星载NO_(2)探测发展及对流层柱浓度产品精度分析北大核心CSCD

NO_(2)作为大气中重要的痕量气体之一,是衡量大气污染状况的重要风向标。与传统地基观测相比,星载传感器能够提供大范围、长时间序列的观测资料,利用遥感卫星数据反演获取对流层NO_(2)浓度已成为近些年研究的热点之一。首先介绍了国内外星载紫外高光谱传感器的发展。然后从原理方面对国际上通用的对流层NO_(2)垂直柱浓度反演算法进行阐述。之后介绍了各官方对流层NO_(2)柱浓度产品的反演流程及产品精度,并比较了各产品DOAS算法的区别。可见,由于传感器所获取数据的时间、空间、光谱分辨率越来越高,NO_(2)柱浓度产品的反演模型及算法的选择更加合理,因此NO_(2)产品精度也更高。     

大气探测傅里叶变换光谱仪

高精度的大气红外光谱遥感是实现数值天气预报、进行环境监测等应用的关键技术。论述了星载大气探测傅里叶变换光谱系统在完成这一高精度遥感测量中的重要地位及其发展与现状,从应用的角度提出了大气探测傅里叶变换光谱仪器光谱波段、分辨率等技术指标的要求,讨论了仪器的总体设计方案,提出了成功地进行大气傅里叶光谱探测所必须解决的关键技术。     

基于Hyperion影像的高光谱水汽含量反演的算法比较研究

基于高光谱遥感图像数据的大气参数反演和一体化辐射校正具有重要研究意义和应用价值。首先,通过6S模型辐射传输计算分析了EO-1/Hyperion遥感影像在940和1 130nm附近水汽吸收区域的光谱吸收特点。其次,采用两通道比值法和三通道比值法,比较了不同波段组合的大气含水量高光谱遥感反演精度并进行了敏感性分析,模拟实验结果表明采用三波段比值算法的相关系数和均方根误差均优于对应的两波段算法。最后,利用张掖地区2008年3景EO-1Hyperion高光谱遥感影像,反演了大气含水量,并与地基CE-318太阳分光光度计测量数据进行对比验证,结果表明:1 124nm水汽吸收通道反演精度优于940nm,两通道和三通道比值法的均方根误差分别为0.369和0.128g/cm2,三通道比值方法优于两通道比值方法,与地面观测结果一致。     

高光谱场景辐亮度模型仿真研究

高光谱遥感的地面场景是高光谱遥感系统中影响因素最复杂多变的部分。首先基于星载高光谱遥感成像的辐射传输过程,对非均匀的朗伯表面的入瞳处大气辐亮度传输模型进行了研究,得到只需要考虑目标与邻近像元反射率,大气传输因子的辐亮度简化模型。之后介绍了大气中光子扩散原理,并采用蒙特卡洛方法对大气点扩散函数进行仿真;联合地表目标像元反射率数据计算得到基于非均匀朗伯面地表的邻近像元反射率;然后总结了大气传输模型软件MODTRAN计算入瞳处辐亮度数据的原理步骤,并利用其反演了朗伯表面的相关大气传输参数。最终利用基于传感器入瞳处的辐亮度数据表征了高光谱地面场景。     

卫星红外高光谱反演大气水汽含量

利用高光谱大气红外探测仪AIRS模拟及观测数据,发展基于主成分分析技术的多层前馈神经网络反演算法,进行大气中水汽柱总量(IWV)的反演计算、模拟及实测验证。首先,基于全球晴空大气廓线训练样本SeeBorV4.0,利用快速辐射传输模式CRTM进行了辐射传输模拟计算,得到全球高光谱分辨率模拟辐亮度;其次,利用主成分分析技术对模式模拟和AIRS实测高光谱数据进行降维、去噪及去相关处理,并采用多层前向神经网络算法反演大气水汽柱总量;最后,利用数值试验、AIRS实测L1B数据及其水汽产品,对反演算法进行了验证。通过与AIRS官方大气产品的统计分析,本算法反演均方根误差为0.387 g/cm²,最大偏差为0.82 g/cm²,空间分辨率保留了AIRS像素原分辨率(比AIRS官方大气产品高3倍)。     

卫星遥感大气CO2的技术与方法进展综述

综合分析了国际上卫星遥感观测大气二氧化碳（CO²）含量的主要技术与方法,讨论了现有星载大气CO²探测器（传感器）的主要理论基础、反演方法,归纳了仪器的主要性能指标、观测方式和观测目标,分析了影响CO²遥感精度的主要因素。具体研究下列3类星载CO2探测器：① 技术相对成熟的、观测要素既包含CO²也包含其他微量气体的综合性星载被动探测仪器,例如大气红外垂直探测仪(AIRS)、大气制图扫描成像吸收光谱仪(SCIAMACHY)、超高光谱红外大气探测干涉仪(IASI);② 针对大气中CO²混合比或者对流层下层CO²含量进行专门观测的星载被动探测器：极轨碳观测卫星(Orbiting Carbon Observatory,OCO)和温室气体观测卫星(Greenhouse gas Observing Satellite,GOSAT)搭载的被动红外探测器;③ ASCENDS和A\|SCOPE等国际卫星计划正在研制中的星载主动激光雷达探测器。 进一步介绍了我国在高光谱仪器研制方面具备的研究基础。最后初步分析了星载CO²探测结果的验证、资料同化方法和未来的发展趋势。     

基于OMI数据的兰州及附近地区大气边界层SO2量值变化初步分析

环境遥感是遥感技术应用的重要领域。随着高光谱遥感技术和定量遥感理论的发展,利用OMI、MODIS等数据开展大气SO²分布的研究得到关注。OMI是新一代大气成分探测传感器,其痕量数据产品OMSO²在反演大气SO²量值中有较好的效果。选取2005年2、5、8、11月及2005~2008年各年2月每日的OMI Level-2痕量数据,采用遥感和GIS方法,对兰州及附近地区大气边界层SO²空间分布、季节变化和冬季年际变化进行了分析。结果表明:研究区内SO²量值呈离散面状分布,兰州-白银、金昌是两个明显的高值区;冬季SO²量值明显高于其他季节,年际变化波动比较明显。研究表明,利用OMI Level\|2痕量数据产品,能够对较大尺度范围人类活动排放SO²状况进行检测和评估。     

热红外遥感地表温度反演研究现状与发展趋势

从Planck函数和热红外辐射传输方程出发,概述了热红外遥感反演地表温度的基本原理,总结了当前反演地表温度常用到的热红外遥感器及相应波段。将热红外遥感地表温度反演算法分为单通道、劈窗和多通道3大类,分析了每一类中较具代表性算法的原理、适用条件及精确度。从热红外遥感机理、发射率、环境辐射、混合像元、大气影响等方面概述了热红外遥感反演地表温度面临的主要问题,并对热红外遥感地表温度反演的发展趋势进行了展望。     

利用Landsat-8 OLI反演大气气溶胶的可见光谱段地表反射率估算

准确估算地表反射率的贡献一直是遥感反演大气气溶胶光学厚度过程中的重点和难点。为了促进Landsat-8OLI传感器在地表参数定量化特别是大气遥感领域的应用,本文提出一种利用OLI 1.6μm、2.2μm短波红外谱段数据估算遥感影像可见光地表反射率的方法。该方法依托于MOD04产品地表反射率估算模式,通过光谱归一化和构建新的短波红外植被指数等过程,建立OLI地表反射率估算模式,通过误差分析发现该模式能够有效地降低由于传感器光谱响应不同对估算结果的影响,对应用在OLI遥感影像的计算结果与同时间同区域MOD04产品地表反射率进行比较,表明其结果有较高的相关性和可靠性。     

机载高光谱数据提取冬小麦冠层叶绿素含量的模型分析及验证

遥感提取叶绿素含量的方法是精准农业的重要研究方向之一,但是如何用冠层光谱数据有效地提取叶绿素含量仍然是一个难点。本文用光谱指数TCARI和OSAVI的组合建立提取冬小麦冠层叶绿素含量的关系式,并使用实验田获取的冬小麦冠层光谱以及与之同步的机载高光谱传感器OMIS数据进行了验证。通过误差分析讨论了该方法用于遥感高光谱数据时需要注意的问题,表明大气校正的精度,传感器的信噪比以及波段中心的漂移是模型反演精度的主要制约因素。     

基于波谱匹配的Hyperion数据大气校正方法对比研究

遥感影像数据的大气校正是高光谱遥感地空对比、信息提取的前提和关键,如何根据不同数据、不同研究区、不同研究目的选择合适的大气校正方法是高光谱遥感应用研究的重点和难点。针对EO\|1卫星Hyperion高光谱遥感数据特点和研究区地形环境特征,分别选择线性回归经验模型、基于MODTRAN4模型的FLAASH和基于DEM数据的ACORN\|3模型不同大气校正方法对研究区Hyperion数据进行大气校正。从波谱匹配、识别的目的出发,通过计算不同方法校正后影像像元的波谱曲线与实测地面波谱曲线的匹配程度分析不同大气校正方法的校正效果。     

Coupled soil-leaf-canopy and atmosphere radiative transfer modeling to simulate hyperspectral multi-angular surface reflectance and TOA radiance data

Coupling radiative transfer models for the soil background and vegetation canopy layers is facilitated by means of the four-stream flux interaction concept and use of the adding method. Also the coupling to a state-of-the-art atmospheric radiative transfer model like MODTRAN4 can be established in this way, thus enabling the realistic simulation of top-of-atmosphere radiances detected by space-borne remote sensing instruments. Possible applications of coupled modeling vary from mission design to parameter retrieval and data assimilation. This paper introduces a modified Hapke soil BRDF model, a robust version of the PROSPECT leaf model, and a modernized canopy radiative transfer model called 4SAIL2. The latter is a hybrid two-layer version of SAIL accommodating horizontal and vertical heterogeneities, featuring improved modeling of the hot spot effect and output of canopy absorptances. The integrated model is simply called SLC (soil-leaf-canopy) and has been implemented as a speed-optimized Windows DLL which allows efficient use of computer resources even when simulating massive amounts of hyperspectral multi-angular observations. In this paper various examples of possible model output are shown, including simulated satellite image products. First validation results have been obtained from atmospherically corrected hyperspectral multi-angular CHRIS-PROBA data of the Upper Rhine Valley in Germany.     

Spectral discrimination of vegetation types in a coastal wetland

Remote sensing is an important tool for mapping and monitoring vegetation. Advances in sensor technology continually improve the information content of imagery for airborne, as well as space-borne, systems. This paper investigates whether vegetation associations can be differentiated using hyperspectral reflectance in the visible to shortwave infrared spectral range, and how well species can be separated based on their spectra. For this purpose, the field reflectance spectra of 27 saltmarsh vegetation types of the Dutch Waddenzee wetland were analysed in three steps. Prior to analysis, the spectra were smoothed with an innovative wavelet approach.In the first stage of the analysis, the reflectance spectra of the vegetation types were tested for differences between type classes. It was found that the reflectance spectra of saltmarsh vegetation types are statistically significantly different for various spectral regions.Secondly, it was tested whether this statistical difference could be enhanced by using continuum removal as a normalisation technique. For vegetation spectra, continuum removal improves the statistical difference between vegetation types in the visible spectrum, but weakens the statistical difference of the spectra in the near-infrared and shortwave infrared part of the spectrum.Thirdly, after statistical differences were found, it was determined how distant in spectral space the vegetation type classes were from each other, using the Bhattacharyya (BH) and the Jeffries-Matusita (JM) distance measures. We selected six wavelengths for this, based on the statistical analysis of the first step. The potential of correct classification of the saltmarsh vegetation types using hyperspectral remote sensing is predicted by these distance measures.It is concluded that the reflectance of vegetation types is statistically different. With high quality radiometric calibration of hyperspectral imagery, it is anticipated that vegetation species may be identified from imagery using spectral libraries that were measured in the field during the time of image acquisition.     

Daytime fire detection using airborne hyperspectral data

The shortwave infrared region of the electromagnetic spectrum, covering wavelengths from 1400 to 2500 nm, can include significant emitted radiance from fire. There have been relatively few evaluations of the utility of shortwave infrared remote sensing data, and in particular hyperspectral remote sensing data, for fire detection. We used an Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) scene acquired over the 2003 Simi Fire to identify the hyperspectral index that was able to most accurately detect pixels containing fire. All AVIRIS band combinations were used to calculate normalized difference indices, and kappa was used to compare classification ability of these indices for three different fire temperature ranges. The most accurate index was named the Hyperspectral Fire Detection Index (HFDI). The HFDI uses shortwave infrared bands centered at 2061 and 2429 nm. These bands are sensitive to atmospheric attenuation, so the impacts of variable elevation, solar zenith angle, and atmospheric water vapor concentration on HFDI were assessed using radiative transfer modeling. While varying these conditions did affect HFDI values, relative differences between background HFDI and HFDI for 1% fire pixel coverage were maintained. HFDI is most appropriate for detection of flaming combustion, and may miss lower temperature smoldering combustion at low percent pixel coverage due to low emitted radiance in the shortwave infrared. HFDI, two previously proposed hyperspectral fire detection indices, and a broadband shortwave infrared-based fire detection index were applied to AVIRIS scenes acquired over the 2007 Zaca Fire and 2008 Indians Fire. A qualitative comparison of the indices demonstrated that HFDI provides improved detection of fire with less variability in background index values.     

Iteratively regularized Gauss-Newton method for atmospheric remote sensing

In this paper we present an inversion algorithm for nonlinear ill-posed problems arising in atmospheric remote sensing. The proposed method is the iteratively regularized Gauss-Newton method. The dependence of the performance and behaviour of the algorithm on the choice of the regularization matrices and sequences of regularization parameters is studied by means of simulations. A method for improving the accuracy of the solution when the identity matrix is used as regularization matrix is also discussed. Results are presented for atmospheric temperature retrievals from a far infrared spectrum observed by an airborne uplooking heterodyne instrument.     

HyMap hyperspectral remote sensing to detect hydrocarbons

The ability of airborne hyperspectral remote sensing methods to detect hydrocarbons was investigated by the Federal Institute of Geosciences and Natural Resources. Reference areas of defined geometry and chemical properties were prepared, e.g. sandy soil, oil-contaminated soil, grass, plastic tarpaulins. The aim of the study was to collect hyperspectral data from these areas and simultaneously determine their spectra with the infrared intelligent spectroradiometer GER Mark V IRIS. The data corrections and further processing were based on data provided by a field spectrometer. This study showed that airborne hyperspectral remote sensing can be used to detect hydrocarbons efficiently. Hydrocarbon-bearing substances are characterized by typical absorption features in the spectra. The availability of the high signal-to-noise-ratio HyMap hyperspectral imaging system permits these features to be recognized in the pixel spectra even if they are not very pronounced. Oil contaminated soil and other materials containing hydrocarbons can be detected and located directly and unambiguously by image processing focused on the spectral characteristics of hydrocarbons. By this procedure, atmospheric correction of the HyMap data is not necessary.     

The Short Wave Aerostat-Mounted Imager (SWAMI): A novel platform for acquiring remotely sensed data from a tethered balloon

We describe a new remote sensing system called the Short Wave Aerostat-Mounted Imager (SWAMI). The SWAMI is designed to acquire co-located video imagery and hyperspectral data to study basic remote sensing questions and to link landscape level trace gas fluxes with spatially and temporally appropriate spectral observations. The SWAMI can fly at altitudes up to 2 km above ground level to bridge the spatial gap between radiometric measurements collected near the surface and those acquired by other aircraft or satellites. The SWAMI platform consists of a dual channel hyperspectral spectroradiometer, video camera, GPS, thermal infrared sensor, and several meteorological and control sensors. All SWAMI functions (e.g. data acquisition and sensor pointing) can be controlled from the ground via wireless transmission. Sample data from the sampling platform are presented, along with several potential scientific applications of SWAMI data.     

温室气体观测卫星GOSAT及产品

为了深入了解国际上新一代温室气体观测卫星及其产品,详细介绍了GOSAT卫星发射背景、卫星平台、传感器设置、地面系统及数据产品特点。GOSAT卫星采用干涉分光技术,结合多种观测方式,可以获得高精度、高时空分辨率的温室气体浓度及廓线资料,其卫星设计及数据应用思路,为我国发展温室气体探测卫星提供了重要参考价值。