Oceanic pycnocline depth retrieval from SAR imagery in the existence of solitary internal waves

Oceanic pycnocline depth is usually inferred from in situ measurements. It is attempted to estimate the depth remotely. As solitary internal waves occur on oceanic pycnocline and propagate along it, it is possible to retrieve the depth indirectly in virtue of the solitary internal waves. A numerical model is presented for retrieving the pycnocline depth from synthetic aperture radar （SAR） images where the solitary internal waves are visible and when ocean waters are fully stratified. This numerical model is constructed by combining the solitary internal wave model and a two-layer ocean model. It is also assumed that the observed groups of solitary internal wave packets on the SAR imagery are generated by local semidiurnal tides. A case study in the East China Sea shows a good agreement with in situ CTD （conductivity-temperature-depth） data.     

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。     

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.     

Separation of ocean features from multifrequency full-polarization SAR imagery

Different ocean features usually appear in synthetic aperture radar (SAR) images simultaneously. This makes the image compli- cated and hard to understand. Because of lower signal-to-noise rate , it is much more difficult to separate different ocean features than to separate different land features. A completely novel method is presented to separate ocean features from multifrequency po- larimetric SAR imagery. AIRSAR data from Jet Propulsion Laboratory (JPL), National Aeronautics and Space Administration (NASA) are used in the case studies and good results are achieved.     

多光谱遥感在长江口水深探测中的应用

利用L andsat-7 ETM 遥感图像反射率和实测水深值之间的相关性,建立了单波段模型、双波段模型、比值模型和多波段模型等4种线性回归模型,以及动量BP人工神经网络水深反演模型,对长江口南港航道水深进行了反演,对比分析了不同方法在长江口水深反演计算中的优劣性,试验表明,神经网络反演模型标准误差最小,精度最高。     

基于WorldView-2遥感影像的龙湾港浅海水深反演

根据遥感水深反演原理,利用海南岛龙湾港的WorldView-2多光谱卫星数据和海图水深资料,通过对水深进行0~2,2~5,5~10,10~15和15~20 m的分区处理、潮汐改正和海图水深数据与相应图像波段反射率值的相关性分析及回归分析,建立了浅海水深线性回归反演模型,开展了浅海水深的实际计算与精度分析。结果表明:对不同水深范围分别建立线性回归模型反演的水深精度要高于未分区建立的模型;分区模型中,多波段模型在0~5 m的反演精度最高,而双波段比值模型在5~20 m的反演精度最高,但是反演水深在最浅处的精度还有待提高。本文方法提取的水深与海图水深数据变化趋势基本相似,可以满足海洋科学研究对大范围浅水水下地形探测的要求。     

星载SAR近岸海上风能遥感研究进展

星载SAR已成为近岸海上风能资源研究的新技术手段之一。本文回顾了SAR近岸海上风能遥感研究进展,特别是国内外星载SAR近岸海上风能遥感探测技术研究、遥感应用研究以及SAR风机尾流遥感探测研究的发展。同时针对星载SAR近岸海上风能遥感研究目前存在的问题和今后有待研究的方向进行了探讨。     

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.     

Ship hull characteristics from surface wake synthetic aperture radar (SAR) imagery

Improved data collection and processing technologies along with the use of high resolution spectral techniques soon will make it possible to obtain estimates of the Kelvin wave amplitude function A(θ), ship speed U, and ship heading α from synthetic aperture radar images of ship wakes. This paper presents a series of methods for deriving additional hull characteristics such as the length L, volume V, and offsets ζ(x,z) from this spectral and surface wave information. The first method estimates the ship length by taking the Fourier transform of the slope amplitude function |kA|. The remaining estimates make use of the hull inversion code developed at the University of Michigan by Wu in 1991. The accuracy of the hull offsets predicted by the code is first determined for various options for solving the linear inversion problem. In this case, both the magnitude and phase of A(θ) are known in addition to the hull draft H. Since the draft is not often known a priori, the accuracy of the code is determined next by predicting the volume of the ship for an approximate though plausible input value of H. Finally, the accuracy of the non-linear inversion problem of obtaining offsets along the entire hull is investigated when only the magnitude but not the phase of A(θ) is known.     

“SZ-4”飞船高度计天线指向角反演方法

利用2003年2月7到8日“SZ-4”高度计的波形数据,经过波形数据预处理(波形为1 s的平均、去除热噪声和波形归一化)后,对海面回波波形后沿进行拟合,计算出了高度计天线的指向角,结果表明,“SZ-4”飞船高度计天线的指向角几乎全部在0.024°以内,天线指向在计算时间内比较稳定.     

Determination of sea ice motion using digital SAR imagery

Using digital SEASAT synthetic aperture radar (SAR) imagery, high-precision densely sampled maps of ice motion have been derived by tracking ice features to determine the small-scale spatial variability of ice deformation. The digital SAR imagery was processed to remove geometric distortions and located on the Earth to an accuracy of about 100 m utilizing an algorithm based on the spacecraft orbital data and the characteristics of the SAR data collection system, independent of attitude information or ground reference points. Radiometric enhancement of the imagery using a variable linear stretch algorithm was performed to remove a system-related gradient and improve the identification of sea ice features. Using ice features common to an overlapping pair of images, vector plots of ice motion were then produced. Examples of ice motion are shown in the marginal ice zone and in the central ice pack where mean displacements of 15.3 km/day and 5.0 km/day were measured, respectively. Considering errors in Earth location with those in feature identification an overall error of 150-200 m in displacement measurements was estimated. The ice motion vector plots indicate a high degree of spatial deformation, demonstrating the potential value of spaceborne SAR data for production of precision large-scale maps of ice displacement with a spatial resolution of ice deformation on scales much less than 100 km.     

Operational oil-slick characterization by SAR imagery and synergistic data

A methodology is proposed for the semiautomatic detection, characterization, and classification of slicks detected in C-band Synthetic Aperture Radar (SAR). For the first detection step, automatic algorithms were tested on Environmental Research Satellite (ERS) and Environmental Satellite (EnviSat) images acquired during the Prestige tanker accident. These tests reveal that simple filter or segmentation methods efficiently detect slicks with high contrasts and simple shapes, while a new and more complex multiscale method is able to detect a wider range of slicks. The characteristics of automatically detected slicks are then combined with meteooceanic data in order to eliminate slicks related to wind anomalies and current fronts. The data suggest that slicks in cold upwelling waters are natural, and confirm that slicks are heavy oils when high sea states are present. This detection-classification methodology is validated with aircraft slick-tracking maps. In most cases, joint SAR and environmental data are sufficient to classify the slicks.     

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.     

Estimate of water inherent optical properties from Secchi depth

This paper suggests a new version of the Secchi disk theory which shows a connection between Secchi depth measurements and inherent optical properties (IOP) of water such as the extinction coefficient, the single scattering albedo, and the backscattering coefficient. Ways around Preisendorfer’s objection to using measurements of the Secchi depth for determining the IOP are proposed. This theory is compared with a marine experiment and its accuracy under different conditions is estimated.     

A fundamental model and efficient inference for SAR ocean imagery

Employing a synthetic aperature radar (SAR) imaging model based on fundamental models of nonlinear hydrodynamics, electromagnetic scattering from a two-scale surface, and SAR imaging of a time-variant scene, the optimal (minimum mean-square error) estimates of the parameters of a sinusoidal, long gravity wave, and the short gravity wave ensemble are found in an efficient recursive form and their performance evaluated, generally by numerical simulation, in a one-dimensional stationary version. An application is made to Seasat-SAR complex imagery.     

A new CFAR ship target detection method in SAR imagery

Many ship target detection methods have been developed since it was verified that ship could be imaged with the space-based SAR systems. Most developed detection methods mostly emphasized ship detection rate but not computation time. By making use of the advantages of the K-distribution CFAR method and two-parameter CFAR method, a new CFAR ship target detection algorithm was proposed. In that new method, we use the K-distribution CFAR method to calculate a global threshold with a certain false-alarm rate. Then the threshold is applied to the whole SAR imagery to determine the possible ship target pixels, and a binary image is given as the preliminary result. Mathematical morphological filter are used to filter the binary image. After that step, we use the two-parameter CFAR method to detect the ship targets. In the step, the local sliding window only works in the possible ship target pixels of the SAR imagery. That step avoids the statistical calculation of the background pixels, so the method proposed can much improve the processing speed. In order to test the new method, two SAR imagery with different background were used, and the detection result shows that that method can work well in different background circumstances with high detection rate. Moreover, a synchronous ship detection experiment was carried out in Qingdao port in October 28, 2005 to verify the new method and one ENVISAT ASAR imagery was acquired to detect ship targets. It can be concluded from the experiment that the new method not only has high detection rate, but also is time-consuming, and is suitable for the operational ship detection system.     

An inverse method for underwater bottom topography by using SAR imagery

I~IOXThe image technology of the synthetic aperture radar (SAR) is one of most important advances in spaceberne microwave remote sensing (Larson et al., 1976). It has extensive application in the ocean remote sensing, such as inversion of the sea subdue winds and currentS, reconstruction of underwater hatom topography and so on. Owing tO the high conductivity of sea water, an electromagnetic wave actually cannot penetrate into the sea water, with extremely smallPenetration depth (abbot 1 c…     

用合成孔径雷达图像反演浅海水下地形的一种方法

海流与海底地形的相互作用导致了海表面的粗糙起伏,从而引起对海面观测的雷达散射截面的变化。在浅海海流速度垂直分布廓线为均匀的假定下,由流体连续性方程和驰豫时间近似下流体力学弱相互作用的理论,已证明雷达散射截面的空间变化正比于海流方向上海流速度变化的梯度。研究了合成孔径雷达(SAR)对海面的观测图像在无槽道信息和海流方向预知条件下,提出用散射系数的二维相关函数确定海流方向。在海流速度和方向已知条件下,推导了由雷达散射截面的空间变化迭推反演浅海水下地形的公式,并研究了航天飞机SIR-CSAR在我香港特区海域的二维图像反演浅海水下地形的应用。     

A Review About SAR Technique for Shallow Water Bathymetry Surveys

Synthetic Aperture Radar(SAR)has become one of the important tools for shallow water bathymetry surveys.This has significant economic efficiency compared with the traditional bathymetry surveys.Numerical models have been developed to simulate shallow water bathymetry SAR images.Inversion of these models makes it possible to assess the water depths from SAR images.In this paper,these numerical models of SAR technique are reviewed,and examples are illustrated including in the coastal areas of China.Some issues about SAR technique available and the research orientation in future are also discussed.     

地转及均匀背景流场对混合层深度SAR遥感反演的影响

以考虑了地转和均匀背景流场影响的两层流体界面内波频散关系模型为基础,得到新的利用SAR遥感图像计算混合层深度的方法。利用该方法对一幅南海北部SAR内波图像进行了实例研究,并且和时空同步的CTD资料进行了对比。结果表明,加入地转和均匀背景流场影响的模型更为合理,为更准确地反演混合层深度奠定了基础。