基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演

冬小麦是我国重要粮食作物之一,对冬小麦覆盖地表土壤水分进行监测有助于解决因土壤供水导致的冬小麦歉收和农业用水浪费等问题.为了降低冬小麦覆盖地表土壤水分微波遥感反演过程中冬小麦对雷达后向散射系数的影响,该文基于Sentinel-1携带的合成孔径雷达(SAR)数据和Sentinel-2携带的多光谱成像仪(MSI)数据,结合水云模型,开展冬小麦覆盖地表土壤水分协同反演研究.首先,基于MSI数据,该文定义了一种新的植被指数,即融合植被指数(FVI),用于冬小麦含水量反演;然后,该文发展了一种基于主被动遥感数据的冬小麦覆盖地表土壤水分反演半经验模型,校正冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;最后,以河南省某地冬小麦农田为研究区域,开展归一化水体指数(NDWI)和FVI两种指数与VV,VH,VV/VH 3种极化组合而成的6种反演方式下的土壤水分反演对比实验.结果表明:以FVI为植被指数,能够更好地去除冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;6种反演方式中,FVI与VV/VH组合下的反演效果最优,其决定系数为0.7642,均方根误差为0.0209 cm3/cm3,平均绝对误差为0.0174 cm3/cm3,展示了该文所提土壤水分反演模型的研究价值和应用潜力.     

基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演

冬小麦是我国重要粮食作物之一,对冬小麦覆盖地表土壤水分进行监测有助于解决因土壤供水导致的冬小麦歉收和农业用水浪费等问题。为了降低冬小麦覆盖地表土壤水分微波遥感反演过程中冬小麦对雷达后向散射系数的影响,该文基于Sentinel-1携带的合成孔径雷达(SAR)数据和Sentinel-2携带的多光谱成像仪(MSI)数据,结合水云模型,开展冬小麦覆盖地表土壤水分协同反演研究。首先,基于MSI数据,该文定义了一种新的植被指数,即融合植被指数(FVI),用于冬小麦含水量反演;然后,该文发展了一种基于主被动遥感数据的冬小麦覆盖地表土壤水分反演半经验模型,校正冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;最后,以河南省某地冬小麦农田为研究区域,开展归一化水体指数(NDWI)和FVI两种指数与VV, VH, VV/VH 3种极化组合而成的6种反演方式下的土壤水分反演对比实验。结果表明:以FVI为植被指数,能够更好地去除冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;6种反演方式中,FVI与VV/VH组合下的反演效果最优,其决定系数为0.7642,均方根误差为0.0209 cm3/cm3,平均绝对误差为0.0174 cm3/cm3,展示了该文所提土壤水分反演模型的研究价值和应用潜力。     

基于微波与光学遥感的石漠化地区土壤剖面含水率反演模型研究

土壤水是全球生态系统的重要组成部分,定量遥感估测喀斯特石漠化地区土壤含水率,可为石漠化治理和生态恢复工作提供基础数据和理论支撑.通过Sentinel-1A和Landsat 8影像数据,运用水云模型提取灌木林地和疏林地的土壤后向散射系数,并计算旱地与有林地的TVDI.并结合实测数据,利用拟合分析对不同深度土壤含水率进行建模,从而对土壤含水率进行反演.结果表明VH极化二次曲线模型和VH极化三次曲线模型分别适用于灌木林地0~5 cm和5~10 cm深度的土壤含水率反演,其R~2和RMSE分别为0.87、0.87和4.57%、4.29%.疏林地0~5 cm和5~10 cm深度土壤含水率反演宜选用VH极化指数回归模型和VH极化下的线性回归模型,各模型的R~2与RMSE分别为0.736、0.72和9.77%、11.28%.三次曲线模型和Logistic回归模型分别适用于旱地和有林地的土壤含水率的反演,各模型的R~2与RMSE在0~5 cm深度分别为0.85、0.69和2.88%、4.02%,在5~10 cm分别为0.76、0.23和3.5%、6.37%.     

基于多时相Sentinel-1 SAR数据的喀斯特石漠化区地表土壤水分反演研究

土壤水分是地球表层水循环、能量循环和生物地球化学循环中的重要组成部分,是研究喀斯特石漠化地区生态系统的关键参数。基于多时相的Sentinel-1 SAR数据与Alpha 近似模型构建土壤水分观测方程组,反演喀斯特石漠化地区地表土壤水分并对其时空变化特征及误差影响因素展开分析。研究发现观测周期内区域土壤水分总体变化趋势与降雨量变化趋势高度一致,石漠化地区土壤水分高值与空间异质性程度明显高于非石漠化地区。精度验证结果显示土壤水分反演结果的均方根误差为0.059 cm³/cm³,平均误差为0.026 cm³/cm³,该方法在区域地表土壤水分反演中表现出一定的适用性,分析认为地表土壤因周边的复杂生境条件产生的混合像元问题是导致反演误差的主要影响因素。研究可为利用短时间周期重复遥感观测方法获取复杂山区环境下的土壤水分提供参考,为喀斯特石漠化地区生态系统修复和生态产业发展提供支撑。     

基于金属-半导体-金属结构的Bi2Te3室温高响应率太赫兹探测器

基于二维拓扑绝缘体Bi_2Te_3材料利用微纳工艺制备了金属-拓扑绝缘体-金属(MTM)结构的太赫兹光电探测器.器件在0. 022 THz的响应率可达2×10~3A/W,噪声等效功率(NEP)低于7. 5×10~(-15)W/Hz~(1/2),探测率D~*高于1.62×10~(11)cm·Hz~(1/2)/W;在0. 166 THz的响应率可达281. 6 A/W,NEP低于5. 18×10~(-14)W/Hz~(1/2),D~*高于2. 2×10~(10)cm·Hz~(1/2)/W;在0. 332 THz的响应率可达7. 74 A/W,NEP低于1. 75×10~(-12)W/Hz~(1/2),D~*高于6. 7×10~8cm·Hz~(1/2)/W;同时器件在太赫兹波段具有小的时间常数(7～8μs).该项工作突破了传统光子探测的带间跃迁,实现了可室温工作、高响应率、高速响应以及高灵敏度的太赫兹探测器件.     

基于极化干涉相似性参数的四元素分解

该文针对传统极化合成孔径雷达(PolSAR)分解方法过高估计植被成分的问题,提出一种基于极化干涉相似性参数(PISP)的极化干涉分解方法。利用极化干涉合成孔径雷达(PolInSAR)的3组最优相干散射机制定义的PISP具有对地表散射体空间分布敏感的特性和旋转不变性。该文基于PISP的物理意义对植被模型进行改进,使利用该模型分解相干矩阵得到的不同地物体散射功率具有自适应性。最后利用欧空局(DLR)E-SAR获取的L波段全极化干涉数据验证该分解方法的有效性,实验结果表明,该算法得到的分解结果能有效区分植被和建筑物。     

基于特征选择与GOPCE的舰船检测方法

原始的广义相对最优极化(GOPCE)中,通过最大化目标与背景极化参数融合结果的比值,而求取极化参数的最优系数向量,但没有考虑参数融合结果中目标与杂波的分布。为提高基于极化合成孔径雷达图像的舰船目标检测性能,提出了一种基于极化特征选择及改进 GOPCE的目标检测方法。首先提出了一个改进的优化准则,同时考虑了最大化信杂比与最小化目标与杂波分布的方差。基于改进的优化准则,选择了3个最优极化特征,并求解对应的最优系数向量,获取了目标与背景区分度更大的融合图像。实验结果表明,该方法有效提高了目标的检测性能。     

MISIS结构的电特性和C(V)研究

通过解一维泊松方程,对均匀掺杂的MISIS结构进行模拟,研究了表层硅厚度对该结构中电势分布和载流子浓度分布的影响.模拟结果表明:在加栅压V_G时,表层硅中同时存在一个耗尽区和积累区,在耗尽区和积累区中间至少存在一个电中性点.在表层硅厚度大于相应的最大耗尽层宽度的1.6倍时,表层硅厚度对MISIS结构性质无影响.本文还从理论上推导出厚表层硅情况下的MISIS结构理想C(V)关系,并得到与实验相符合的结果.研究结果表明:C(V)特性对于研究MISIS结构中的参数,具有分析一般MIS结构相同的功能.     

具有极化特征的植被相干散射模型及参数反演

重点研究了具有极化特征的植被相干散射模型及参数反演算法。基于目标分解理论,推导了植被及地表回波的极化干涉相干系数与植被高度、衰减系数、地形相位等参数之间的数学关系,以衰减系数作为极化分量的函数,建立了具有明显极化特征的植被及地表相干散射模型。在此基础上,以全极化干涉相干系数作为输入参量,考虑模型对极化的依赖性,采用三阶段法对植被的物理参量进行反演,获得了地形相位和植被高度。在环境可控的微波暗室内构建了极化干涉半实物合成孔径雷达系统对模型进行实验验证,获取了南洋杉和土壤所构成场景的极化干涉回波数据,实验结果表明:采用该模型在场景的地体幅度比小于-10dB的情况下,植被反演高度与实际高度的误差仅为0.03m,说明了模型的有效性。     

基于RVoG模型的极化干涉SAR最优基线分析

 针对极化干涉SAR体散射体参数估计应用,基于随机体散射体/地表二层(RVoG)模型分析研究了其最优基线问题.在基线去相干、RVoG模型、相位管等相关理论分析基础之上,证明了最优基线的存在性,给出了其定义与计算方法,比较了其与常规干涉SAR最优基线的异同.通过仿真验证并讨论了雷达、成像几何和体散射体参数对最优基线选取的影响.     

Statistical characteristics of simulated radar imagery from baresoil surfaces: effects of surface roughness and soil moisturevariability

The potential of high-resolution radar imagery to estimate various hydrological parameters, such as soil moisture, has long been recognized. Image simulation is one approach to study the interrelationships between the radar response and the underlying ground parameters. In order to perform realistic simulations, the authors incorporated the effects of naturally occurring spatial variability and spatial correlations of those ground parameters that affect the radar response, primarily surface roughness and soil moisture. Surface roughness and soil moisture images were generated for a hypothetical 100×100 m bare soil surface area at 1 m resolution using valid probability distributions and correlation lengths. These values were then used to obtain copolarized radar scattering coefficients at 2 GHz (L band) and 10 GHz (X band) frequencies using appropriate backscatter models, which were then converted to a digital number within 0-255 gray scale in order to generate radar images. The effect of surface roughness variability causes variability in the radar image, which is more apparent under smooth soil conditions. On the other hand, the inherent spatial pattern in soil moisture tends to cause similar patterns in the radar image under rougher soil conditions. The maximum difference between contrast-enhanced mean values of the radar image digital number due to moisture variations occurs at surface roughness values in the 1.5-2.0 cm range     

裸地散射特性分析

本文详细地研究了裸地（农用耕地）的散射特性。根据实验现象提出了新的散射系数与入射角关系模型，与实验数据获得了很好的吻合性。通过分析裸地散射系数的雷达参数（入射角、极化、频率）和地面参数（粗糙度、土壤湿度）的响应特性，得到微波遥感土壤湿度时的最佳工作参数。     

Discrete scatter model for microwave radar and radiometer responseto corn: comparison of theory and data

As part of the Multisensor Aircraft Campaign, MACHYDRO, two microwave sensors, NASA's Airborne Synthetic Aperture Radar (AIRSAR) and Pushbroom Microwave Radiometer (PBMR) collected data over the same corn fields during the summer of 1990. During these flights, measurements were made on the ground of soil moisture and plant parameters. In this paper the measured canopy and soil parameters are used in a discrete scatter model to predict the response of both sensors (radar and radiometer). A distorted Born approximation is used to compute the scattering coefficient for the corn canopy. The backscatter coefficient gives the radar response and the radiometer response is obtained by integrating the bistatic coefficient over all scattering angles above ground. The objective of this analysis is to test the model and, in particular, to determine how well a single set of plant parameters and single model can yield agreement with both the radar and radiometer measurements. The model values are in reasonably good agreement with the measurements at horizontal polarization and reflect observed changes in soil moisture     

Radar measurement of soil moisture content

The effect of soil moisture on the radar backscattering coefficient was investigated by measuring the 4-8 GHz spectral response from two types of bare-soil fields: slightly rough and very rough, in terms of the wavelength. An FM-CW radar system mounted atop a 75-ft truck-mounted boom was used to measure the return at 10 frequency points across the 4-8 GHz band, at 8 different look angles (0degthrough70deg), and for all polarization combinations. A total of 17 sets of data were collected covering the range 4-36 percent soil moisture content by weight. The results indicate that the radar response to soil moisture content is highly dependent on the surface roughness, microwave frequency, and look angle. The response seems to be linear, however, over the range 15-30 percent moisture content for all angles, frequencies, polarizations, and surface conditions.     

Simple algorithm for soil moisture retrieval with co-polarized SAR data

In this paper, an empirical methodology to retrieve bare soil moisture by Synthetic Aperture Radar (SAR) is developed. The model is based on Advanced Integral Equation Model (AIEM). Since AIEM cannot express cross-polarized backscattering coefficients accurately, we propose an empirical model to retrieve soil moisture for bare farmland only with co-polarized SAR data. The soil moisture can be obtained by solving an equation of HH and VV polarized data without any field measurements. Both simulated and real SAR data are used to validate the accuracy of the model. This method is especially effective in a large area where the surface roughness is difficult to be completely measured.     

Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces

A semiempirical polarimetric backscattering model for bare soil surfaces is inverted directly to retrieve both the volumetric soil moisture content M/sub v/ and the rms surface height s from multipolarized radar observations. The rms surface height s and the moisture content M/sub v/ can be read from inversion diagrams using the measurements of the cross-polarized backscattering coefficient /spl sigma//sub vh//sup 0/ and the copolarized ratio p(=/spl sigma//sub hh//sup 0///spl sigma//sub vv//sup 0/). Otherwise, the surface parameters can be estimated simply by solving two equations (/spl sigma//sub vh//sup 0/ and p) in two unknowns (M/sub v/ and s). The inversion technique has been applied to the polarimetric backscattering coefficients measured by ground-based polarimetric scatterometers and the Jet Propulsion Laboratory airborne synthetic aperture radar. A good agreement was observed between the values of surface parameters (the rms height s, roughness parameter ks, and the volumetric soil moisture content M/sub v/) estimated by the inversion technique and those measured in situ.     

Microwave Backscatter Dependence on Surface Roughness, Soil Moisture, And Soil Texture: Part III-Soil Tension

Results are presented of an experimental program to determine the impact of soil texture on radar response to soil moisture present within nonvegetated soil surfaces. These findings extend previous reports which document the experimental relationship between the radar backscattering coefficient ?° and soil moisture for bare soil [1] and soil under crop canopies [2]. In confirmation of previous results [1] and [2], the sensitivity of ?° to surface gravimetric or volumetric soil moisture is shown to be inversely related to clay content of the soil. As a result, gravimetric or volumetric moisture indicators exhibit poor performance in moisture estimation algorithms for complex multitextured soils. However, estimation algorithms incorporating some knowledge of soil water retention as a function of soil matric potential, or tension, display strong correlation with radar response, typically r ? 0.8, and are shown to be relatively independent of soil texture. These findings are shown to be consistent with soil dielectric properties [3]-[5].     

Effects of Varying Soil Moisture Contents And Vegetation Canopies on Microwave Emissions

This paper presents an analysis of radiometric data taken at 21, 2.8, and 1.67 cm during a NASA sponsored flight over agricultural fields in Phoenix, AZ. The objective of the mission was to provide comprehensive information concerning microwave responses due to a broad range of soil moisture contents. Generally, data taken over bare fields agree well with theoretical estimates from a combined multilayer radiative transfer model with simple roughness correction. With the surface moisture content ranging between <5 and >35 percent, the emissivity ranges between >0.9 and ~0.7. The response to soil moisture content at 21 cm is more senstive than that at either 2.8 or 1.67 cm. The vegetation model takes into account both the effect of dielectric coefficient and the volume scattering characteristics of the vegetation layer. At the longer wavelengths (e.g., 21 cm) radiation from soil penetrates through vegetation layers of wheat and alfalfa and provides surface moisture information. However, short wavelength radiation from soil cannot penetrate through vegetation canopies; the volume scattering characteristics of vegetation controls the overall microwave signatures.     

Soil Moisture Retrieval During a Corn Growth Cycle Using L-Band (1.6 GHz) Radar Observations

This paper reports on the retrieval of soil moisture from dual-polarized L-band (1.6 GHz) radar observations acquired at view angles of 15$^{circ}$, 35 $^{circ}$, and 55$^{circ}$ , which were collected during a field campaign covering a corn growth cycle in 2002. The applied soil moisture retrieval algorithm includes a surface roughness and vegetation correction and could potentially be implemented as an operational global soil moisture retrieval algorithm. The surface roughness parameterization is obtained through inversion of the Integral Equation Method (IEM) from dual-polarized (HH and VV) radar observations acquired under nearly bare soil conditions. The vegetation correction is based on the relationship found between the ratio of modeled bare soil scattering contribution and observed backscatter coefficient $(sigma^{rm soil}/sigma^{rm obs})$ and vegetation water content $(W)$. Validation of the retrieval algorithm against ground measurements shows that the top 5-cm soil moisture can be estimated with an accuracy between 0.033 and 0.064 $hbox{cm}^{3}cdothbox{cm}^{-3}$, depending on the view angle and polarization.