基于PSO-RF的GNSS-IR土壤湿度反演方法研究

基于全球导航卫星系统干涉测量(Global Navigation Satellite System Interferometry and Reflectometry,GNSS-IR)的土壤湿度监测弥补了传统测量方法的不足,是近年来遥感领域研究的热点.针对以往研究多采用全球定位系统(Global Positioning ...     

微扰法结合最小二乘支持向量机反演土壤湿度

将最小二乘支持向量回归技术应用到土壤湿度反演研究.利用微扰法数值模拟不同雷达参数下裸露土壤微波后向散射特性.经过数据敏感性分析,选取雷达频率为L波段(1.4 GHz),双入射角(40°、50°),并设计多种反演方案,分别以单极化、双极化及同极化后向散射系数比值作为微波信号样本信息,经过适当的训练,利用最小二乘支持向量回归技术对土壤含水量进行了反演研究.结果表明:当采用多入射角、同极化后向散射系数比值作为微波信号样本信息时,反演结果具有较高的精度.同时,经过与人工神经网络结果比较,证明了该方法的有效性及抗噪声能力,为土壤湿度的实时反演研究提供了一种新方法.     

基于天宫二号多光谱数据的青藏高原冻湖自动提取

高原湖泊在反映全球气候变化背景下区域自然环境变迁方面具有重要意义。以新型国产遥感数据源天宫二号多光谱数据为基础采用面向对象方法,结合水体指数和高程信息,提出一种面向冻湖的自动提取算法。该算法充分考虑了不同形态的水体特性,可以同时提取结冰和未结冰的湖泊,并能够排除冰川、河流的影响。针对选定的7个典型区域,采用自动提取算法进行湖泊提取试验,并进行精度验证。湖泊提取整体精度达99.10%,F-score为0.982。结果表明:天宫二号多光谱数据在高原湖泊提取方面具有较强应用潜力,该数据作为一种有效的数据源,可推广用于青藏高原地区湖泊提取与变化研究,为研究区域气候变化提供数据支持。     

钟神“美神二号”书架式音箱

钟神公司新推出的这款“美神二号”表面采用优质木皮制作，造型呈优美的流线型曲面；内有抑制箱体共振的加强筋，可减少驻波和谐波失真。箱体材料采用20mm高密度音箱板，选取发烧级丹麦威发单元，高音为天然绢膜材料，无氧铜线圈绕制，磁液冷却，音质柔和，有很好自然表现力。低音单元具有良好的阻尼，优秀的解析力和细节，平坦的响应，效率高，声压级大，互调失真低。     

爱浪“水晶二号”家庭影院套装

爱浪新品“水晶二号”套装包括两只主音箱、一只中置音箱、两只环绕音箱及爱浪X6多声道放大器。主音箱为钢琴漆外饰，且有珍珠白、挪威红、金刚黑三种颜色可选。主音箱的超高频单元最有特色，采用了独家的“360度宽频发声蓝点声学系统”，     

“北斗二号”卫星导航系统覆盖仿真分析

论述了“北斗一号”卫星导航系统存在的不足,对于“北斗二号”区域卫星导航系统及其所采用的三种导航卫星进行了介绍。通过对所选区域及六个有代表性城市的几何精度衰减因子（GDOP）和可见星数进行仿真,验证了系统的可用性。同时,仿真表明系统对于我国部分地区的覆盖相对较差,部分时间段内的GDOP波动较大,说明系统中卫星的相对位置还应进行调整。     

“星光移动二号”，让数字多媒体完美呈现

消费者对未来手机的需求已经越来越注重于多媒体功能，仅仅提供语音功能的手机已经是昨日黄花了。手机将是一个集成了声音、图像和连接的综合平台。2005年6月底，中国在IT通信核心技术领域再次取得突破，长期致力于数字多媒体芯片开发的中星微电子有限公司推出功能强大的“星光移动二号”多媒体芯片，成功实现了音视频一体化，为当前的热门应用——无线音乐和手机动画业务提供可靠的硬件芯片级解决方案。     

飞达“Fidek）“征服一号”家庭影院

飞达音响公司是有着数十年生产音响产品历史的老厂家了,他们新推出的这套家庭影院系统有一个非常神气的名字:“征服一号”,让人想起歌手那英那首出名的流行歌曲:《征服》。全套系统包括有落地式主音箱一对、环绕音箱一对、中置音箱一只、多声道放大器一台和多功能解码器一台。名字归名字,究竟它的实际表现能否“征服”挑剔的音响消费者们呢?让我们来详细考察一下。     

On current limits of soil moisture retrieval from ERS-SAR data

Assesses the feasibility of retrieving soil moisture content over smooth bare-soil fields using European Remote Sensing synthetic aperture radar (ERS-SAR) data. The roughness conditions considered in this study correspond to those observed in agricultural fields at the time of sowing. Within this context, the retrieval possibilities of a single-parameter ERS-SAR configuration is assessed using appropriately trained neural networks. Three sources of error affecting soil moisture retrieval (inversion, measurement, and model errors) are identified, and their relative influence on retrieval performance is assessed using synthetic datasets as well as a large pan-European database of ground and ERS-1 and ERS-2 measurements. The results from this study indicate that no more than two soil moisture classes can reliably be distinguished using the ERS configuration, even for the restricted roughness range considered.     

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.     

基于植被供水指数的旱区土壤湿度反演方法研究

植被供水指数(VSWI)是进行干旱研究的有效指标,是进行区域土壤湿度反演的重要方法。利用MODIS数据,提取归一化植被指数(NDVI)、修正的土壤调整植被指数(MSAVI)、增强型植被指数(EVI)和地表温度(Ts)等参数,建立植被供水指数、基于MSAVI的植被供水指数(VSWI-M)、基于EVI的植被供水指数(VSWI-E),并对比三种指数反演土壤湿度的效果;在此基础上,建立分区域、基于NDVI阈值的混合植被供水指数(MVSWI)模型,利用20 cm土壤墒情实测数据对模型进行检验,RE,RMSE误差结果显示,MVSWI模型具有较好的精度,可以用来估算土壤湿度。     

A sensitivity analysis of soil moisture retrieval from the tau-omega microwave emission model

The potential of the /spl tau/--/spl omega/ model for retrieving the volumetric moisture content of bare and vegetated soil from dual-polarization passive microwave data acquired at single and multiple angles is tested. Measurement error and several additional sources of uncertainty will affect the theoretical retrieval accuracy. These include uncertainty in the soil temperature, the vegetation structure, and consequently its microwave single-scattering albedo, and uncertainty in soil microwave emissivity based on its roughness. To test the effects of these uncertainties for simple homogeneous scenes, we attempt to retrieve soil moisture from a number of simulated microwave brightness temperature datasets generated using the /spl tau/--/spl omega/ model. The uncertainties for each influence are estimated and applied to curves generated for typical scenarios, and an inverse model used to retrieve the soil moisture content, vegetation optical depth, and soil temperature. The effect of each influence on the theoretical soil moisture retrieval limit is explored, the likelihood of each sensor configuration meeting user requirements is assessed, and the most effective means of improving moisture retrieval indicated.     

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.     

基于双谱模型的被动微波遥感土壤湿度反演

利用随机粗糙表面的双谱散射模型计算了粗糙裸土壤表面的发射率,并建立了基于双谱模型的被动微波遥感的土壤湿度的人工神经网络反演方法.结果表明,基于双谱散射模型计算粗糙裸土壤表面发射率是可行的,利用人工神经网络反演方法,仅采用两个发射率的数据组合便可以反演出精度较高的土壤湿度值,且反演方法具有一定的抗噪性能.     

Soil moisture retrieval from AMSR-E

The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.     

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。     

Use of radar and optical remotely sensed data for soil moisture retrieval over vegetated areas

This work assesses the possibility of obtaining soil moisture maps of vegetated fields using information derived from radar and optical images. The sensor and field data were acquired during the SMEX'02 experiment. The retrieval was obtained by using a Bayesian approach, where the key point is the evaluation of probability density functions (pdfs) based on the knowledge of soil parameter measurements and of the corresponding remotely sensing data. The purpose is to determine a useful parameterization of vegetation backscattering effects through suitable pdfs to be later used in the inversion algorithm. The correlation coefficients between measured and extracted soil moisture values are R=0.68 for C-band and R=0.60 for L-band. The pdf parameters have been found to be correlated to the vegetation water content estimated from a Landsat image with correlation coefficients of R=0.65 and 0.91 for C- and L-bands, respectively. In consideration of these correlations, a second run of the Bayesian procedure has been performed where the pdf parameters are variable with vegetation water content. This second procedure allows the improvement of inversion results for the L-band. The results derived from the Bayesian approach have also been compared with a classical inversion method that is based on a linear relationship between soil moisture and the backscattering coefficients for horizontal and vertical polarizations.     

An observation system simulation experiment for the impact of landsurface heterogeneity on AMSR-E soil moisture retrieval

Using a high-resolution hydrologic model, a land surface microwave emission model (LSMEM), and an explicit simulation of the orbital and scanning characteristics for the advanced microwave sensing radiometer (AMSR-E), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity on large-scale retrieval and validation of soil moisture products over the U.S. Southern Great Plains using the 6.925 GHz channel on the AMSR-E sensor. Land surface heterogeneity impacts soil moisture products through the presence of nonlinearities in processes represented by the LSMEM, as well as the fundamental inconsistency in spatial scale between gridded soil moisture imagery derived from in situ point-scale sampling, numerical modeling, and microwave remote sensing sources. Results within the 575000 km² Red-Arkansas River basin show that, for surfaces with vegetation water contents below 0.75 kg/m², these two scale effects induce root mean squared errors (RMSEs) of 1.7% volumetric (0.017 cm_water³/cm_soil³ ) into daily 60 km AMSR-E soil moisture products and RMS differences of 3.0% (0.030 cm_water/3cm_soil³ ) into 60 km comparisons of AMSR-E soil moisture products and in situ field-scale measurements of soil moisture sampled on a fixed 25-km grid