利用一种改进的算法对中国高分三号同极化SAR图像反演的有效波高的验证

Chinese Gaofen-3(GF-3) is the first civilian satellite to carry C-band(5.3 GHz) synthetic aperture radar(SAR).During the period of August 2016 to December 2017, 1 523 GF-3 SAR images acquired in quad-polarization(vertical-vertical(VV), horizontal-horizontal(HH), vertical-horizontal(VH), and horizontal-vertical(HV)) mode were recorded, mostly around China's seas. In our previous study, the root mean square error(RMSE) of significant wave height(SWH) was found to be around 0.58 m when compared with retrieval results from a few GF-3 SAR images in co-polarization(VV and HH) with moored measurements by using an empirical algorithm CSAR_WAVE. We collected a number of sub-scenes from these 1 523 images in the co-polarization channel,which were collocated with wind and SWH data from the European Centre for Medium-Range Weather Forecasts(ECMWF) reanalysis field at a 0.125° grid. Through the collected dataset, an improved empirical wave retrieval algorithm for GF-3 SAR in co-polarization was tuned, herein denoted as CSAR_WAVE2. An additional 92 GF-3 SAR images were implemented in order to validate CSAR_WAVE2 against SWH from altimeter Jason-2, showing an about 0.52 m RMSE of SWH for co-polarization GF-3 SAR. Therefore, we conclude that the proposed empirical algorithm has a good performance for wave retrieval from GF-3 SAR images in co-polarization.     

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.     

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.     

The comparison of altimeter retrieval algorithms of the wind speed and the wave period

With the launch of altimeter,much effort has been made to develop algorithms on the wind speed and the wave period.By using a large data set of collocated altimeter and buoy measurements,the typical wind speed and wave period algorithms are validated.Based on theoretical argument and the concept of wave age,a semi-empirical algorithm for the wave period is also proposed,which has the wave-period dimension,and explicitly demonstrates the relationships between the wave period and the other variables.It is found that Ku and C band data should be applied simultaneously in order to improve either wind speed or wave period algorithms.The dual-band algorithms proposed by Chen et al.(2002) for the wind speed and Quilfen et al.(2004) for the wave period perform best in terms of a root mean square error in the practical applications.     

Sea surface wind speed retrieval from Sentinel-1 HH polarization data using conventional and neural network methods

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.     

Sentinel-1卫星合成孔径雷达获取第一幅台风图像

In this note, we present the first Sentinel-1 synthetic aperture radar(SAR) typhoon image acquired in the northwest Pacific on October 4, 2014. The eye shape and sea surface wind patterns associated with Typhoon Phanfone are clearly shown in the high-quality SAR image. SAR winds retrieval procedure was given but the actual wind estimates will only be available after the European Space Agency(ESA) releases the official calibration coefficients in order to accurately derive the SAR-measured normalized radar cross section. This study demonstrates the advantage of Sentinel-1 SAR with regards to imaging fine scale typhoon patterns on the sea surface beneath storm clouds. This paper also advocates the use of Sentinel-1 SAR data that is made freely and openly available worldwide for the first time in civilian SAR history.     

SWAN模型中不同风拖曳力系数对风浪模拟的影响

本文以荷兰哈灵水道海域为实验区域,通过敏感性实验,研究了在14 m/s、31.5 m/s和50 m/s（分别代表一般大风、强热带风暴和强台风的极端条件）定常风速下SWAN模型中不同风拖曳力系数对风浪模拟的影响程度。结果表明,对于近岸浅水区域（水深小于20 m）,风拖曳力系数计算方案的选择对有效波高影响较小,而且当风速增加到一定程度后,波浪破碎成为影响波高值的主要因素;对于深水区域（水深大于30 m）,一般大风条件下风拖曳力系数计算方案的选择对有效波高影响仍然较小,随着风速的继续增大,风拖曳力系数计算方案的选择对有效波高的影响逐渐显著。对于平均周期,风拖曳力系数计算方案的选择和风速的改变对其影响均较小,而由水深变浅导致的波浪破碎对其影响较为显著。根据敏感性实验结果,本文对SWAN模型中风拖曳力系数计算方案的选择做出如下建议:计算近岸浅水区域风浪场或深水区域一般大风条件风浪场时,其风拖曳力系数可以直接采用模型默认选项;而对于深水区域更大风速条件,可首先采用模型默认选项试算,然后结合当地海域实测波浪资料进行修正。     

利用TOPEX卫星高度计资料分析东中国海的风、浪场特征

利用TOPEX卫星高度计和日本气象厅浮标观测资料,对东中国海的有效波高和风速进行比较,分析了卫星高度计资料的有效性。利用有效波高和风速的3种概率密度函数分布,结合TOPEX卫星高度计资料,并采用最大似然方法对统计分布参数进行估计,结果表明,有效波高的对数-正态概率密度分布与观测资料的直方图在有效波高的整个范围内符合较好,风速的直方图与Weibul概率密度分布符合较好。同时,分析了有效波高大于4 m的巨浪在东中国海的时空分布特征,表明巨浪多出现在冬、秋两季,平均有效波高最大值出现在夏季,且主要分布在东中国海东南部。     

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.     

基于TerraSAR-X卫星数据的内孤立波参数和海表流速信息提取的探索研究

本文利用TerraSAR-X(TSX)卫星于2010年4月22日在南海东沙岛附近海域获取的数据进行海洋内孤立波动力要素和海表流速信息的提取研究。基于TSX数据的后向散射强度信息,利用经验模态分解法得到内孤立波半波宽度,再利用两层模型法和参数化法计算得到内孤立波振幅和相速度。反演结果显示,利用参数化方法得到的振幅(约21～39 m)和两层模型法得到的相速度(约1.07 m/s)与历史实测资料较为一致。进而利用TSX的顺轨干涉数据获取研究海域内的多普勒速度,再分别采用M4S模型法和直接分离法处理,进而提取海表流速。结果显示,两种方法得到的海表流速的全场平均值较为一致,均为1.10 m/s左右。M4S模型法对流速最大值的改变量较大而直接分离法对流速最小值的改变量较大。M4S模型对内孤立波波峰线区域海表流速的修正大于无内孤立波的海域。最后,基于KdV方程计算得到内孤立波引起的表面流的流速约为0.28 m/s,对反演出的海表流速贡献占比23%。