针对MODIS影像的劈窗算法研究

在分析热红外遥感和现有的劈窗算法的理论基础上,针对MODIS数据对劈窗算法进行了推导。通过对热辐射强度和温度之间的关系计算,对Planck函数进行了线性简化,同时分析了MODIS的波段设置特点。MODIS的近红外波段适宜于反演大气水汽含量,而大气透过率主要从MODIS的近红外波段数据反演得到大气水汽含量,并进而根据水汽含量与大气透过率的关系来进行估算。通过MODIS的可见光波段、近红外和中红外波段数据,完全可以获得地表温度反演所需要的基本参数,从而形成了针对MODIS数据的地表温度反演的劈窗算法。最后以环渤海地区为实验区,对本文提出的方法进行了实际应用分析。     

基于GPS和MODIS数据的大气可降水量反演算法研究

基于高精度的PWVGPS与MODIS近红外三通道比值透射率间的关系建立MODIS大气可降水量新反演算法以提高反演精度。利用美国Kansas州和Oklahoma州2009年4月～9月间17期的MODIS数据和对应的SuomiNet网PWVGPS数据进行研究,发现Kaufman和Gao Bo-Cai算法反演的PWVMODIS系统性地偏低于PWVGPS,存在较大的反演误差。以高精度的PWVGPS为标准水汽值,研究PWVGPS与MODIS近红外第19波段的三通道比值透射率间的关系,建立了MODIS大气可降水量新反演算法,并利用研究区数据进行试验验证,结果表明新算法可大幅提高PWVMODIS的反演精度。     

应用MODIS数据反演河北省海域叶绿素a浓度

为了建立更加合理、准确的叶绿素a遥感反演模型,利用地物光谱仪测定了河北省海域水面的光谱反射率,分析了光谱反射率与实测叶绿素a浓度之间的关系.在此基础上,通过MODIS数据各波段及波段组合的反射率与实测叶绿素a浓度的相关分析,确定第1波段(B1)为最佳反演波段,建立了应用B1反演叶绿素a浓度的遥感模型,并对模型精度进行验证.结果表明:该模型相关系数为0.66,反演结果均方根误差为0.48 mg/m3,模型精度优于SeaDAS的OC3标准经验算法;该模型反演河北省海域表层水体的叶绿素a浓度有较好的效果.     

利用微波辐射计AMSR-E的京津冀地区大气水汽反演

目的 发展了微波遥感水汽反演算法,对于裸露地表,通过极化差比值形式消除地表信息对大气水汽反演的干扰;针对非裸露地表,首先反演了地表发射率并对不同波段地表发射率之间的关系进行分析,进而建立了非裸露地表上空大气水汽的反演算法。本文算法的反演结果与GPS探测结果的对比显示均方根误差为7.4mm,与MODIS大气水汽产品空间分布特征的对比也显示了两者较高的区域一致性。最后对京津冀平原地区和山地地区的水汽进行了时间序列的分析。     

基于劈窗算法从ASTER遥感数据反演地表温度

应用卫星热红外遥感影像反演地表温度对于研究城市生态环境、气象过程具有重要意义,ASTER 遥感数据为此提供了有效的信息源。针对从 ASTER 数据中反演地表温度(LST)的需要,首先利用 MODIS 数据反演大气水汽含量,并模拟出大气水汽含量与大气透过率的关系,求得大气透过率,然后通过决策树分类结果和地物光谱特征计算出地表反射率,最后采用劈窗算法反演出地表温度。通过某市2个季节的试验表明该方法具有较高的精度,能够有效应用于城市热环境分析,为城市物理环境综合分析评价提供支持。     

北京地区Landsat 8 OLI高空间分辨率气溶胶光学厚度反演

卫星气溶胶光学厚度(AOD)反演中,传统暗目标方法在反射率较低的水体、浓密植被覆盖区域取得了较好效果,在反射率较高且结构复杂的高反射地表上空目前多采用深蓝算法,但存在空间分辨率较低,对细节分布描述性较差等问题。为解决这一问题,本文首先以5年(2008年—2012年)长时间序列MODIS地表反射率产品为基础,采用最小值合成法建立500 m分辨率逐月地表反射率产品数据集,然后利用地物波谱库中典型地物波谱数据,分析建立MODIS与Landsat 8 OLI传感器蓝光波段反射率转换模型,最后北京地区AERONET地基观测数据确定了气溶胶光学物理参数,并反演获取了北京地区上空500 m分辨率的AOD分布。为验证反演算法的精度,分别将反演结果同AERONET及MODIS/Terra气溶胶产品(MOD04)进行交叉对比,同时利用相关系数R,均方根误差RMSE,平均绝对误差MAE以及MODIS AOD产品预期误差EE共4个指标进行衡量。结果表明:算法反演获取的AOD与AERONET观测值具有较高的一致性,各指标分别为R=0.963,RMSE=0.156,MAE=0.097,EE=85.3%,稍优于MOD04产品(R=0.962,RMSE=0.158,MAE=0.101,EE=75.8%),并且有效的对比点数也高于MOD04。通过与地基观测相比,卫星遥感获取的高分辨率城市地区AOD精度可作为定量评估城市空气质量的有效依据。     

利用MODIS图像反演海岸与海岛的地物光谱反射率

提出一种利用MODIS图像,用查找表反演海岸与海岛地物光谱反射率的方法。该方法首先借助AHMAD辐射传输模型,由MODIS图像的水体像元反演出气溶胶的光学特性;在所选影像为晴空无云条件下,假设一定范围内的海岛与海岸上空的大气和水体上空的大气一样,借助6S辐射传输模型计算基于地物光谱反射率的查找表,然后由MODIS图像的陆地像元的反射率和几何条件加上反演的气溶胶光学厚度,用插值法可求得地物光谱反射率。还给出了厦门地区实际卫星图像的反演结果,并就反演误差进行了分析。     

MODIS大气校正精度评价及其对表层雪密度提取影响

以古尔班通古特沙漠为研究区,以中分辨率成像光谱仪（MODIS）为遥感数据源,结合ASD FieldSpec准同步实测积雪反射光谱数据对FLAASH大气校正能力进行了评价。研究表明： ①校正后的MODIS各波段积雪反射率与准同步实测积雪反射率波形相似, 在第1～7波段整体相关系数达0.82,表明FLAASH大气校正能极大地提高MODIS地物识别能力; ②校正后的MODIS 第6波段反射率和归一化差值积雪指数（NDSI）与实测雪密度呈线性相关,可用回归拟合构建MODIS雪密度遥感计算模式。     

光谱先验知识在植被结构遥感反演中的应用

针对绿色植被反射光谱的特点，指出植被反射光谱曲线的相对位置关系可以作为先验知识应用到对植被结构的反演中，提出了在绿光、红光和近红外波段重新构造光谱参数，并利用波段差值和比值描述光谱先验知识进而反演树冠结构参数的方法。经大量的模拟反演及统计分析表明，相对于各波段单独反演再对结构参数平均的方法，该方法的抗噪声能力 得到了明显的增强。对实测反射率数据的反演发现，引入光谱先验知识后的结果更为接近测量值。     

利用MODIS BRDF估算中国东北森林背景反射率——以大兴安岭加格达奇地区为例

基于MODIS二向反射分布函数(BRDF)模型参数产品数据,利用4-scale模型建立查找表,以中国东北大兴安岭加格达奇地区为研究区,反演森林背景反射率,并分析不同森林类型二向反射与背景反射率特性及其季节变化。研究结果表明:(1)研究区针叶林和混交林二向反射特征较为相似,夏季阔叶林在红光波段的二向反射率值均低于针叶林和混交林,而在近红外波段则相反;不同森林类型二向反射率均存在明显的季节变化,其中阔叶林二向反射率季节变化最为明显;(2)研究区夏季森林背景反射率在红光波段较低,均在0.1以下,近红外波段背景反射率普遍高于0.3,且空间差异较大;(3)不同森林类型的背景反射率季节变化趋势大致相同,但变化幅度存在差异:阔叶林的背景反射率值季节差异最大,尤其在近红外波段。     

针对MODIS近红外数据反演大气水汽含量研究

遥感反演大气水汽含量对进行天气预报、遥感大气校正、气候变化及水循环等研究具有重要意义。首先，通过对大气辐射传 输方程的推导，改进了三通道算法； 然后，模拟了在不同传感器视角条件下，大气水汽含量与MODIS 17、18、19通道大气透过率之 间的关系，解决了传感器视角问题，提出了针对MODIS数据的大气水汽含量计算方法； 最后，在IDL 6.0环境下，编程实现了该方法 ，并对2003年6月14日的一景图像进行了反演，结果表明，本文提出的方法是可行的。     

Study of fractional vegetation cover using high spectral resolution data

A field experiment was conducted to study the effect of vegetation cover on soil spectra and relationship of spectral indices with vegetation cover. Multi-date spectral measurements were carried out on twelve wheat fields. Five sets of measurements were taken during the growth period of wheat crop. Field reflectance data were collected in the range 350 to 1800 nm using ASD spectroradiometer. Analysis of data was done to select narrow spectral bands for estimation of ground cover. The ratio of reflectance from vegetation covered soil and reflectance from bare soil indicated that spectral reflectance at 670 and 710 nm are the most sensitive bands. Two bands in visible (670 and 560 nm), three bands in near infrared (710, 870 and 1100 nm) and three bands in middle infrared (1480, 1700 and 1800 nm) were found highly correlated with fractional cover. Vegetation indices developed using narrow band spectral data have been found to be better than those developed using broad- band data for estimation of ground cover.     

Spectral Reflectance Characteristics of the Soils on Basaltic Terrain of Central Indian Plateau

In-situ spectral reflectance characteristics of soils were studied under field conditions with Multiband Ground Truth Radiometer covering 0.45?C0.52, 0.52?C0.59, 0.62?C0.68, and 0.77?C0.86 ??m spectral bands. Twenty-two surface soil samples were studied in laboratory for their spectral reflectance characteristics using ISCO Model S.R. Spectroradiometer in visible wavelength (450?C725 nm), with 25 nm bandwidth, and in infrared wavelength (750?C1550 nm), with 50 nm bandwidth. The Bidirectional reflectance factor representative of spectral reflectance varied from 3.78 to 11.3???m in band 1, 6.09 to 15.41???m in band 2, 8.05 to 19.41???m in band 3, and 12.18 to 31.2???m in band 4. In-situ spectral reflectance in general increases with the wavelength from visible to infrared bands for all the soils. Black soils have relatively lower reflectance as compared to red soils, which is attributed to the variation in the physicochemical properties of soils. Spectral reflectance, under laboratory conditions, for all the soils increases with wavelength from visible to infrared region except at 950 nm and 1200 nm, where reflectance decreased in all soils, due to weaker water absorption bands and also at 1350 nm, due to strong water absorption at this band. The spectral reflectance of red soils were higher, in-situ as well as under laboratory conditions, as compared to black soils, which is attributed to variation in soil colour, organic matter and clay content of soils. It is observed that the spectral reflectance decrease due to moisture content in soils in all the spectral bands because of darker appearance of soils at moist conditions. Laboratory reflectance measurements serve to define the extent to which intrinsic spectral information is available from soils as a consequence of their composition.     

Sentinel-2A与Landsat 8O LI逐像元辐射归一化方法研究

考虑不同传感器光谱响应函数差异及不同地物类型反射率光谱的差异，提出了一种逐像元辐射归一化方法，并以2017年7月17日内蒙古达里诺尔湖地区准同步过境的Sentinel-2A及Landsat 8数据为例，对两类数据可见-近红外波段（VNIR）地表反射率结果进行归一化。首先采用Sen2cor方法及NASA官方提供大气校正算法，分别对Sentinel-2A及Landsat 8 OLI影像进行大气校正并重采样到同一空间分辨率；然后基于光谱库计算匹配因子并构建图像与光谱库之间的匹配转换模型，实现像元尺度上从Sentinel-2影像到Landsat 8影像地表反射率相似波段之间的转换。结果表明，经逐像元归一化的影像相比原始影像及经HLS光谱归一化的影像，与Landsat 8 VNIR波段的相关性明显提高，辐射一致性增强。该转换模型为多源中高分辨率遥感图像高精度辐射归一化提供了新思路。     

Day-1 INSAT-3DR Vicarious Calibration Using Reflectance-Based Approach Over Great Rann of Kutch

This study describes the post-launch calibration for visible (VIS) and shortwave infrared (SWIR) bands of Indian National Satellite System (INSAT)-3DR imager over Great Rann of Kutch (GROK) on Day-1 (15th September 2016), when the first time INSAT-3DR Imager camera was switched on. In order to account the characterization of errors and undetermined post-launch changes in sensor spectral response, this calibration activity was performed and extended for its monitoring to Day-56 (since the Day-1; 09th November 2016). A reflectance based technique is used in the present study. The surface reflectance and atmospheric variables were measured over the site as per solar and viewing geometry of the INSAT-3D scan. Top of atmosphere (TOA) spectral radiances were computed using 6SV (second simulation of the satellite signal in the solar spectrum) radiative transfer code with the in situ measurements as well as spectral response function of each channel. Preliminary results of the Day-1 vicarious calibration yield gain coefficients of 0.974 and 0.820 for VIS and SWIR channels respectively despite the inhomogeneity of the ground target caused by sufficient sub-surface soil moisture. In extension of the present study, the obtained gain coefficients were 1.001 and 0.9887 for VIS and SWIR, respectively, during Day-56 which indicates the performance of sensor is within the range of pre-launch laboratory calibration.     

黄浦江水质指标与反射光谱特征的关系分析

利用地物光谱测量技术及同步配套的常规水质采样分析实验，对上海市黄浦江全河段水体进行调查研究。共选取31个典型站位进行了光谱测量和同步水质取样，每个测点分析了9个水质指标，包括TP，TN，CODMn，CODCr，NH3-N，BOD5，DO，悬浮物浓度和浊度。各水质参数之间存在较大的相关性，以总氮（TN）和悬浮物浓度两个水质指标为例，分析了这两个水质指标与单波段归一化反射率、反射率的一阶微分、不同波段之间反射率的比值以及反射率取对数等之间的相关关系，给出了以上两个水质指标的单波段归一化反射率、一阶微分反射率识别的特征波段，以及两个波段比值的最佳波段组合。建立了常规水质参数与水体光谱反射率之间的关系模型，为利用遥感技术监测水环境提供了基础。     

Is There a Physical Linkage Between Surface Emissive and Reflective Variables Over Non-Vegetated Surfaces?

For a satellite sensor with only one or two thermal infrared channels, it is difficult to retrieve the surface emissivity from the received emissive signal. Empirical linear relationship between surface emissivity and red reflectance are already established for deriving emissivity, but the inner physical mechanism remains unclear. The optical constants of various minerals that cover the spectral range from 0.44 to 13.5 μm in conjunction with modern radiative transfer models were used to produce corresponding surface reflectance and emissivity spectra. Compared to the commonly used empirical linear relationship, a more accurate multiple linear relationship between Landsat TM5 emissivity and optical reflectances was derived using the simulated data, which indicated the necessity of replacing the empirical relationship with the new one for improving surface emissivity estimate in the single channel algorithm. The significant multiple linear relationship between broadband emissivity (BBE, 8–13.5 μm) and MODIS spectral albedos was also derived using the same data. This paper demonstrates that there is a physical linkage between surface emissive and reflective variables, and provides a theoretical perspective on estimating surface emissivity for sensors with only one or two thermal infrared channels.     

Development of a scheme for atmospheric correction of Resourcesat-2 AWiFS data

This paper presents a method called SACRS2, a scheme for atmospheric correction of RS2-AWiFS (Resourcesat2-Advanced Wide Field Sensor) data. The SACRS2 is a computationally fast scheme developed from a physics-based detailed radiative transfer model 6SV for correcting large amount of data from the high-repetivity AWiFS sensor. The method is based on deriving a set of equations with coefficients which depend on the spectral bands of the RS2-AWiFS sensor through forward signal simulations by 6SV. Semi-empirical formulations provided in the SMAC method with a few improvements have been used to describe various atmospheric interactions. A total of 112 coefficients for different equations are determined using the best fit equations against the computations of the 6SV. After the specific coefficients for the RS2-AWiFS spectral bands are determined, the major inputs of the scheme are raw digital numbers recorded by RS2-AWiFS sensor, atmospheric columnar water vapour content, ozone content, aerosol optical thickness at 550 nm and viewing-illumination conditions. Results showed a good performance of the SACRS2 with a maximum relative error in the SACRS2 simulations ranged between 1% for a reflectance of 0.5 and 8.6% for reflectance of 0.05 with respect to 6SV computations. Validation of retrieved surface reflectance using the SACRS2 scheme with respect to in-situ measurements at two sites indicated a capability of this scheme to determine the surface reflectance within 10%. This is a first of its kind scheme developed for the atmospheric correction of any Indian Remote Sensing satellite data. A package containing the SACRS2 software is available on the MOSDAC website for the researchers.     

不同传感器的模拟植被指数对水稻叶面积指数的估测精度和敏感性分析

本研究利用水稻冠层高光谱数据,模拟NOAA-AVHRR,Terra-MODIS和Landsat-TM的可见光波段反射率数据,计算各传感器的多种植被指数(NDVI,RVI,EVI,GNDVI,GRVI和Red-edge RVI),比较植被指数模型对水稻LAI的估测精度,分析不同植被指数对LAI变化的敏感性.相对于红波段植被指数,红边比值植被指数(Red-edge RVI)和绿波段指数GRVI与LAI有更好的线性相关关系,而GNDVI和LAI呈现更好的对数相关关系.MODIS的Red-edge RVI指数不仅模型拟合的精度最高,还有独立数据验证的估测精度也最高,而且它的验证精度较拟合精度下降幅度最小;其次是绿波段构建的GNDVI和GRVI植被指数的估测精度,再次是NDVI和EVI的估测精度,而RVI的估测精度最差.敏感性分析发现,13个植被指数对水稻LAI的估测能力都随着LAI的增加而下降,但归一化类植被指数和比值类植被指数对LAI变化反应的差异明显,归一化类植被指数在LAI较低时(LAI<1.5)对LAI变化的反应开始非常敏感,但迅速下降,而比值类植被指数在LAI较低时,明显小于归一化类植被指数,之后随着LAI的增大(LAI>1.5)比值类植被指数对LAI的变化敏感性,则明显高于归一化类植被指数.Red-edge RVI和绿波段指数GRVI和LAI不仅表现了很好的线性相关关系,而且在LAI大于2.9左右保持较高的敏感性.