Envisat-1双极化雷达数据建模及应用

根据欧空局Envisat-1卫星上ASAR传感器的系统参数和双极化特点，利用AIEM模型模拟，建立了裸露地表同极化后向散射模型和粗糙度参数计算模型。前者把同极化总后向散射系数表达成人射角和两个地表参数（土壤水分和粗糙度）的函数；后者给出了用双极化雷达数据计算粗糙度的方法。把这两个模型结合，用于土壤水分反演，分别用模拟数据和实测数据验证，良好的结果证明了这两个模型的可靠性和实用性。双极化后向散射模型的建立，将为以后PALSAR（日本）和RADARSAT-2（加拿大）多极化雷达数据的应用打下基础。     

地基雷达的微波面散射模型对比与土壤水分反演

为了探究地基合成孔径雷达(c GBSAR)后向散射信号的时空变化规律和研究雷达土壤水分反演的影响因素,在内蒙古闪电河流域的昕元牧场站进行了地基雷达观测试验,本文结合以上观测试验的地基雷达数据进行波段、入射角度、极化通道3个雷达参数以及地表粗糙度参数对雷达的后向散射系数影响的分析,然后利用以上分析结果选择地表微波面散射模型,最后利用选定的地表微波面散射模型构建人工神经网络数据集来反演地表土壤水分。结果表明:(1)在地基雷达视场内,各地表微波面散射模型的模拟结果与地基雷达实测的L波段全极化数据拟合效果最佳的是AIEM-Oh模型。(2)通过对20°—60°范围内的雷达入射角度的AIEM-Oh模型后向散射系数模拟的绝对残差分析发现,雷达入射角为25°、41°和53°时模拟结果最接近雷达实测值。(3)最后通过分析土壤水分反演结果发现,当雷达入射角度为41°时的土壤水分反演精度最高,相关系数R是0.8080,RMSE是0.0385 m~3m~3。本文的结论是雷达后向散射信号受到雷达入射角度和地表粗糙度相互作用的影响,因此通过考虑地表粗糙度来合理的选取雷达入射角能够提高土壤水分的反演精度。     

多频率多极化地表辐射参数化模型

发展了针对对地观测系统被动微波辐射计AMSR-E应用的裸露地表辐射模型。首先，利用1993年法国INRA地面试验数据对AIEM在宽波段高频率和大角度的辐射信号模拟能力进行评价。验证结果表明，AIEM模型模拟值与地面实测数据吻合很好，说明AIEM模型能很好模拟宽波段和大角度的辐射信号。在此基础上，用AIEM模型建立了一个针对AMSR-E传感器参数配置．包含各种地表粗糙度和介电特性的裸露地表辐射模拟数据库。利用AIEM模拟数据和地面实测数据对目前人们使用的半经验地表模型进行了比较和分析，发展了多频率多极化的地表辐射参数化模型——Qp模型。该模型中，地表粗糙度对辐射信号的影响通过粗糙度参数Qp来表示。参数Qp可简单表示为均方根高度与相关长度的比值（s／l）。从Qp模型与AIEM模型模拟的发射率比较结果来看，它们之间的绝对误差很小，不超过10^-3因而，本文发展的参数化模型可用作模拟地表辐射的前向模型，如用于估算AMSR-E传感器的亮温值，同时模型的发展有利于提高人们对辐射机制的理解和认识。     

航天飞机极化干涉雷达数据反演地表植被参数

利用基于极化干涉测量的基本原理和相干散射模型。提出了基于模拟加温-退火算法的极化干涉雷达数据地表植被参数的反演算法，首先，对极化干涉测量的基本原理和一个考虑了地表和植被散射的二层相干散射模型进行了阐述。接着，对模拟退火算法的基本理论和基于模拟加温-退火算法的地表植被参数反演模型进行了论述，最后，利用和田地区1994年10月9日和10日的航天飞机SIR-CL波段单视散射短阵复数据进行了地表植被参数反演的计算，将反演结果与实测数据比较，表明该反演算法能以较好的精度获取地表植被的高度。     

双极化SAR数据反演裸露地表土壤水分

为了较高精度地获取大范围地表土壤水分,提出一种基于双极化合成孔径雷达数据的裸露地表土壤水分反演模型即非线性方程组,通过改进的粒子群算法求解非线性方程组从而得到土壤水分。首先通过AIEM模型数值模拟和回归分析,得到一种新的组合粗糙度,然后模拟分析得到土壤水分与雷达后向散射系数的关系,从而建立雷达后向散射系数与组合粗糙度、土壤水分的经验关系。利用ASAR C波段双极化雷达数据,基于经验关系和改进的粒子群算法即可实现土壤水分的反演。经过黑河流域实测土壤水分数据对模型进行验证,反演结果与实测数据具备良好的相关性(R~2=0.778 6)。与以往同一区域研究成果比较,文中的方法反演精度有所提高,更适用于裸露地表土壤水分反演。     

多参数SAR数据森林应用潜力分析

利用多参数机载全球雷达(GlobeSAR)数据和航天飞机成像雷达(SIR-C/X-SAR)数据,分别在我国南、北方两个试验区进行森林识别与分类,以及蓄积量估测的试验.为了更好地了解雷达后向散射与森林结构特征的关系,分别从雷达图像上提取了后向散射系数和强度,进行森林类型识别效果的分析,以及森林结构参数与雷达后向散射强度的相关分析.结果显示多波段、多极化SAR数据能有效地识别不同类型的森林.雷达的后向散射强度对森林的结构参数,尤其是森林的平均胸径和高度较为敏感,据此对试验区的森林蓄积量进行了估测,并分析了多参数SAR在森林应用中的潜力.     

非均匀植被地表全极化散射的高分辨率雷达图像的模拟

用自然地表非球形散射体全极化一阶散射的Mueller矩阵解，在选定高空间分辨率条件下，计算具三维空间结构与非均匀分布的植被地表的全极化后向散射。用射线跟踪法，按射程计算将散射投影在平面图像对应单元的像素点上，得到自然地表的雷达图像模拟。改变非均匀地表的组成，如树木数目与高度、下垫地表、隐藏目标等，产生各种条件下自然地表景象产生的雷达图像模拟。作为一个对比，模拟图像与机载SAR图像在散射量级以及P，L，C波段多通道散射的物理解释上作了比较。     

利用ASAR图像监测土壤含水量和小麦覆盖度

以高级合成孔径雷达（ASAR）影像数据和地面实测数据为基础，分析了裸土、低覆盖（覆盖度为0．2左右）冬小麦麦地的后向散射与土壤含水量、地表粗糙度及小麦覆盖度之间的关系，探讨了裸土和冬小麦麦地土壤含水量及小麦覆盖度的反演方法。分析结果表明：①裸土后向散射系数受地表粗糙度和土壤质地的综合影响较大，裸土的后向散射和土壤含水量正相关关系未达显著，反演裸土土壤含水量必须考虑这两个因素的影响。②冬小麦麦地两种极化后向散射对土壤含水量和小麦覆盖度的敏感性差异明显。由于小麦植株与土壤的水平同极化后向散射差异较大，水平极化后向散射系数和小麦覆盖度及土壤含水量相关性达到显著；冬小麦麦地的垂直同极化后向散射对土壤含水量较敏感，垂直极化后向散射系数和土壤含水量的相关性达到显著，但与小麦覆盖度的相关性相对较低。据此，利用冬小麦麦地的两个同极化后向散射系数，建立了后向散射系数与土壤含水量和小麦覆盖度之间的关系模型，实现了小麦覆盖度和冬小麦覆盖下的土壤含水量反演。验证结果表明：土壤含水量和小麦覆盖度反演结果与地面调查和测量结果一致。     

基于全极化SAR数据反演鄱阳湖湿地植被生物量

鄱阳湖是中国最大的淡水湖,也是国际重要湿地,对其生物量进行长期、定量研究有助于加深对区域乃至全球碳平衡的认识和理解。探讨了利用全极化Radarsat-2 C波段数据反演鄱阳湖湿地生物量的方法,改进了基于辐射传输理论的植被冠层散射模型,模拟了C波段湿地植被的后向散射特性;应用极化分解技术,增加了神经网络训练数据,并用后向反馈神经网络(BP)算法,反演了鄱阳湖湿地植被生物量。与野外实测生物量比较的结果表明:将改进的植被冠层散射模型和全极化分解得到的后向散射系数引入BP神经网络算法,能够有效降低生物量反演误差;全极化SAR数据在生物量反演中具有广阔的应用前景。     

ENVISAT ASAR 数据用于水稻监测和参数反演

用雷达后向散射模型模拟了水稻生长周期内入射角对雷达后向散射的影响关系。用模拟结果归一化雷达数据的后向散射系数，得到同一入射角下水稻周期内后向散射系数时间序列值。分析了归一化ASAR数据与水稻生物参数的关系，实验结果表明，ASAR数据可以用来估测水稻参数。     

An Effective Model to Retrieve Soil Moisture from L- and C-Band SAR Data

This study investigated an appropriate method for soil moisture retrieval from radar images and coincident ground measurements acquired over bare soil and sparsely vegetated regions. The adopted approach based on a single scattering integral equation method (IEM) was developed to establish the relationship between backscatter coefficient and surface soil parameters including volumetric soil moisture content and surface roughness. The performance of IEM in 0–7.6 cm is better than that in 0–20 cm. Moreover, IEM can simulate correctly the backscatter coefficients only for the root mean square (RMS) height s < 1.5 cm at C-band and s < 2.5 cm at L-band by using an exponential correlation function and for s > 1.5 cm at C-band and s > 2.5 cm at L-band by using Gaussian function. However, due to the difficulties involved in the parameterization of soil surface roughness, the estimated accuracy is not satisfactory for the inversion of IEM. This paper used a combined roughness parameter and Fresnel reflection coefficient to develop an empirical model. Simulations were performed to support experimental results and to highlight soil moisture content and surface roughness effects in different polarizations. Results showed that a good agreement was found between the IEM simulations and the SAR measurements over a wide range of soil moisture and surface roughness characteristics. The model had a significant operational advantage in soil moisture retrieval. The correlation coefficients were 77.03 % at L-band and 81.45 % at C-band with the RMSEs of 0.515 and 0.4996 dB, respectively. Additionally, this work offered insight into the required application accuracy of soil moisture retrieval at a large area of arid regions.     

Formal tilt invariance of the nonlocal curvature approximation and its connection to the integral equation method

Tilt invariance is a stringent constraint that second-order scattering models such as the integral equation method (IEM) should satisfy in order to expand their domain of applicability. Moreover, second-order scattering models must reproduce elementary limits such as the small perturbation method (SPM) and the high-frequency Kirchhoff approximation. Tilt invariance is met if and only if a scattering model yields the same asymptotic limit whether the scattering surface is tilted before or after the limiting process. In particular, the tilted SPM coefficients are well determined by simply tilting the reference frame. If it is tilt invariant, a second-order scattering model will reproduce these tilted coefficients by simply tilting the surface explicitly present in the expression of the scattering model before reducing it to the SPM limit. In this letter, we demonstrate that our nonlocal curvature approximation (NLCA) is formally tilt invariant up to first order in the tilting vector. Satisfying the tilt invariance property can extend the applicability of scattering models to account, for example, for scattering from multiscale surfaces and polarization mixing due to out-of-plane tilting. It is also suggested that replacing the field coefficients of IEM by the curvature kernel of NLCA introduces a promising alternative technique that includes multiple scattering up to double reflections from the rough surface, while remaining analytically compact and formally tilt invariant.     

Influence of surface and vegetation characteristics on C-band radar measurements for soil moisture content

Soil moisture estimation using microwave remote sensing faces challenges of the segregation of influences mainly from roughness and vegetation. Under static surface conditions, it was found that Radarsat C-band SAR shows reasonably good correlation and sensitivity with changing soil moisture. Dynamic surface and vegetation conditions are supposed to result in a substantial reduction in radar sensitivity to soil moisture. A C-band scatterometer system (5.2 GHz) with a multi-polarization and multi-angular configuration was used 12 times to sense the soil moisture over a tall vegetated grass field. A score of vegetation and soil parameters were recorded on every occasion of the experiment. Three radar backscattering models Viz., Integral Equation Model (IEM), an empirical model and a volume scattering model, have been used to predict the backscattering phenomena. The volume scattering model, using the Distorted Born Approximation, is found to predict the backscattering phenomena reasonably well. But the surface scattering models are expectedly found to be inadequate for the purpose. The temporal variation of soil moisture does show good empirical relationship with the observed radar backscattering. But as the vegetation biomass increases, the radar shows higher sensitivity to the vegetation parameters compared to surface characteristics. A sensitivity analysis of the volume scattering model for all the parameters also reveals that the radar is more sensitive to plant parameters under high biomass conditions, particularly vegetation water content, but the sensitivity to surface characteristics, particularly to soil moisture, is also appreciable.     

3维场景植被冠层多波段模拟研究进展

植被冠层辐射散射信号中蕴含了丰富的植被信息,通过构建植被冠层辐射散射模型,可以实现植被结构等生物物理参数的遥感定量反演。迄今为止,可见光/近红外、热红外、微波波段均已发展了大量的模型,这些模型在参数反演方面各具优势,但不同波段的模型又有其自身的局限性。跨波段的联合模拟可以实现模型间的优势互补,进而提高地表参数的反演精度,近年来已有学者专注于可见光/近红外与热红外模型,热红外与微波模型,主被动微波模型,以及可见光/近红外与微波模型的联合模拟和协同反演,但多是两两联合,且主要是基于经验模型或解析模型。基于3维场景的植被冠层辐射散射特性模拟模型可以细致刻画不同组分的结构和空间分布特征,对于由植被结构引起的多次散射和组分比例变化的考虑具有优势。本文主要介绍了3维模拟模型在可见光/近红外、热红外和微波波段,以及跨波段联合模拟方面的研究进展,从模型机理、场景统一、以及组分理化参数的统一的角度,探讨了构建多波段3维模拟系统的可行性,展望了多波段3维模拟模型的发展趋势。     

AIEM模型在积雪散射模拟中的应用

 介绍一种基于一阶辐射传输的积雪散射模型。该模型考虑了积雪覆盖地表微波散射的3种回波分量： 雪层表面散射、下垫面散射以及雪层体散射。对于其中2个面散射分量，文章中应用一种新的面散射模型——AIEM取代原有的IEM模型进行处理。最后，使用Michigan大学的实测数据对改进后模型的模拟结果进行验证，并与改进前的模拟结果进行了对比。     

遥感反演中不确定性信息处理的一种数学方法

在遥感反演中对先验知识的表达和应用方法是多阶段目标反演中急需解决的关键问题。论述一种在遥感反演中处理不确定性信息的一种数学方法 ,引入未确知有理数和盲数的概念和运算方法 ,定量计算反演参数在可能取值区间上反演前后的可信度及其改变量。为遥感反演中先验知识的积累、更新和应用提供定量的方法依据。     

GOCE卫星重力梯度数据反演重力场的滤波器设计与比较分析

地球重力场和海洋环流探测（gravity field and steady-state ocean circulation explorer,GOCE）卫星重力梯度数据有色噪声和低频系统误差的滤波处理是反演高精度地球重力场的一个关键问题。针对GOCE卫星重力梯度数据的滤波处理,基于移动平均（moving average,MA）方法和CPR(circle per revolution)经验参数方法设计了两类低频系统误差滤波器,并分别将这两类滤波器与基于自回归移动平均（auto-regressive and moving average,ARMA）模型设计的有色噪声滤波器组合起来形成级联滤波器。为了分析滤波器处理的实际效果,基于空域最小二乘法采用70 d的GOCE观测数据,并联合重力恢复与气候实验（gravity recovery and climate experiment,GRACE）数据分别反演了224阶次的重力场模型GOGR-MA(MA+ARMA级联滤波)和GOGR-CPR(CPR+ARMA级联滤波)。将反演模型与采用同期数据求解的第一代GOCE系列模型及GOCE和GRACE联合模...     

降雨对Ku波段旋转扇形波束散射计影响

星载散射计测量的归一化后向散射截面是有关海面毛细重力波的大小和方向的函数:散射计的回波强度与海面毛细重力波的振幅成正比;风向对后向散射系数具有调制作用。因此,可以利用散射计的数据,根据地球物理模型反演出具有高精确度、无雨和中低风速条件下的海面风场矢量。然而高达10%的散射计测量数据会受到降雨影响(Nie,etal.,2008),尤其工作在Ku波段的散射计。降雨对海面散射幅度的影响主要包括:(1)降雨对雷达波的衰减和散射;(2)降雨改变海洋水面形态和海面粗糙度。     

The inversion method study of sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data

From repeat pass SIR-C L band polarimetric SAR interferometric data and fully maximum likelihood inversion decomposition model of PolInSAR, a method for sub-canopy soil moisture estimation using repeat pass SIR-C PolInSAR data is proposed. At the same time, the potential and validity of fully maximum likelihood inversion decomposition model of PolInSAR for sub-canopy soil moisture inversion is investigated. Firstly, from the random oriented volume over ground two layer coherent scattering model and the statistical characteristics of Pol-InSAR coherency matrix, the fully maximum likelihood inversion decomposition model is used to reconstruct or recover the surface polarimetric coherency matrix with volume scattering components significantly removed; then, from recovered surface polarimetric coherency matrix, co-HH, VV and cross-HV polarization backscattering coefficient are obtained, and the sub-canopy soil moisture are inverted from Oh and Dihedral scattering model. At last, Compared the inversion result with the field measurement and the climate data of hetan region from 1951 to 2006, the preliminary result indicates that the proposed method based on fully maximum likelihood inversion decomposition model has enough high inversion accuracy, if the new spaceborne or airborne polarimetric SAR interferometric data with synchronously spaceborne or airborne-ground measurement will be acquired, the validity and accuracy of proposed inversion method will be further investigated and validated.