基于C-2PO模型和CMOD5.N地球物理模式函数的SAR风速反演性能评估

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。     

一种改进海面风速反演的分类神经网络方法

为了提高使用SSM/I资料反演全球海面风速的精度,发展了一个新型的神经网络方法.在这个方法中,使用高风速、中、低风速状态和天气状态分类的方法分别训练神经网络,然后根据其类别的不同使用不同的神经网络计算风速.此方法较好地去除了由于高风速和云天天气状态下训练样本数据的缺少所产生的误差,改进了在高风速状态下反演风速值比实际风速偏低的情况,使得反演的高风速值被校正到了正常位置.本方法反演海面风速的值与浮标实测风速值之间的均方根误差达到1.60m/s.     

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.     

海浪对ASCAT散射计反演风场的影响研究

To improve retrieval accuracy, this paper studies wave effects on retrieved wind field from a scatterometer. First, the advanced scatterometer(ASCAT) data and buoy data of the National Data Buoy Center(NDBC) are collocated. Buoy wind speed is converted into neutral wind at 10 m height. Then, ASCAT data are compared with the buoy data for the wind speed and direction. Subsequently, the errors between the ASCAT and the buoy wind as a function of each wave parameter are used to analyze the wave effects. Wave parameters include dominant wave period(dpd), significant wave height(swh), average wave period(apd) and the angle between the dominant wave direction(dwd) and the wind direction. Collocated data are divided into sub-datasets according to the different intervals of each wave parameter. A root mean square error(RMSE) for the wind speed and a mean absolute error(MAE) for the wind direction are calculated from the sub-datasets, which are considered as the function of wave parameters. Finally, optimal wave conditions on wind retrieved from the ASCAT are determined based on the error analyses. The results show the ocean wave parameters have correlative relationships with the RMSE of the retrieved wind speed and the MAE of the retrieved wind direction. The optimal wave conditions are presented in terms of dpd, swh, apd and angle.     

基于神经网络的卫星高度计海面风速反演算法

通过对TOPEX/Poseidon高度计资料与NDBC浮标实测数据进行时空匹配处理,得到同步数据集,利用人工神经网络方法试验得到海面风速反演算法,并与业务运行的M CW算法进行分析比较,指出考虑波浪状态影响因素的神经网络算法在均方根误差和对称性方面的优越性。研究表明利用神经网络方法反演海面风速是可行的。     

利用C波段RADARSAT-2单极化SAR图像的方位向截断波长估计有效波高

This paper proposes two simple models, look-up table(LUT) model and empirical model, to directly retrieve significant wave height(Hs) using synthetic aperture radar(SAR) azimuth cutoff(λc). Both models aim at C-band VV, HH, VH, and HV single-polarization SAR images. The LUT model relates Hs to λc, while the empirical model relates Hs to both λc and SAR range-to-velocity(β). The LUT model coefficients are derived by simulation under different sea states and observation conditions, which depend on incidence angle(θ), wave direction(dw), and βbut are independent of polarization. The empirical model coefficients are obtained by fitting the collocated data,which only depend on polarization. To fit empirical model coefficients and validate the two models, C-band RADARSAT-2 fine quad-polarization(VV+HH+VH+HV) single-look complex(SLC) SAR images and collocated buoy data are collected. Retrieved Hs, using Yang model and the two models proposed in this paper from four kinds of polarization SAR data, are compared with buoy Hs. Results show that both LUT and empirical models have the capacity of retrieving Hs from C-band RADARSAT-2 co-polarization SAR data, while Yang model is not suitable for these kinds of SAR data. Moreover, the empirical model is also valid for cross-polarization SAR data showing clear ocean wave stripes.     

卫星高度计海面风速的校准与验证

为了改善不同卫星高度计海面风速数据之间的一致性,以浮标数据为基准,对国内的HY-2A和国外的T/P、GFO、Jason-1、Envisat、Jason-2、CryoSat-2共7颗卫星高度计的海面风速数据进行了分析,给出了各个卫星高度计的海面风速校准公式,并对其校准效果进行了验证。验证结果表明:各个卫星高度计的海面风速在经过校准后,与浮标海面风速差异的均值和均方根都有所降低,其中HY-2A最为显著。经过校准后所有卫星高度计的海面风速与浮标海面风速差异的均值都在±0.2m/s以内。除了HY-2A、GFO和Jason-1,其余4颗卫星高度计校准后的海面风速与浮标海面风速差异的均方根都在1.6m/s以下。由此可以得出结论,利用本文的校准公式对各个卫星高度计(特别是HY-2A卫星高度计)的海面风速进行校准,可以有效减少其与浮标海面风速之间的差异。     

基于SAR图像雨团足印的海面风向提取方法

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。     

基于SAR多普勒质心频移的海面流场迭代反演算法

为了克服SAR多普勒质心频移法反演海面流场时风场贡献去除困难的难题,本文提出了基于M4S模型的弦截下山法,利用其迭代计算局部区域的海面流场;然后估算整幅SAR图像中风场对多普勒速度的风贡献因子7;最后去除风场对多普勒速度的贡献.将该算法用于Radarsat-2数据反演海面径向流速,并利用匹配的实测数据验证反演精度.研究...     

Wind speed inversion and in-orbit assessment of the imaging altimeter on Tiangong-2 space station

Imaging altimeter (IALT) is a new type of radar altimeter system. In contrast to the conventional nadir-looking altimeters, such as HY-2A altimeter, Jason-1/2, and TOPEX/Poseidon, IALT observes the earth surface at low incident angles (2.5°–8°), so its swath is much wider and its spatial resolution is much higher than the previous altimeters. This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station. Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles, a neural network is used to retrieve the ocean surface wind speed in this study. The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed, buoy wind speed, and in-situ ship measurements. The results show that the retrieved wind speed bias is about –0.21 m/s, and the root-mean-square (RMS) error is about 1.85 m/s. The wind speed accuracy of IALT meets the performance requirement.     

两种ASCAT 散射计风产品的比较及评估

基于浮标和步进频率微波辐射计(SFMR,Stepped-Frequency Microwave Radiometer)数据对NASA JPL(Jet Propulsion Laboratory)和RSS(Remote Sensing Systems)公司分别发布的已广泛应用于全球海面风场观测的ASCAT(Advanced SCATterometer)散射计风产品进行了比较和分析。结果表明,两者风速在中低风速(15 m/s)时基本一致;高风速(15 m/s)时RSS风速整体高于JPL风速。通过浮标数据对比,风速15 m/s时两者风速精度一致;风速15 m/s时两者风速RMS相当,但JPL和RSS风速分别低估和高估。利用SFMR数据检验表明RSS风速与SFMR风速一致性更好。两者风向精度在低风速(5 m/s)时较低,但随风速增加而提高并趋于稳定。该研究结果对相关科研人员的ASCAT散射计风产品选择具有重要的指导意义。     

利用AMSR—E卫星数据反演蒸发波导高度的BP神经网络方法

主要结合P-J模式和全通道AMSR-E卫星数据,利用两种BP神经网络方法对热带海区蒸发波导高度进行了反演研究。(1)利用BP神经网络反演得到的气象参数通过P-J模式计算蒸发高度;(2)利用卫星AMSR-E数据直接反演蒸发波导高度。两种方法得到的与浮标实测参数计算得到的蒸发波导高度之间的相关系数相当,都为0.82左右,均方根差后者比前者小,分别为2.64m和1.89m。利用后一种网络直接反演了南海地区的蒸发波导高度,平均结果与其他文献结果基本一致。     

基于混合密度网络的NSCAT散射计海面风场反演

以NSCAT散射计数据为例,介绍了一种神经网络反演海面风场的方法.风速的反演是基于多层感知器网络;多解风向的反演是基于多层感知器网络和混合密度模型组合而成的混合密度网络,其中的核函数采用高斯函数的形式.通过与欧洲中期天气预报模式风场和现场浮标数据对比,证明了该神经网络反演海面风场的有效性.     

基于神经网络技术的遥感水深反演模型研究

利用Landsat7 ETM+遥感影像反射率和实测水深值之间的相关性,选取了相关性较好的ETM1、ETM2、ETM3、ETM4、ETM3/ETM2等5个水深反演因子,建立了BP神经网络水深反演模型。为充分体现BP神经网络模型的优越性,利用SPSS软件建立了单波段、波段比值、多波段三种不同的线性回归模型。通过对比发现,具有很好的自适应能力和非线性映射能力的BP神经网络模型在处理遥感水深反演问题上比传统的线性模型效果更好。     

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.     

OSMAR-S100便携式高频地波雷达海浪和海面风探测性能分析

OSMAR-S系列便携式高频地波雷达系统采用单极子/交叉环紧凑型天线阵,通过单站雷达即可实现有效探测距离约10km内海浪和海面风的单点观测。为了更好地了解OSMAR-S100雷达系统海浪和海面风的综合探测性能,于2013年1月29日至3月7日在台湾海峡西南部海域进行了雷达与浮标观测的对比试验,得到了有效波高、有效波周期、平均风速和平均风向数据。对比结果表明,OSMAR-S100便携式高频地波雷达可有效观测距雷达10km以内有效波高0.5m以上的海浪平均状况和平均风速5m/s以上的海面风,雷达反演有效波高和有效波周期的均方根误差分别为0.60m和1.60s,反演平均风速和平均风向的均方根误差为1.83m/s和16.7°。在未经区域化标定的情况下,此结果说明了该型雷达产品已初步具备了海浪和海面风的业务化观测水平。     

基于BP人工神经网络的水体遥感测深方法研究

利用Landsat7ETM＋遥感图像反射率和实测水深值之间的相关性,建立了动量BP人工神经网络水深反演模型,并对长江口南港河段水深进行了反演.结果表明：具有较强非线性映射能力的动量BP神经网络模型能较好地反演出长江口南港河段的水深分布情况;由于受长江口水体高含沙量的影响,模型对小于5 m的水深值反演精度较高,而对大于10 m的水深值反演精度较低.     

星载SAR对雨团催生海面风场的观测研究

雨团或对流雨是热带与亚热带地区的主要降雨形式,较易被高分辨率星载合成孔径雷达（SAR）探测到。SAR图像上的雨团足印是由大气中雨滴的散射与吸收、下沉气流等共同导致形成的。本文以RADARSAT-2卫星100 m分辨率的SAR图像上雨团引起的海面风场及其结构反演与解译作为实例进行分析。使用CMOD4地球物理模式函数,分别以NCEP再分析数据、欧洲MetOp-A卫星先进散射计（ASCAT）和中国HY-2卫星微波散射计的风向为外部风向,进行了SAR图像的海面风场反演。反演的海面风速相对于NCEP、ASCAT和HY-2的均方根误差（RMSE）分别为1.48 m/s,1.64 m/s和2.14 m/s。SAR图像上一侧明亮另一侧昏暗的圆形信号图斑被解译为雨团携带的下沉气流对海面风场（海面粗糙度）的改变所致。平行于海面背景风场其通过雨团圆形足印中心的剖面上的风速变化可拟合为正弦或余弦曲线,其拟合线性相关系数均不低于0.80。背景风场的风速大小、雨团引起的风速大小以及雨团足印的直径可利用拟合曲线获得,雨团足印的直径大小一般为数千米或数十千米,本文的8例个例解译与分析均验证了该结论。     

基于BP人工神经网络的水体遥感测深方法研究

利用Landsat7 ETM 遥感图像反射率和实测水深值之间的相关性,建立了动量BP人工神经网络水深反演模型,并对长江口南港河段水深进行了反演。结果表明:具有较强非线性映射能力的动量BP神经网络模型能较好地反演出长江口南港河段的水深分布情况;由于受长江口水体高含沙量的影响,模型对小于5m的水深值反演精度较高,而对大于10m的水深值反演精度较低。     

基于极限学习机的浅海水深遥感反演研究

以Sentinel-2A遥感影像为数据源,在数据预处理基础上,使用极限学习机建立水深反演非线性回归模型,并与双波段回归模型、神经网络BP模型进行了比较。结果表明,总体上,支持极限学习机反演精度比较高,在水深5～20m处,极限学习机模型具有较高的反演精度和良好的效果,适用于研究区的水深反演。