无人机平台的沙漠场地BRDF特征观测方法及建模

目标双向反射分布函数BRDF（Bidirectional Reflectance Distribution Function）不仅是陆面遥感的关键地球物理参数,也是星载光学遥感仪器基于地面目标的场地辐射校正重要参量,是影响定标精度的关键要素。传统野外地物多角度测量使用的观测设备,一般其结构较为复杂,重量体积较大,而且运输和组装过程繁琐,观测目标时容易受地形和交通限制,难以进行高效快速精确的野外测量。近年来,无人机由于其设备操作简便、运输和观测方式灵活等方面的优点,可作为新的观测平台应用于当前遥感试验中。本文设计了一种基于无人机平台的地表BRDF测量装置、观测方案和数据处理流程。利用多旋翼低空无人机和云台的组合,搭载野外地物光谱仪和跟拍相机,通过对地面目标多角度观测和高精度定位及角度控制,实现针对固定目标的多方位角和天顶角观测。本文采用上述设计方案和观测流程,在敦煌辐射校正场开展多次稳定均匀沙漠目标的多角度光谱观测试验,并利用实验观测数据,基于Ross-Li核驱动模型推算了场地BRDF模型参数,并与MODIS的陆表BRDF产品（MCD43C1）及反射率产品（MOD/MYD09）进行对比验证。通过开展野外实验,核验了这种新的BRDF观测手段的可靠性,获取的敦煌地表BRDF参数与MODIS遥感产品有良好的一致性,各波段的相对偏差在5%以内。本研究表明,基于多旋翼无人机的BRDF观测系统,提供了一种全新的地物目标方向反射特性观测方法,可用于自动化高频次场地特性观测以及卫星同步定标等野外实验活动。在保证观测精度的同时,极大地减轻人力物力的投入,值得广泛推广应用。     

GF-1星WFV相机的快速大气校正

高分一号卫星(GF-1)WFV相机是中国新型高分辨率传感器,为了更好地进行定量应用,需完成高精度大气校正,但需要解决数量大,辅助数据不足等关键问题。针对WFV相机构建了快速大气校正模型,(1)采用交叉定标方法借助Landsat 8数据完成辐射定标;(2)从WFV相机的辅助数据出发,计算得到太阳天顶角、观测天顶角等辅助信息;(3)考虑不同海拔大气分子散射的不同,完成基于海拔数据的分子散射校正;(4)采用深蓝算法,从第一波段(蓝光)反演得到气溶胶信息;(5)计算每个像元的大气校正参数,进而获取地表反射率,完成大气校正。在此基础上,利用IDL语言建立相应的大气校正模块,以过境华北地区的3景WFV数据为例进行大气校正实验。结果表明,模型能够快速完成大气校正,并能较好的去除大气分子与气溶胶影响,较好地还原植被、裸土等典型地表类型的光谱反射曲线,校正后的NDVI更好地反映了各地物的特征。     

考虑地物BRDF特性改进后的CBERS-02卫星CCD相机的辐射定标系数

在MODIS数据对CBERS-02卫星CCD相机的交叉辐射定标、获得CCD相机时间系列的辐射定标斜率系数与截距基础上，本文考虑地物BRDF特性的影响，开展了提高CCD相机数据定量化应用的辐射定标数据精度的研究，并提供了比较全的时间系列辐射定标系数。     

顾及多积分时间特性的GF-4卫星PMS传感器交叉辐射定标

针对GF-4卫星PMS传感器具备5个积分时间的成像特性,提出一种基于辐射区域网平差的交叉辐射定标方法。首先,利用敦煌辐射校正场地区时间序列有效MODIS影像集,构建该区域高精度BRDF模型。其次,考虑BRDF和SBAF校正,从该地区Landsat-8卫星OLI影像和PMS影像中提取辐射控制点。然后,设置DN值和CV限制条件,在凝视成像状态下从不同积分时间PMS影像中提取辐射连接点。最后,利用新建定标模型计算各积分时间状态下的定标系数。验证结果表明,不同积分时间的各波段平均绝对定标精度均优于5.18%,且不同积分时间影像间各波段辐射差异最高降低36.64%。同时,探讨了辐射控制点分布、BRDF、SBAF和辐射连接点筛选条件对定标结果的影响,为进一步优化PMS传感器交叉辐射定标流程提供重要参考。     

基于准不变目标物下CBERS-02星CCD图像的交叉定标

提出了一种基于准不变反射率地物的交叉定标方法,首先用MODTRAN模式从已知定标系数的影像中反演出反射率,然后计算出对应未知定标系数影像中相同准不变地物的辐射率,最后求出定标系数.将这种方法应用于CBERS-02卫星CCD影像定标,并用6S模式对CBERS-02和ETM+影像进行大气校正,比较用不同定标系数校正后的地物光谱曲线,得出在高反射率地物下,不同定标系数校正后的地物光谱曲线与ETM+的基本一致;低反射率地物下,单点定标系数误差较大,两点定标系数误差较小,用两点定标系数进行大气校正后植被信息得到明显增强.     

“天绘一号”卫星在轨辐射定标方法

在轨辐射定标包括相对辐射定标和绝对辐射定标, 它是提高遥感数据定量化精度的关键步骤和重要方法。本文阐述了基于均匀场地分区综合的相对辐射定标方法、基于反照率的绝对辐射定标方法及其基本原理, 并首次将其应用于“天绘一号”卫星上的高分辨、多光谱和三线阵相机的辐射定标中。研究结果表明, 相对辐射定标过程去除了卫星图像的条带噪声, 且保存了图像细节;然后, 使用反照率基法, 通过在敦煌场地铺设灰阶靶标, 测量卫星过顶时的地物目标反射率光谱和大气信息, 对“天绘一号”卫星传感器进行了绝对辐射定标;最后, 使用辐射定标结果来反演地物反射率, 与实测的地物反射率相比误差小于5%, 验证了在轨辐射定标系数的有效性。     

卫星遥感大气订正的参数化模式及其模拟应用

发展了一个用于卫星大气订正的参数化模式,包括一个新的程辐射亮度模式和一个参数化的朗伯地表一大气辐射耦合引起的亮度增量模式.应用最小二乘法,程辐射亮度被参数化为大气总光学厚度、一次散射反照率、太阳天顶角、视天顶角、方位角、大气不对称因子的函数.应用这一参数化的亮度模式进行大气订正应用的数值模拟,即进行卫星遥感地表光谱反照率的模拟试验.数值检验结果表明对于865 nm,670nm,550nm和412nm 4个MODIS通道,在0°-70°的太阳天顶角、0°-60°视观测角以及0.05-0.8的地表反照率条件下,参数化的向上辐射亮度的标准差小于4%,由该参数化亮度模式引起的地表反照率解的标准差小于0.03.     

基于BRDF原型反演地表反照率

先验知识在遥感反演中起到重要的作用,本文首先根据各向异性平整指数AFX和地表多角度观测数据集建立了BRDF二向性反射分布函数原型库,然后介绍了以BRDF原型作为先验知识的提取多角度数据地表反照率的原型反演算法,为验证算法的精度,将原型反演及全反演算法拟合验证数据子集得到的反照率与MODIS全反演算法拟合完整数据集得到的结果作比较。结果对比表明,观测天顶角在40°以内时,非垂直主平面的观测数据能够代表部分地表的各向异性反射特征,此时原型反演算法的最大平均绝对误差为0.036,相对精度比全反演算法高约5%—10%;当数据位于垂直主平面时,观测信息量严重不足,此时原型反演算法结果相对较稳定,而全反演算法的最大绝对误差高达0.18。 综上所述,原型反演算法能够在整个采样空间范围内对BRDF进行约束,对观测数据位置分布的依赖较小,抗噪声能力强,在反演观测信息不足的多角度数据时,结果精度优于全反演算法。     

基于可展开式太阳漫反射板的星上相对辐射定标

相对辐射定标的目的是降低或消除由于探测元件之间响应差异引起的图像高频竖向条纹或条带噪声,是遥感数据定量化应用的前提与基础。针对可展开式太阳漫反射星上定标装置,本文提出利用漫反射板收拢过程一次成像的星上相对辐射定标方法。通过卫星机械结构驱动漫反射板收拢调整传感器入瞳处太阳入射能量,获取覆盖传感器全部灰度动态范围的星上定标图像,采用直方图匹配算法解算相对辐射定标系数。为了验证本文提出的方法,利用在轨运行的吉林一号光谱星多光谱仪进行星上相对辐射定标实验,测试定标系数对原始图像的校正效果并定量分析相对辐射定标精度,实验结果表明定标系数能够有效消除全色与多光谱谱段地物原始图像的竖向高频条纹与条带噪声,定量分析的结果表明本文提出的方法在传感器灰度响应的全动态范围均能达到较好的校正效果,谱段相对辐射定标精度优于2%,为卫星在轨运行期间进行定量化遥感应用提供可靠保障。     

天然林林下地表热辐射方向性观测

热辐射方向性是影响地表温度反演精度的关键因素之一。为深入了解森林地表的热辐射方向性特征,探索林下地表的热辐射方向性规律,在夏秋两季分别用FLIR i7和T420热像仪对东北天然林下草本植被与部分裸露土壤的亮度温度进行了多角度观测和分析,采用线性近似方法对获取的方向亮温进行纠正。结果表明:(1)林下地表具有较为明显的热辐射方向性,并与地表结构有关;(2)夏季地表由植被和小部分裸露土壤组成,冠层密度大,不同天顶角观测的亮温差异较小,平均为1—1.5℃,10:30 am观测亮温最高值在东方向天顶角40°位置(太阳天顶角30.5°±0.5°,方位角149°±1°),而14:30 pm在西方向天顶角40°左右(太阳天顶角为43.5°±1.5°,方位角247.5°±1.5°);(3)秋季地表大部分为10 cm厚的枯落物,不同天顶角观测差异较夏季大1℃左右,最高亮温则均位于天顶角50°,10:30 am位于东方向(太阳天顶角为57.5°±0.5°,方位角169°±2°),14:30 pm位于西方向(太阳天顶角为73°±1°,方位角231°±1°)。(4)林龄对于林下地表的热辐射方向性有一定影响,但季节性影响更显著。因此,林下地表的热辐射方向性对遥感像元尺度亮度温度的方向性有贡献,在以后的研究中应该予以考虑。     

Evaluation of directional normalization methods for Landsat TM/ETM+ over primary Amazonian lowland forests

Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross–Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration.     

基于ETM~+和ASTER近红外波段的水体信息提取对比

利用Landsat ETM+和ASTER近红外波段数据进行了水体信息提取,然后利用知识规则对2种提取结果进行进一步分类,并分析了波谱分辨率的差异对水体信息提取结果的影响。实验表明,基于Landsat ETM+数据的水体提取总体精度为82.4%,基于ASTER数据的水体信息提取结果总体精度为92.4%;在空间分辨率相同情况下,波谱分辨率的提高可以有效地提高水体信息提取的精度。     

A simple procedure for estimating distributed daily evapotranspiration using landsat-TM data

A procedure to estimate distributed daily evapotranspiration (ET) using remotely sensed data is presented. Landsat-TM data for a part of the Western Yamuna Canal command (Haryana) has been used for model application. The model utilizes the surface reflectance in visible, infrared and thermal bands to generate surface albedo, surface temperature and leaf area index and thus surface energy fluxes to determine distributed daily ET. Result reveals a reasonable estimate of distributed daily ET. For well-watered crop, average ET by the proposed model is estimated as 1.8 mm/d, whereas using Penmen-Monteith equation it is calculated as 1.9 mm/d. The error involved in estimating ET by the proposed model is calculated about 5%, which is quite acceptable for most applications. The proposed procedure is also found computationally simple and can also be applied on current Landsat ETM+ data.     

MISR和MODIS二向性反射数据产品的对比分析

利用4种地表类型共16个地面站点的MISR 9个角度反射率数据产品和MODIS反射率数据产品(MOD09A1), 对比两种传感器获取的地表方向反射观测数据和用BRDF模型参数产品计算的方向反射数据, 分析了这两种BRDF模型参数的外推能力。研究结果表明: (1) MISR和MODIS BRDF模型参数数据产品都具有一定的对观测数据以外方向反射的外推能力, 相比用MISR BRDF模型参数数据外推到相应MODIS观测角度方向反射的结果一致性较强。(2) 采用两种BRDF模型参数推算的方向反射率与观测数据的接近程度有随观测天顶角的增大而减弱的趋势, 在观测天顶角较小时一致性较好。(3) 所用数据处理结果显示, MODIS BRDF模型参数数据产品在近垂直主平面方向反射率推算结果相比近主平面方向的推算结果更接近观测数据。     

Calibration of ASTER and ETM + imagery using empirical line method—A case study of north-east of Hajjah, Yemen

This study is aimed at using the Empirical Line Method (ELM) to eliminate atmospheric effects with respect to visible and near infrared bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) and enhanced thematic mapper plus (ETM+) data. Two targets (Amran limestone as light target and quartz-biotite-sericite-graphite schists as dark target), which were widely exposed and easy to identify in the imagery were selected. The accuracy of the atmospheric correction method was evaluated from three targets (vegetation cover, Amran limestone and Akbra shale) of the surface reflectance. Analytical spectral devices (ASD) FieldSpec3 was used to measure the spectra of target samples. ETM+ data were less influenced by the atmospheric effect when compared to ASTER data. Normalized differences vegetation indices (NDVI) displayed good results with reflectance data when compared with digital number (DN) data because it is highly sensitive to ground truth reflectance (GTR). Most of the differences observed before and after calibration of satellite images (ASTER and ETM+) were absorbed in the SWIR region.      

Surface Reflectance Climate Data Records (CDRs) is a Reliable Landsat ETM+ Source to Study Chlorophyll Content in Pecan Orchards

We evaluated the relationships among three Landsat Enhanced Thematic Mapper (ETM+) datasets, top-of-atmosphere (TOA) reflectance, surface reflectance climate data records (surface reflectance-_CDR) and atmospherically corrected images using Fast Line-of-Sight atmospheric analysis of Spectral Hypercubes model (surface reflectance-_FLAASH) and their linkto pecan foliar chlorophyll content(chl-cont). Foliar chlorophyll content as determined with a SPAD meter, and remotely-sensed data were collected from two mature pecan orchards (one grown in a sandy loam and the other in clay loam soil) during the experimental period. Enhanced vegetation index derived from remotely sensed data was correlated to chl-cont. At both orchards, TOA reflectance was significantly lower than surface reflectance within the 550–2400 nm wavelength range. Reflectance from atmospherically corrected images (surface reflectance-_CDR and surface reflectance-_FLAASH) was similar in the shortwave infrared (SWIR: 1550–1750 and 2080–2350 nm) and statistically different in the visible (350–700 nm). Enhanced vegetation index derived from surface reflectance-_CDR and surface reflectance-_FLAASH had higher correlation with chl-cont than TOA. Accordingly, surface reflectance is an essential prerequisite for using Landsat ETM+  data and TOA reflectance could lead to miss-/or underestimate chl-cont in pecan orchards. Interestingly, the correlation comparisons (Williams t test) between surface reflectance-_CDR and chl-cont was statistically similar to the correlation between chl-cont and commercial atmospheric correction model. Overall, surface reflectance-_CDR, which is freely available from the earth explorer portal, is a reliable atmospherically corrected Landsat ETM+ image source to study foliar chlorophyll content in pecan orchards.     

改善MODIS BRDF产品热点效应的方法研究

核驱动的Ross Thick-LiSparse Reciprocal(RTLSR)双向反射函数(BRDF)模型已广泛地应用于MODIS等星载传感器的业务化产品处理中。但是,对于多年MODIS二向反射产品历史数据,如何基于RTMLSR模型发展一种简单有效快速的方法,进行热点效应的校正是一个迫切需要解决的问题。本文提出了一种简单有效的方法,不需要对观测数据重新反演,直接在现有MODIS二向反射产品的基础上进行热点校正,方便用户对历史MODIS二向反射产品的使用。该方法应用POLDER-3/BRDF数据库和部分经MODIS业务化算法筛选的反射率数据进行验证,并与RTLSR模型和RTMLSR模型的结果进行比较,结果表明:(1)该方法比现有的MODIS业务化RTLSR算法,对热点反射率有明显改善,拟合相对误差平均降低了10.12%;(2)该方法相对于RTMLSR模型在热点反射率拟合效果上差别不大,相对误差相差2.10%;(3)该方法对热点和冷点归一化的植被指数(NDHD)的估算效果有一定程度的改善,相对于RTLSR模型降低了约4.99%,与RTMLSR模型的相对误差相差1.32%,该方法对直接应用现有MODIS BRDF产品,基于热点方向反射率反演植被结构参数(如植被聚集指数)的精度提高有现实应用价值。     

A kernel functions analysis for support vector machines for land cover classification

Information about the Earth's surface is required in many wide-scale applications. Land cover/use classification using remotely sensed images is one of the most common applications in remote sensing, and many algorithms have been developed and applied for this purpose in the literature. Support vector machines (SVMs) are a group of supervised classification algorithms that have been recently used in the remote sensing field. The classification accuracy produced by SVMs may show variation depending on the choice of the kernel function and its parameters. In this study, SVMs were used for land cover classification of Gebze district of Turkey using Landsat ETM+ and Terra ASTER images. Polynomial and radial basis kernel functions with their estimated optimum parameters were applied for the classification of the data sets and the results were analyzed thoroughly. Results showed that SVMs, especially with the use of radial basis function kernel, outperform the maximum likelihood classifier in terms of overall and individual class accuracies. Some important findings were also obtained concerning the changes in land use/cover in the study area. This study verifies the effectiveness and robustness of SVMs in the classification of remotely sensed images.     

ASTER GDEM与SRTM3高程差异影响因素分析

作为最新发布的全球地形数据,ASTER GDEM比目前常用的SRTM3数据有着更高的分辨率和更广的覆盖范围,对于相关地学分析具有重要意义。本文以华中地区为研究区域,对ASTER GDEM与SRTM3数据进行了比较,重点分析了坡度、坡向、地形起伏度、土地利用类型、植被覆盖度、生成ASTER GDEM栅格点高程数据所用的ASTER DEM影像数等因素对2种DEM数据高程差异的影响。结果表明,在研究区域内,ASTER GDEM高程比SRTM3高程平均低5.42 m,两种DEM数据高程差异的RMS值为16.90 m;ASTER GDEM与SRTM3之间的高程差异随着坡度、地形起伏度、植被覆盖度的增大而增大,而ASTER DEM影像数越大,高程差异越小;坡向、土地利用类型对高程差异也有影响。     

对ASTER GDEM数字高程模型的精度评价及修正

针对ASTER GDEM高程精度还未得到充分验证,以江西省莲花县为试验区,使用ICESat-2数据系统分析了ASTER GDEM在坡度、地形起伏度和土地利用类型中的误差分布。结果表明,ASTER GDEM受坡度、地形起伏度影响严重,随坡度、地形起伏度增加,GDEM误差呈上升趋势;对于不同土地利用类型,GDEM误差存在较大差异,在水域误差最大,在建设用地误差最小。最后,使用后向传播神经网络(BPNN)对莲花县ASTER GDEM修正,结果发现BPNN模型可以有效改善其高程精度。