结合光谱响应函数的Landsat-8影像大气校正研究

针对受大气吸收与散射影响,遥感器得到的测量值与目标物的真实值间存在误差,给反演地表反射率/反照率和地表温度等关键参数带来较大误差,影响图像分析精度的问题,该文利用Landsat-8的光谱响应函数,对OLI多光谱数据进行大气辐射校正和反射率反演,对校正前后的地物光谱曲线和归一化植被指数(Normalized Difference Vegtation Index,NDVI)的变化进行了对比。研究表明:OLI大气校正后较好地恢复各类地物光谱的典型特征;大气校正后NDVI增幅明显;类似的基于光谱响应函数的FLAASH大气校正方法可以为其他的高级陆地成像仪等传感器校正提供依据。     

地形坡面对被动微波遥感影响的试验研究

地表起伏所形成的倾斜表面,特别是在山区,受地形坡度和坡向变化的影响,地表的微波辐射特征较之平坦地表发生明显变化。基于地基微波辐射地形试验,模拟星载被动微波辐射计AMSR\|E的观测参数,通过建立地形坡面的地貌微缩景观进行观测,探索地表斜坡对被动微波辐射特征的影响,用AIEM模型 和 Fresnel 方程分别模拟裸土地形坡面的微波辐射特征。结果表明,倾斜坡面对被动微波辐射的亮度温度产生了10~15 K的偏差,由坡度形成的本地入射角改变了地表的有效发射率,并随坡向的变化发生微波极化旋转。经试验数据和模型模拟结果对比,认为AIEM 在考虑了表面粗糙度影响时可以较好地模拟地形坡面的被动微波辐射特征。     

基于FLAASH的多光谱影像大气校正应用研究

受大气吸收与散射影响,遥感器得到的测量值与目标物的真实值间存在误差,给反演地表反射率/反照率和地表温度等关键参数带来较大误灿跋炝送枷穹治龅木?选择ASTER多光谱数据,利用FLAASH模块进行大气辐射校正和反射率反演.对校正前后的反射辐射和归一化植被指数(NDVI)的变化进行了对比研究,并利用校正前后的NDVI反演植被盖度.研究表明,大气校正后图像计算的植被盖度显著提高.     

Hyperion高光谱遥感数据大气校正方法

由于受到大气的影响,传感器接收到的辐射信息不能真实地反映地表反射光谱信息,因此,从遥感影像中去除大气的影响,即进行大气校正,是高光谱遥感数据处理中极为重要的环节;通过应用大气校正模块FLAASH,研究选择了合适的大气模式、水汽含量、气溶胶模型、波谱分辨率和多散射模型等参数,对内蒙东胜地区Hyperion高光谱遥感影像进行大气校正;比较了校正前后典型地物的光谱曲线,并将它们与实验室典型地物光谱曲线进行对比,大气校正后得到的光谱曲线和实验室得到的光谱曲线具有较好的一致性,达到了去除大气影响的目的,同时校正生成的水汽分布也表明校正效果良好。     

机载倾斜摄影数据在三维建模及单斜片测量中的应用

机载倾斜摄影测量系统是对常规摄影测量系统的改进和发展,它能够获取常规摄影无法得到的地物立面的纹理信息和几何信息,在数字城市构建中具有重要的意义.本文利用机载倾斜摄影数据进行了三维建模和单斜片测量的应用研究与实验,初步实验表明,倾斜摄影数据应用于三维建模单斜片测量是可行的,具有较好的应用前景.     

机载综合孔径辐射计图像和光学图像融合及评价

微波辐射计图像可以很直观的反映地表亮温,但其分辨率不高且图像地物的几何外形不明显等缺点给研究带来诸多不便。现有一些光学图像与之相比,具有分辨率高,几何外形更清晰等优点。如何有效利用光学图像的信息来研究辐射计图像是一件有意义的课题。采用IHS方法、PCA方法、小波方法对机载综合孔径微波辐射计图像和Landsat ETM+图像进行融合。从对实验结果定量和定性的分析表明小波融合方法更具潜力。融合结果中可以看出在多光谱图像中表达相似的地物,可 以很快的在图像中分辨出来。结果图像基本上保持了辐射计图像的辐射特性,并且融合了光学图像的不同地物的光谱信息,为在辐射计图像上对大区域地物的解译和分析带来方便。     

神舟4号飞船多频段微波辐射计及其应用

介绍了我国首次研制的空间对地观测被动微波遥感器--神舟4 号飞船多频段微波辐射计的设备性能参数及指标, 并对海洋、沙漠等地物目标的测量结果进行了分析, 与国外其它星载微波辐射计的测量结果进行了比较。     

机载成像光谱图象边缘辐射畸变校正

由于受大气效应、地物反射非朗伯特性、仪器－太阳－目标相对几何关系以及仪器系统本身特性等多种因素的影响,机载成像光谱数据中不可避免地将引入系统或非系统的辐射畸变,严重地影响了数据表达信息的可靠性和有效性。目前虽然已有一些机载遥感图像辐射畸变校正方法,但由于受各种应用条件的限制,所发展的这些方法普遍缺乏实用性和通用性。该文在对机载成像光谱图象边缘辐射畸变形机制进行理论分析和探讨的基础上,针对机载成像光     

机载AISA Eagle Ⅱ高光谱数据处理——以额济纳旗试验区为例

机载AISA EagleⅡ传感器为"黑河综合遥感联合试验(HiWATER)"额济纳旗试验区提供航空高光谱影像。介绍了高光谱原始数据的辐射定标、几何校正、大气校正等预处理过程。根据研究区地形差异以及数据使用目的的多样性,几何校正中可选择是否加高精度DEM产品,大气校正的选择策略可分为平坦地形无DEM的大气校正和起伏地形添加DEM大气校正。本试验数据采用加载高精度DEM的几何校正和平坦地形大气校正方法,经过预处理后的高光谱数据产品,其地理坐标与高分辨率的CCD影像对比,地理位置信息较为准确;与实测地物光谱对比,影像光谱能较好地体现地物光谱的特性,数据可用作定量遥感进一步的研究。     

基于多时相光谱和物候特征的陕西省神木县地物遥感分类研究

开展了时间序列Landsat TM/ETM遥感影像定量化处理与相对辐射校正,提取了陕西神木县不同地物光谱和NDVI物候特征,结合时间序列NDVI物候特征和多时相光谱信息,采用了地表覆盖的决策树分类算法,实现了陕西神木县地物的高精度遥感分类,包括水体、沙地、城镇、耕地、林地、草地及灌丛等7类地物,分类总体精度达95.77%,Kappa系数达0.93。研究结果表明,基于多时相光谱和物候特征的决策树分类算法能够有效集成多时相、多光谱信息,从而克服了单时相影像分类的缺陷,实现了地物的分类。论文研究方法和结果能够为三北防护林区域的生态环境监测与评估提供技术支持。     

GNSS-R SAR地面目标建模与成像

针对GNSS-R SAR对地成像仿真中,由于地面目标建模以目标复杂的实地勘测DEM（Digital Elevation Model）为基础而导致的典型目标影像特征不利于单独观察的问题,基于蒙特卡罗方法对随机地表建模,基于欧几里德原理对建筑和树木建模,构建结构灵活可控的成像场景。利用导航卫星作为辐射源,机载接收机作为信号接收平台,成像场景反射的GNSS-R信号作为回波信号,BP算法作为成像处理算法,进行成像仿真。仿真结果表明,该仿真场景下生成的图像可反映不同地面目标的结构特点和散射强度差异,证明了所构建的地面目标模型能够应用于GNSS-R SAR成像系统的仿真验证。     

机载综合光电系统的应用与发展

作战飞机微波隐身和电子战技术的不断进步,使得机载火控雷达的探测距离和抗干扰能力逐步下降,且雷达本身采用有源探测方式,工作时主动发射电磁波易被敌方发现、干扰和攻击。为弥补机载雷达的不足,体积小、重量轻、功耗小、角分辨率高、抗电磁干扰能力强、隐蔽性好,且以被动方式工作的机载综合光电系统不断发展起来。从现有光电探测系统的技术基础和任务需求出发,分析了机载红外搜索与跟踪系统(IRST)和光电分布式孔径系统(EODAS)等机载光电对抗系统的应用、功能、特点、装备现状及发展,并对新一代机载综合光电系统的要求及技术发展趋势进行了预测。     

面向遥感叶面积指数产品的地形校正研究

地形校正是提高复杂地形区地表参数遥感定量化反演精度的重要手段。当前广泛应用的遥感叶面积指数产品（Leaf Area Index, LAI）多具有一定的地形误差,减少地形影响、提升其产品精度有着非常重要的意义。以我国江西省千烟洲地区为研究区域,利用地面实测LAI数据、LandsatTM数据和高程数据等,基于高程标准差和GLOBMAP LAI产品值的关系,建立面向叶面积指数产品的地形校正模型,利用这一模型对GLOBMAP LAI产品进行地形校正。结果表明：校正后的LAI与地面实测数据更为接近,LAI产品与地面测量值的RMSE由2.11下降到2.04;校正后LAI产品的标准差由2.08下降至1.69,LAI产品的地形误差得到了较好的改正。该方法较好地完成了LAI产品的地形校正,进一步提高了产品精度,具有一定的实用价值。     

基于行频变化的航空高光谱成像仪相对辐射校正方法研究

对于核心部件为线阵CCD阵列的航空高光谱成像仪而言,积分时间因随飞行过程中成像行频动态变化而变化,导致影像中各行数据记录的地物灰度值不具备可比性,更使得相邻航带可能出现明显色差。为此,发展了一种基于行频变化的相对辐射校正方法,在传统的相对辐射校正模型中引入了行频的影响,实现了反映行频动态变化的航空高光谱数据辐射校正三维曲面模型。通过无人机载高光谱成像仪的航空飞行试验,利用所获取的验证场的图像进行相对辐射校正处理,通过相邻航带间图像处理前后色差比较、验证场布设均匀靶标的定量化。     

基于Sentinel-1及 Landsat 8数据的黑河中游农田土壤水分估算

土壤水分是陆地表层系统中的关键变量。利用主动微波遥感,特别是合成孔径雷达(Synthetic Aperture Radar,SAR)的观测,在监测和估计表层土壤水分时空分布方面已开展了诸多研究。然而,SAR土壤水分反演仍存在诸多挑战,特别是地表粗糙度和植被的影响。因此,本文提出了一种结合主动微波和光学遥感的优化估计方案,旨在同步反演植被含水量、地表粗糙度和土壤水分。反演算法首先在水云模型的框架下对模型中的植被透过率因子(与植被含水量密切相关)采用3种不同的光学遥感指数——修正的土壤调节植被指数(Modified Soil Adjusted Vegetation Index,MSAVI)、归一化植被指数(Normalized Difference Vegetation Index,NDVI)和归一化水体指数(Normalized Difference Water Index,NDWI)进行参数化估计,用于校正植被层的散射贡献。在此基础上,构造基于SAR观测和Oh模型的代价函数,利用复型洗牌全局优化算法进行土壤水分和地表粗糙度的联合反演。采用Sentinel-1 SAR和Landsat 8多光谱数据在黑河中游开展了反演试验,并利用相应的地面观测数据对结果进行了验证。结果表明反演结果与地面观测具有良好的一致性,其中基于NDWI的植被含水量反演效果最佳,与地面观测比较,土壤水分决定系数(R 2)在0.7以上,均方根误差(RMSE)为0.073 m^ 3/m^ 3;植被含水量R 2大于0.9,RMSE为0.885 kg/m 2,表明该方法能够较准确地估计土壤水分。同时发现植被含水量的估计结果,以及植被透过率的参数化方案对土壤水分的反演精度有一定的影响,在未来的研究中需要进一步探索。     

空间遥感现状及未来发展

从第三十五届国际宇航联合会的空同遥感专业小组会议上可以看出,目前空间遥感的现状及未来发展前景。今后空间遥感将从具有单一遥感能力向具有综合遥感能力方面发展,不仅能对陆地,而且对海     

Soil moisture retrievals at L-band using a two-step inversion approach (COSMOS/NAFE'05 Experiment)

COSMOS (Campaign for validating the Operation of Soil Moisture and Ocean Salinity), and NAFE (National Airborne Field Experiment) were two airborne campaigns held in the Goulburn River catchment (Australia) at the end of 2005. These airborne measurements are being used as benchmark data sets for validating the SMOS (Soil Moisture and Ocean Salinity) ground segment processor over prairies and crops. This paper presents results of soil moisture inversions and brightness temperature simulations at different resolutions from dual-polarisation and multi-angular L-band (1.4 GHz) measurements obtained from two independent radiometers. The aim of the paper is to provide a method that could overcome the limitations of unknown surface roughness for soil moisture retrievals from L-band data. For that purpose, a two-step approach is proposed for areas with low to moderate vegetation. Firstly, a two-parameter inversion of surface roughness and optical depth is used to obtain a roughness correction dependent on land use only. This step is conducted over small areas with known soil moisture. Such roughness correction is then used in the second step, where soil moisture and optical depth are retrieved over larger areas including mixed pixels. This approach produces soil moisture retrievals with root mean square errors between 0.034 m³ m^− 3 and 0.054 m³ m^− 3 over crops, prairies, and mixtures of these two land uses at different resolutions.     

Analysis of the applicability of multi-temporal full polarimetric airborne L-band SAR scattering to paddy rice field mapping

ABSTRACT

A Synthetic Aperture Radar (SAR) is an all-weather imaging system that is often used for mapping paddy rice fields and estimating the area. Fully polarimetric SAR is used to detect the microwave scattering property. In this study, a simple threshold analysis of fully polarimetric L-band SAR data was conducted to distinguish paddy rice fields from soybean and other fields. We analysed a set of ten airborne SAR L-band 2 (Pi-SAR-L2) images obtained during the paddy rice growing season (in June, August, and September) from 2012 to 2014 using polarimetric decomposition. Vector data for agricultural land use areas were overlaid on the analysed images and the mean value for each agricultural parcel computed. By quantitatively comparing our data with a reference dataset generated from optical sensor images, effective polarimetric parameters and the ideal observation season were revealed. Double bounce scattering and surface scattering component ratios, derived using a four-component decomposition algorithm, were key to extracting paddy rice fields when the plant stems are vertical with respect to the ground. The alpha angle was also an effective factor for extracting rice fields from an agricultural area. The data obtained during August show maximum agreement with the reference dataset of estimated paddy rice field areas.     

Improving Soil Moisture Estimation by Joint Assimilationof MODIS Land Surface Temperature and Airborne L band Microwave Brightness Temperature

In this work,a novel soil moisture data assimilation scheme was developed,which was based land surface model (CoLM,Common Land Model),microwave radioactive transfer model (L MEB,L band Microwave Emission of the Biosphere),and data assimilation algorithm (EnKS,Ensemble Kalman Smoother).This scheme is used to improve the estimation of soil moisture profile by jointly assimilatingMODIS land surface temperature and airborne L band passive microwave brightness temperature.The ground based data observed at DAMAN superstation,which is located at Yingke oasis desert area in the middle stream of the Heihe River Basin,are used to conduct this experiment and validate assimilation results.Three LAI products are used to analyze the influence of LAI on soil temperature.Three assimilation experiments are also designed in this work,including assimilation of MODIS LST,assimilation of microwave brightness temperature,and assimilation of MODIS LST and microwave brightness temperature.The results show that the uncertainties in LAI influence significantly soil temperature simulations in different soil layers.MODIS LAI product is seriously underestimated in this study area,which results soil temperature overestimation about 4~6 K.Three assimilation schemes can improve soil moisture estimations to different extend.Joint assimilation of MODIS LST and microwave brightness temperature achieved the best performance,which can reduce the RMSE of soil moisture to 31%~53%.     

Temporal and spatial soil moisture change pattern detection in an agricultural area using multi‐temporal Radarsat ScanSAR data

Monitoring the characteristics of spatially and temporally distributed soil moisture is important to the study of hydrology and climatology for understanding and calculating the surface water balance. The major difficulties in retrieving soil moisture with Synthetic Aperture Radar (SAR) measurements are due to the effects of surface roughness and vegetation cover. In this study we demonstrate a technique to estimate the relative soil moisture change by using multi‐temporal C band HH polarized Radarsat ScanSAR data. This technique includes two components. The first is to minimize the effects of surface roughness by using two microwave radar measurements with different incidence angles for estimation of the relative soil moisture change defined as the ratio between two soil volumetric moistures. This was done by the development of a semi‐empirical backscattering model using a database that simulated the Advanced Integral Equation Model for a wide range of soil moisture and surface roughness conditions to characterize the surface roughness effects at different incidence angles. The second is to reduce the effects of vegetation cover on radar measurements by using a semi‐empirical vegetation model and the measurements obtained from the optical sensors (Landsat TM and AVHRR). The vegetation correction was performed based on a first‐order semi‐empirical backscattering vegetation model with the vegetation water content information obtained from the optical sensors as the input. For the validation of this newly developed technique, we compared experimental data obtained from the Southern Great Plain Soil Moisture Experiment in 1997 (SGP97) with our estimations. Comparison with the ground soil moisture measurements showed a good agreement for predication of the relative soil moisture change, in terms of ratio, with a Root Mean Square Error (RMSE) of 1.14. The spatially distributed maps of the relative soil moisture change derived from Radarsat data were also compared with those derived from the airborne passive microwave radiometer ESTAR. The maps of the spatial characteristics of the relative soil moisture change showed comparable results.