MODIS数据库在测量地物辐射亮度和反射率特性中的应用

以往用户利用MODIS数据研究最多的领域是区域以至全球尺度的环境变化，而利用MODIS数据来定量测量地物参数（如辐射亮度和反射率）的研究却很少，这主要是因为MODIS数据的空间分辨率低，容易造成像元不纯从而影响到数据的精度。但是由于MODIS数据作为目前光谱辐射精度最高的遥感数据源，对于探索遥感定标以及定量遥感具有重要意义。如果所研究地物面积匹配于MOIDS数据的空间分辨率时，那么MODIS无疑是最好的数据源之一。本文详细讨论了MODIS数据参数定标原理和在测量地物辐射亮度和反射率中的应用，并用Landsat-7数据作为参考，验证了MODIS用来测量地物参数的可行性。     

基于多个稳定目标的吉林一号光谱02卫星多光谱成像仪的在轨绝对辐射定标

卫星在轨后精准的绝对辐射定标是对其观测数据进行定量化应用的基础。与传统的场地定标法、交叉定标法相比，基于稳定场景目标的定标方法具有成本低、频次高、可实现历史数据定标等优点。使用中分辨率成像光谱仪（MODIS）的MCD19A1产品、MCD43A1产品和MOD03(MYD03)产品模拟出MODIS band8在光谱02卫星观测条件下的方向反射率，再结合MCD43A1产品中MODIS band1～band5的方向反射率、MCD19A2产品中的气溶胶参数、水汽参数以及MOD07(MYD07)产品中的臭氧参数，对2021年10月光谱02卫星多光谱成像仪对多个稳定目标成像的入瞳辐亮度进行辐射传输模拟，实现对其在轨绝对辐射定标。对该定标系数的真实性检验结果表明：与发射前定标系数相比，基于重新标定系数进行大气校正的结果与哨兵2号反射率产品的差异明显减小；重新标定的观测辐亮度与包头地基自动辐射定标数据的平均相对差异为3.18%，说明定标结果具有较高的精度。研究结果可以为使用稳定目标对中高空间分辨率光学遥感卫星在轨绝对辐射定标提供方法支撑。     

珠海一号欧比特高光谱数据交叉辐射定标初探

搭载于“珠海一号”卫星星座的欧比特高光谱OHS（Orbit Hyper Spectral）传感器,以较高的光谱分辨率和空间分辨率,在近岸及内陆湖泊水色遥感应用方面具有很大潜力。然而OHS缺乏星上定标系统,目前在轨定标采用陆地定标场的资料,其定标结果在水体等低反射率地物误差较大。因此提出一种基于传感器入瞳总辐亮度的交叉辐射定标法,该方法结合QAA（Quasi-Analytical Algorithm）准分析算法和6SV2.1辐射传输模型,利用GOCI（Geostationary Ocean Color Imager）多光谱数据对OHS高光谱数据进行交叉辐射定标。研究结果表明：①GOCI和OHS传感器获取的地物辐射相关性好,在可见光波段范围内,R²均高于0.84;②重新定标后的数据能明显改善不同传感器之间的辐射差异,在可见光波段范围内,定标误差小于9%。实验为高光谱传感器的辐射定标提供了一种新的方法,对建立高光谱定量化、业务化水色遥感处理系统,特别对OHS数据在水域的各种应用具有重要意义。     

基于地面定标技术的地物光谱反演方法研究

阐述了利用地面光谱定标和实验室遥感仪器定标技术和遥感方程对成像光谱遥感数据进行反射率反演的理论基础、技术方案及操作规范。结合航空遥感飞行试验,对成像光谱遥感数据进行了地物反射率反演,并讨论分析了该方法的可行性条件。研究结果表明:(1)该方法简便、实用、可行;(2)地面定标点的选择及光谱测量精度是影响反射率反演准确性的主要因素;(3)遥感器的几何参量、遥感数据获取的时间跨度、地形因子及二向反射因素是造成反射率反演误差的重要原因,在实际应用中也应加以重视。     

基于劈窗算法的Landsat 8影像地表温度反演

陆地表面温度（LST）是表征地表能量交换和地面特征的重要指标,目前遥感技术逐渐成为区域和全球尺度上LST反演的一种便捷工具,而采样不同算法及不同影像的热红外遥感LST反演研究层出不穷,其中基于Landsat数据的反演成果尤为突出。文章利用劈窗算法对Landsat 8遥感影像进行地表温度反演,对比探讨了根据经验值与借助MODIS热红外数据两种不同方式的LST反演结果,并进行北京市热红外波段辐射亮度温度比较,针对地表温度分级进行统计,分析了当地地表温度分布趋势。结果表明：劈窗算法下Landsat 8数据的反演温度更接近实际温度,精度较高且优于MODIS产品;北京市地表温度空间分布格局受地物结构与反射率所制约,高温区主要集中分布于中东部,中低温区分布与林地及水体分布结构较为吻合。     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

环境一号卫星与MODIS NDVI的定量关系分析

不同遥感器由于波段设置和光谱响应函数差异导致其在红光和近红外波段所接收的地物反射信号不同,产生了NDVI的不一致性问题,针对这一问题,该文研究了HJ-1NDVI和MODIS NDVI的定量关系。采用高光谱Hyperion数据进行光谱卷积和空间卷积,模拟HJ-1与MODIS两遥感器波段反射率数据。通过计算NDVI,进行回归分析求出两者之间的定量关系。分别基于单一覆盖类型和混合覆盖类型建立HJ-1与MODIS的转换方程,排除其定量关系可能受不同土地覆盖类型的影响。定量分析与精度验证表明:HJ-1NDVI与MODIS NDVI有很高的线性正相关性(R20.99);单一覆盖类型转换精度高于混合覆盖类型转换精度,均能满足定量应用要求,但单一覆盖类型转换在实际应用中较为复杂,可采用混合覆盖类型转换方程进行HJ-1 NDVI与MODIS NDVI的转换。     

STARFM算法生成湿地类型TM反射率数据的应用评价

当前数据获取条件下,很难直接获得兼具高时间与高空间分辨率的多光谱遥感数据,提出利用STARFM(Spatial and Temporal Adaptive Reflection Fusion Model)算法来合成高时间序列的高空间分辨率数据。该算法在我国地理区域的适用性与预测精度验证等工作尚未充分展开。为此,以内蒙古呼伦湖湿地自然保护区为研究样区,并借助于Landsat-5 TM(Thematic Mapper)与高时序MODIS反射率产品,利用STARFM算法生成具有高时序特征的TM数据,进而将其与真实TM数据进行对比验证分析。结果表明:STARFM算法能够在空间上保持一定预测精度的条件下,对湿地区域内不同地物类别随时相的变化特征具有较好的预测能力,尤其适用于对反射特征随时相变化较小的湿地区域进行时空拟合或数据预测研究。     

HJ-1星CCD相机场地辐射定标与真实性检验研究

为了满足HJ-1星遥感数据定量化应用的需求,2008年10月中旬,以敦煌定标实验场为地面目标,采用反射率基法对HJ-1A和HJ-1B星的CCD相机开展场地绝对辐射定标,以确定CCD相机响应的变化,并对得到的定标结果进行了误差分析。同时利用非定标同日的敦煌场和贡格尔实验场的CCD星地同步数据得到的表观辐亮度作为标准值,对定标系数进行真实性检验研究。结果表明,定标系数模拟出的CCD表观辐亮度与标准值非常接近,误差均小于3%。研究表明:此次定标实验的结果精度高,并将两次同步数据的定标系数平均值作为定标系数结果,为CCD相机定量化应用研究奠定了良好基础。     

Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites

Land surface temperature (LST) and emissivity are key parameters in estimating the land surface radiation budget, a major controlling factor of global climate and environmental change. In this study, Terra Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 LST and emissivity products are evaluated using long-term ground-based longwave radiation observations collected at six Surface Radiation Budget Network (SURFRAD) sites from 2000 to 2007. LSTs at a spatial resolution of 90 m from 197 ASTER images during 2000-2007 are directly compared to ground observations at the six SURFRAD sites. For nighttime data, ASTER LST has an average bias of 0.1 °C and the average bias is 0.3 °C during daytime. Aqua MODIS LST at 1 km resolution during nighttime retrieved from a split-window algorithm is evaluated from 2002 to 2007. MODIS LST has an average bias of − 0.2 °C. LST heterogeneity (defined as the Standard Deviation, STD, of ASTER LSTs in 1 × 1 km² region, 11 × 11 pixel in total) and instrument calibration error of pyrgeometer are key factors impacting the ASTER and MODIS LST evaluation using ground-based radiation measurements. The heterogeneity of nighttime ASTER LST is 1.2 °C, which accounts for 71% of the STD of the comparison, while the heterogeneity of the daytime LST is 2.4 °C, which accounts for 60% of the STD. Collection 5 broadband emissivity is 0.01 larger than that of MODIS Collection 4 products and ASTER emissivity. It is essential to filter out the abnormal low values of ASTER daily emissivity data in summer time before its application.     

基于AMSR-E的全球陆表被动微波发射率数据集

地表微波发射率表征了地物向外发射微波辐射的能力,星载被动微波发射率估算可在宏观、大尺度上对陆表微波辐射进行整体表达,是被动微波地表参数定量反演中重要基础数据,也是在大尺度上获取陆表微波辐射特征的一种途径。本数据集利用搭载在Aqua卫星上的高级微波扫描辐射计(AMSR-E)和中分辨率成像光谱仪(MODIS)的同步观测特点,采用MODIS的地表温度和大气水汽产品数据作为输入,基于考虑大气影响的发射率估算模型,生产了全球晴空条件下AMSR-E传感器运行期间(2002年6月~2011年10月)的陆表多通道双极化微波瞬时发射率。通过产品低频无线电信号影响、数据间比对、分布统计、不同地表覆盖条件的发射率特征、频率依赖和相关性研究等开展验证性分析,结果表明:瞬时发射率的动态大、细节表达丰富,月内日变化标准差在0.02以内,其时空变化、频率依赖和相关性等符合微波理论分析和自然物理过程理解。此套数据集还包括AMSR-E全生命周期的全球陆表逐日、侯、旬、半月及月产品,可用于开展星载被动微波遥感模拟、陆面模型以及陆表温度、积雪、大气降水/水汽/可降水量等反演研究。     

针对MODIS 数据的地表温度非线性迭代反演方法

地表温度是气象、水文、生态等研究领域中的一个重要参数。构建了MODIS31/ 32 波段的热辐射传输方程, 讨论了方程的数值迭代解法, 提出了针对MODIS 数据地表温度的非线性迭代反演方法, 并介绍了大气透过率和地表比辐射率这两个中间参数的估计方法。误差及敏感性分析表明,提出的方法对大气透过率和地表比辐射率都不敏感, 反演精度优于传统的线性分裂窗算法。     

Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors

This study compared surface emissivity and radiometric temperature retrievals derived from data collected with the MODerate resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) sensors, onboard the NASA's Earth Observation System (EOS)-TERRA satellite. Two study sites were selected: a semi-arid area located in northern Chihuahuan desert, USA, and a Savannah landscape located in central Africa. Atmospheric corrections were performed using the MODTRAN 4 atmospheric radiative transfer code along with atmospheric profiles generated by the National Center for Environmental Predictions (NCEP). Atmospheric radiative properties were derived from MODTRAN 4 calculations according to the sensor swaths, which yielded different strategies from one sensor to the other. The MODIS estimates were then computed using a designed Temperature-Independent Spectral Indices of Emissivity (TISIE) method. The ASTER estimates were derived using the Temperature Emissivity Separation (TES) algorithm. The MODIS and ASTER radiometric temperature retrievals were in good agreement when the atmospheric corrections were similar, with differences lower than 0.9 K. The emissivity estimates were compared for MODIS/ASTER matching bands at 8.5 and 11 μm. It was shown that the retrievals agreed well, with RMSD ranging from 0.005 to 0.015, and biases ranging from −0.01 to 0.005. At 8.5 μm, the ranges of emissivities from both sensors were very similar. At 11 μm, however, the ranges of MODIS values were broader than those of the ASTER estimates. The larger MODIS values were ascribed to the gray body problem of the TES algorithm, whereas the lower MODIS values were not consistent with field references. Finally, we assessed the combined effects of spatial variability and sensor resolution. It was shown that for the study areas we considered, these effects were not critical.     

Quantitative study of net radiation from MODIS data in the lower boundary layer in Poyang Lake area of Jiangxi Province,China

This paper focuses on quantitative calculation of longwave radiation and shortwave radiation from MODIS data in the Poyang Lake area of Jiangxi Province, China. The sum of the net longwave radiation and the shortwave radiation is the net radiation. These parameters are critical for the study of energy exchange in the lower boundary layer on land surface. Two of the most important factors for the retrieval of longwave radiation are the land surface temperature and emissivity. In this paper, the land surface temperature and emissivity were calculated from MODIS data using the regional self‐iterative split‐window method. The most important factor in the determination of the shortwave radiation is Earth surface albedo. The spectral reflectance and surface albedo were derived from MODIS data using the Synergy of TERRA and AQUA MODIS data (SYNTAM) algorithm. The net shortwave and longwave radiation were calculated and compared with the in situ measurement data. Our results indicate that the methods for quantitative calculation of net longwave radiation, shortwave radiation and net radiation from MODIS data can have a good accuracy. The relative errors are between 2.1% to 9.72% for longwave radiation, 0.15% to 10.48% for shortwave radiation and 0.64% to 13.7% for net radiation. We can conclude that a good accuracy can be achieved for deriving longwave radiation, shortwave radiation and net radiation, which are helpful for heat exchange, environmental, hydrology and ecology research in land areas.     

基于MODIS 影像数据的劈窗算法研究及其参数确定

劈窗算法是目前由热红外遥感数据获取陆面温度的主要方法。在介绍劈窗算法的一般表现形式的基础上, 我们推导出适合于MOD IS 影像数据的劈窗算法。大气透过率和地表比辐射率是求解地表温度的两个关键参数。由于MOD IS 图像分辨率较低,MOD IS 像元主要由水面、植被和裸土3种地物类型构成, 故可依据这3 种地物的构成比例确定地表比辐射率。从遥感影像上反演大气的水汽含量, 再根据大气水汽含量与大气透过率的关系计算出大气透过率。最后将文中推导的劈窗算法用于江苏省地表温度的反演。反演出来的地表温度图显示出明显的地表温度空间差异、城市热岛效应和不同的地物类型。     

Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study

Net radiation is a key component in the surface radiation budget. Numerous studies have developed frameworks to estimate net radiation or its components (upwelling or downwelling longwave and/or shortwave radiation) from remote sensing data for clear sky conditions. Application of existing methodologies to estimate net radiation for cloudy sky conditions from remote sensing sensors remains a significant challenge. In this paper, we present a framework to estimate instantaneous and daily average net radiation under all sky conditions from using the data from the MODerate Resolution Imaging Spectroradiometer (MODIS), onboard from the Terra satellites. Bisht et al. (2005) methodology is used for the clear sky portion of the MODIS overpass; while for cloudy portion of the MODIS overpass an extension of Bisht et al. (2005) methodology is applied. The extension of Bisht et al. (2005) methodology utilizes the MODIS cloud data product (MOD06_L2) for cloud top temperature, cloud fraction, cloud emissivity, cloud optical thickness and land surface temperature for cloudy days. The methodology is applied over the Southern Great Plains (SGP) for a time period covering all seasons of 2006. During the MODIS-Terra overpasses in 2006 over the SGP, only 24% of day-overpasses and 9% of night-overpasses had 75% or more of the study region as cloud free. Thus, this proposed study is applicable to a large portion of the MODIS-Terra overpasses. The root mean square errors (RMSE) of instantaneous and daily average net radiation estimated under cloudy conditions using the MOD06_L2 product, comparing to ground-based measurements are 37 W m^− 2 and 38 W m^− 2, respectively. The strength of the proposed methodology is that it can rely exclusively on remote sensing data in the absence of ancillary ground observations, thus it has a potential to estimate surface energy budget globally.     

森林叶面积指数遥感反演与空间尺度转换研究

以贵州省黎平县为研究区,着重研究森林叶面积指数(LAI)的ETM遥感信息反演和向1km空间尺度转换算法.通过LAI-2000的针叶林和阔叶林等植被类型的LAI实地观测,建立实测LAI与ETM影像归一化植被指数(NDVI)的相关关系并进行LAI遥感制图,并在陆地覆盖类型遥感分类信息提取的基础上,发展了针叶林、混交林和空旷地三种地表类型LAI的向上空间尺度转换算法,以对粗分辨MODIS遥感数据的LAI产品实现LAI算法的转换与校正,并通过示例应用显示了本研究空间尺度转换算法的有效性.     

Improvements of the MODIS terrestrial gross and net primary production global data set

MODIS primary production products (MOD17) are the first regular, near-real-time data sets for repeated monitoring of vegetation primary production on vegetated land at 1-km resolution at an 8-day interval. But both the inconsistent spatial resolution between the gridded meteorological data and MODIS pixels, and the cloud-contaminated MODIS FPAR/LAI (MOD15A2) retrievals can introduce considerable errors to Collection4 primary production (denoted as C4 MOD17) results. Here, we aim to rectify these problems through reprocessing key inputs to MODIS primary vegetation productivity algorithm, resulting in improved Collection5 MOD17 (here denoted as C5 MOD17) estimates. This was accomplished by spatial interpolation of the coarse resolution meteorological data input and with temporal filling of cloud-contaminated MOD15A2 data. Furthermore, we modified the Biome Parameter Look-Up Table (BPLUT) based on recent synthesized NPP data and some observed GPP derived from some flux tower measurements to keep up with the improvements in upstream inputs. Because MOD17 is one of the down-stream MODIS land products, the performance of the algorithm can be largely influenced by the uncertainties from upstream inputs, such as land cover, FPAR/LAI, the meteorological data, and algorithm itself. MODIS GPP fits well with GPP derived from 12 flux towers over North America. Globally, the 3-year MOD17 NPP is comparable to the Ecosystem Model-Data Intercomparison (EMDI) NPP data set, and global total MODIS GPP and NPP are inversely related to the observed atmospheric CO₂ growth rates, and MEI index, indicating MOD17 are reliable products. From 2001 to 2003, mean global total GPP and NPP estimated by MODIS are 109.29 Pg C/year and 56.02 Pg C/year, respectively. Based on this research, the improved global MODIS primary production data set is now ready for monitoring ecological conditions, natural resources and environmental changes.     

A practical split‐window algorithm for retrieving land‐surface temperature from MODIS data

This paper presents a practical split‐window algorithm utilized to retrieve land‐surface temperature (LST) from Moderate‐resolution Imaging Spectroradiometer (MODIS) data, which involves two essential parameters (transmittance and emissivity), and a new method to simplify Planck function has been proposed. The method for linearization of Planck function, how to obtain atmosphere transmittance from MODIS near‐infrared (NIR) bands and the method for estimating of emissivity of ground are discussed with details. Sensitivity analysis of the algorithm has been performed for the evaluation of probable LST estimation error due to the possible errors in water content and emissivity. Analysis indicates that the algorithm is not sensitive to these two parameters. Especially, the average LST error is changed between 0.19–1.1°C when the water content error in the simulation standard atmosphere changes between ?80 and 130%. We confirm the conclusion by retrieving LST from MODIS image data through changing retrieval water content error. Two methods have been used to validate the proposed algorithm. Results from validation and comparison using the standard atmospheric simulation and the comparison with the MODIS LST product demonstrate the applicability of the algorithm. Validation with standard atmospheric simulation indicates that this algorithm can achieve the average accuracy of this algorithm is about 0.32°C in LST retrieval for the case without error in both transmittance and emissivity estimations. The accuracy of this algorithm is about 0.37°C and 0.49°C respectively when the transmittance is computed from the simulation water content by exponent fit and linear fit respectively.