系统成像机理为基础，以低风速SAR图像为具体案例，给出了3种造成这一现象的原因。 相似文献
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《Oceanic Engineering, IEEE Journal of》2005,30(3):508-515
A methodology for retrieving high-resolution ocean surface wind fields from satellite-borne synthetic aperture radar (SAR) data is introduced and validated. The algorithms developed are suited for ocean SAR data, which were acquired at the C band of either vertical (VV) or horizontal (HH) polarization in transmission and reception. Wind directions are extracted from wind-induced streaks that are visible in SAR images of the ocean at horizontal scales greater than 200 m. These wind streaks are very well aligned with the mean surface wind direction. To extract the orientation of these streaks, two algorithms are introduced, which are applied either in the spatial or spectral domain. Ocean surface wind speeds are derived from the normalized radar cross section (NRCS) and image geometry of the calibrated SAR images, together with the local SAR-retrieved wind direction. Therefore, several C-band models (CMOD IFR2, CMOD4, and CMODS) are available, which were developed for VV polarization, and have to be extended for HH polarization. To compare the different algorithms and C-band models as well as demonstrate their applicability, SAR-retrieved wind fields are compared to numerical-model results considering advanced SAR (ASAR) data from Environmental Satellite (ENVISAT), a European satellite. 相似文献