EOS—MODIS数据在青藏高原冰雪季节性变化信息自动提取中的应用研究

冰雪的动态变化是环境变化的重要指标,利用地球系统中分辨率成像光谱仪（EOS-MODIS)数据监测冰雪的季节变化是目前国土国际上领域研究的重要方向之一。本利用中国科学院地理科学与资源研究所全球变化信息研究中心“中美联合EOS－MODIS地面站”的数据,选择青藏高原东部工布江达附近常年积雪区为试点地区,通过对2001年4月,6月和7月等三个不同时相EOS－MODIS数据的处理和分析,探讨利用EOS－MODIS自动提取冰雪空间分布数据的方法,研究结果表明,利用EOS－MODIS可见光,近红外,热红外通道提取冰雪的空间分布和季节变化信息是可行的。     

基于EOS/MODIS 的云雾光学厚度和有效粒子半径反演研究

使用SBDART 辐射传输模式模拟EOS/MODIS 可见光和近ö短波红外通道的光学特性, 分析了使用这些通道进行云雾光学厚度和有效粒子半径反演的可行性, 建立了不同条件下的正演模拟辐射数据库, 在此基础上反演云雾光学厚度和有效粒子半径。敏感性研究表明, 不同波长的近/短波红外波段反射率对不同高度上的粒子敏感, 使用不同通道组合反演所得的有效粒子半径反映了云层不同高度上的粒子尺度特征。结合卫星数据和常规资料做了实例分析, 分析结果表明, 反演结果具有合理性。     

利用EOS/MODIS数据反演水云云底高度的初步研究

云底高度作为重要的云宏观物理特征参数,在云层与地表之间的能量交换中起着重要作用。传统的云底高度测量方法大多基于常规观测资料,利用星载被动遥感仪器的观测数据反演云底高度在国内尚未开展。论述了基于EOS/MODIS可见光、红外数据反演云底高度的原理、方法和可行性,并结合西北某空域的飞机探测数据进行了MODIS水云云底高度反演的对比试验。初步结果表明：利用MODIS数据反演水云的云底高度是可行的;在与3次飞机穿云记录的云高真实数据对比中,反演结果平均误差为249.4 m。     

EOS/MODIS 遥感资料探测海洋赤潮信息方法

近几年来, 我国沿海赤潮的发生越来越频繁, 已经成为一种常见海洋灾害。它的发生给沿海经济、居民生活和生态系统造成了很大影响。通过分析赤潮水体及其周边水体的光谱特性, 以及赤潮发生期间海水叶绿素a 浓度的变化特点, 提出了利用EOS/ MODIS 通道4 与通道3 的反射率比和通道11 与通道9 的离水辐射率比再结合相关的悬浮泥沙信息提取海水中赤潮信息的方法。利用此方法, 对2002 年6 月15 日和2004 年5 月31 日发生在我国渤海的赤潮进行了信息提取。结果表明此方法可以有效地提取海水中的赤潮信息。     

基于Landsat 8 TIRS数据的大气水汽含量反演劈窗算法

Landsat 8是2013年最新发射的Landsat卫星,携带了OLI和TIRS两个传感器,其中TIRS传感器获取了两个临近的热红外通道信息。劈窗协方差—方差比算法(SWCVR)是一种最通用的基于热红外的大气水汽含量反演方法,利用两个热红外通道(其中一个在大气窗口,另一个在大气吸收谱段)的吸收差异来反演大气水汽含量,该方法已经在MODIS等中低分辨率(1km)的热红外数据上得到很好的应用。将SWCVR算法移植到较高分辨率的Landsat 8TIRS数据上,并对水汽含量反演结果进行精度验证。气象数据验证结果表明,水汽含量的反演精度可以达到0.43g/cm~2。用MODIS水汽产品(MOD05)做交叉验证,反演的水汽含量和MOD05水汽含量的均方根误差(RMSE)为0.44g/cm~2,平均绝对误差(MAE)为0.34g/cm~2。总的来说,SWCVR算法应用于Landsat 8数据的水汽含量反演也能得到一个较高的精度。     

多时相MODIS数据融合效果的分析与评价

MODIS是美国新一代地球观测卫星EOS计划中重要的传感器之一,其主要目标是实现对大气和地球环境变化的长期观测和研究,因此,充分挖掘并利用MODIS数据信息十分必要。采用IHS变换法、IHS_FFT法及IHS_WT法等多种方法对MODIS多时相数据进行融合比较,并从反映图像亮度、空间细节和光谱信息的三类统计参数给出客观评价分析结果。结果表明,IHS_WT方法的融合效果较优,特别是采用双正交小波基（bior6.8）,而IHS和IHS_FFT法存在一定得光谱扭曲现象。     

MODIS遥感数据特征及其压缩技术研究

MODIS是EOS计划中新一代光学遥感仪器,广泛应用于地球长期观测、地球环境监测、自然灾害监测方面.首先简要地概述EOS及MODIS特性,接着分析MODIS数据特征,总结当前出现的各类MODIS数据压缩技术,最后介绍几种典型算法.     

利用EOS／MODIS数据反演水云云底高度的初步研究

云底高度作为重要的云宏观物理特征参数,在云层与地表之间的能量交换中起着重要作用。传统的云底高度测量方法大多基于常规观测资料,利用星载被动遥感仪器的观测数据反演云底高度在国内尚未开展。论述了基于EOS／MODIS可见光、红外数据反演云底高度的原理、方法和可行性,并结合西北某空域的飞机探测数据进行了MODIS水云云底高度反演的对比试验。初步结果表明：利用MODIS数据反演水云的云底高度是可行的;在与3次飞机穿云记录的云高真实数据对比中,反演结果平均误差为249．4m。     

基于高空间分辨率的热污染遥感监测研究进展

综述了卫星遥感热红外海表温度反演原理,并对海表温度中的“皮温”和“体温”进行了界定;着重总结了热红外遥感海表温度反演的主要方法-单通道法、分裂窗法、多角度法等;详细介绍了具有较高热红外通道空间分辨率卫星传感器Landsat TM/ETM+、CBERS-02 IRMSS、ASTER、HJ-1B IRS的海表温度反演算法,并对各卫星热红外通道特点进行了对比;最后,对影响热红外温度反演精度的因素进行了简要分析,指出了目前研究存在的问题并对未来的研究方向进行了展望。     

利用EOS/MODIS资料监测森林火情

分析了MODIS适于火情监测的各通道特性,并基于今年的MODIS资料,运用红外通道及三通道合成的方法,成功地探测到今年发生在我国大兴安岭及境外的多起火情。结果表明,利用红外通道探测法时,将21通道作为红外辐射探测火情的通道能够得到较好的效果。利用三通道合成法时,火灾区域与周边的地物颜色差异较大,可以快速地发现火情,并估计火情的发展趋势。     

基于MODIS多通道合成的江淮暴雨云系分析方法

卫星多通道合成能直观反映出卫星云图上的一些特性,通过采用经过预处理后的MODIS多通道数据,利用“白天自然色”、“白天微物理”、“白天太阳”和“空气团”4种多通道组合,结合实况降水分布和地面气象观测站点资料,以2010年7月13日江淮流域的一次特大暴雨过程为例,定性分析暴雨云系微观物理性质,推断云粒子大小和相态等,并对比高时空分辨率的局地分析预报系统(LAPS)中尺度物理量场以及不同研究个例的多通道合成图分析。结果表明：卫星多通道RGB合成图能以色彩的形式有针对性地突出对流系统、云粒子微观物理性质等属性,具有一定的精确度和普适性,有利于暴雨等中尺度强对流天气的监测。     

Land surface temperature and emissivity retrieval by integrating MODIS data onboard Terra and Aqua satellites

This paper presents an algorithm to retrieve land surface temperature (LST) and emissivity by integrating MODIS (Moderate Resolution Imaging Spectroradiometer) data onboard Terra and Aqua satellites. For a study area, there will be four pairs of day and night observations by MODIS onboard two satellites every day. Solar zenith angle, view zenith angle, and atmospheric water vapour have first been taken as independent variables to analyse their sensitivities to the same infrared channel measurements of MODIS on both Terra and Aqua satellites. Owing to their similar influences on the same MODIS band from Terra and Aqua satellites, four pairs of MODIS data from Terra and Aqua satellites can be thought of as MODIS measurement on a satellite at different viewing angles and viewing time. Comparisons between the retrieved results and in-situ measurements at three test sites (Qinghai Lake, Poyang Lake and Luancheng in China) indicate that the root mean square (rms) error is 0.66 K, except for the sand in Poyang Lake area. The rms error is less than 0.7 K when the retrieved results are compared with Earth Observing System (EOS) MODIS LST data products using the physics-based day/night algorithm. Emissivities retrieved by this algorithm are well compared to EOS MODIS emissivity data products (V5). The proposed algorithm can therefore be regarded as complementary and an extension to the EOS physics-based day/night algorithm.     

MODIS图象的云检测及分析

云一直是遥感图象处理、图象分析的一大障碍.为了解决这一问题,试图探讨利用中分辨率成像光谱仪MODIS检测云的方法,该方法充分考虑到MODIS数据具有36个光谱通道,特别是红外波段细分的特点,先是基于云的波谱特性采用多光谱综合法、红外差值法及指数法来对MODIS图象上的云点进行检测,鉴于这些方法有一定的局限性,因而还运用了一种基于空间结构分析和神经网络的云自动检测算法;最后将各种方法的云检测结果进行相互映证和对照分析,结果表明,这些方法检测到的云互相吻合,说明利用MODIS图象可成功地检测云点像元.这不仅为云的去除奠定了良好基础,而且也可以提高图象识别、图象分类及图象反演的精度.     

EOS-MODIS数据在我国农作物监测中的应用

中分辨率成像光谱仪（MODIS：Moderate Resolution Imaging Spectroradiometer）是美国1999年开始的第二阶段对地观测系统计划（EOS：Earth Observation System）中最有特色的仪器之一。与AVHRR相比，MODIS数据具有36个波段和高分辨率（250-1000m），加上数据以每天上、下午的频率采集和免费接收的数据获取政策，使得MODIS数据成为我国地学研究不可多得的数据资源。在概要介绍了MODIS应用于作物监测的原理和主要指标后，总结了我国在近几年利用该数据源进行作物监测（包括长势监测、面积监测、作物估产和灾害监测等）研究方面的进展。提出应用MODIS进行作物监测需要加强研究的几个方面。     

多源卫星绿潮遥感信息提取易混淆因素分析

针对绿潮遥感信息提取过程中容易出现的几种易混淆因素,开展了多源卫星绿潮遥感信息提取易混淆因素分析研究。基于多源遥感卫星图像,分析了光学和微波遥感数据在提取绿潮过程中常见的几种易混淆因素。结果发现：（1）HJ 1卫星CCD遥感影像上,岛屿、船只、堤坝、云都是易混淆因素。在信息提取中,需结合基础地理资料或“天地图”,将岛屿识别出来,此方法同样适用于MODIS和SAR数据。对于堤坝、船只和有云覆盖的绿潮区域,则需要通过人机交互的方式进行识别。（2）MODIS遥感影像中散布的小面积云和条带噪声是易混淆因素,因此需在MODIS数据预处理中进行云掩膜和条带噪声去除。（3）ENVISAT ASAR遥感影像中船只是易混淆因素,需通过人机交互的方式进行区分。     

Classification-based emissivity for land surface temperature measurement from space

Classification-based global emissivity is needed for the National Aeronautics and Space Administration Earth Observing System Moderate Resolution Imaging Spectrometer (NASA EOS/MODIS) satellite instrument land surface temperature (LST) algorithm. It is also useful for Landsat, the Advanced Very High Resolution Radiometer (AVHRR) and other thermal infrared instruments and studies. For our approach, a pixel is classified as one of fourteen 'emissivity classes' based on the conventional land cover classification and dynamic and seasonal factors, such as snow cover and vegetation index. The emissivity models we present provide a range of values for each emissivity class by combining various spectral component measurements with structural factors. Emissivity statistics are reported for the EOS/MODIS channels 31 and 32, which are the channels that will be used in the LST split-window algorithm.     

Satellite-observed features of a mesoscale convective complex over SE Europe

On 24 May 2009, a large size mesoscale convective system crossed south-eastern Europe causing severe weather, heavy precipitation, and strong wind. The system met both spatial and duration criteria of a mesoscale convective complex (MCC). The case was analysed using different image processing techniques based on the high spectral resolution of Meteosat Second Generation (MSG) satellite data. First, an automatic cloud tracking algorithm was applied on successive infrared images to objectively characterize the life cycle of the MCC and analyse the temporal evolution of several morphological, positional, and spectral parameters. Then, satellite data were processed and visualized as single channel, channel differences, RGB (Red-Green-Blue) composite, and ‘blended multi-layer’ images to reveal important information on the development and cloud-top structure and microphysics of the MCC. Lightning data were also used as a measure of intense convective activity in the MCC. The MCC exhibited extraordinary characteristics from the satellite point of view related to its evolution, vertical development, and structure. The results of this study showed how the use of different processing methods of multispectral MSG imagery can provide valuable information in both studying and nowcasting MCCs.     

The reconstruction of abnormal segments in HJ-1A/B NDVI time series using MODIS: a statistical method

HJ-1A/B is the first small satellite constellation built by China for environmental and disaster monitoring and forecasting. The satellite group has a 2-day repetition cycle and 30 m spatial resolution (charge coupled device camera). Thus, HJ-1A/B can provide hyper-temporal normalized difference vegetation index (NDVI) time series with a medium–high spatial resolution. However, the quality of the HJ NDVI time series can be abnormally low due to a number of factors, such as cloud cover, continuous fog, and haze. In the rainy season or in areas with serious atmospheric pollution, low-quality series often appear in succession, which is referred to as an abnormal segment. Neither the composition method nor quality flags satisfactorily solve this problem; therefore, a large amount of noise and long periods of abnormally low values often remain in HJ NDVI time series. This article presents a method to reconstruct the abnormal segments in HJ NDVI time series with the assistance of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series. The cointegration test was adopted to decide whether MODIS can be used for the reconstruction of NDVI time series for the corresponding HJ image pixels. Statistical quality control methods were used for singling out the abnormal segments in the HJ NDVI time series and establishing an error correction model that combines MODIS and HJ NDVI time series to perform the reconstruction. The study area is located in Jiangsu Province, China. Four-year (2009–2012) HJ multispectral images that cover the study area were used. The results show that abnormal segments in the HJ NDVI time series can be corrected using the proposed method. In a particular year, this method can decrease the root mean square error between the HJ NDVI time series and the reference sequence by 52.5%.     

Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset

In monsoon Asia, optical satellite remote sensing for rice paddy phenology suffers from atmospheric contaminations mainly due to frequent cloud cover. We evaluated the quality of satellite remote sensing of paddy phenology: (1) through continuous in situ observations of a paddy field in Japan for 1.5 years, we investigated phenological signals in the reflectance spectrum of the paddy field; (2) we tested daily satellite data taken by Terra/Aqua MODIS (MOD09 and L1B products) with regard to the agreement with the in situ data and the influence of cloud contamination. As a result, the in situ spectral characteristics evidently indicated some phenological changes in the rice paddy field, such as irrigation start, padding, heading, harvest and ploughing. The Enhanced Vegetation Index (EVI) was the best vegetation index in terms of agreement with the in situ data. More than 65% of MODIS observations were contaminated with clouds in this region. However, the combined use of Terra and Aqua decreased the rate of cloud contamination of the daily data to 43%. In conclusion, the most robust dataset for monitoring rice paddy phenology in monsoon Asia would be daily EVI derived from a combination of Terra/MODIS and Aqua/MODIS.     

Interannual variability of smoke and warm cloud relationships in the Amazon as inferred from MODIS retrievals

Aerosol and cloud data from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Earth Observing System (EOS) Aqua are used to investigate interannual variability of smoke and warm cloud relationships during the dry-to-wet transition season (August-October) over the Amazon for two years and its association with meteorological conditions. In one year (2003), smoke aerosols are associated with an increase of cloud fraction and a decrease of cloud effective radius. These effects amplify the cooling at the surface and at the top of the atmosphere (TOA) caused by the aerosol extinction. However, in another year (2002) the cloud fraction decreases with increasing aerosol optical depth. Such a decrease of cloud fraction could offset the effect of increased reflection of solar radiation by the aerosols both at the surface and at TOA. The changes in radiative fluxes between these years would contribute to interannual changes of surface energy fluxes and radiative balance at the top of the atmosphere and influence variability of the wet season onset in the basin. In 2003, the atmosphere was more humid and less stable. These conditions may be relatively favorable for the activation of aerosol particles into cloud condensation nuclei and hence cloud droplets. In 2002, the clouds were less extensive and thinner in a relatively dry atmosphere and presumably dissipated more easily. This study suggests that the aerosol-cloud relation can be influenced by atmospheric structure and convective motions, in addition to changes in aerosols properties. An adequate characterization of aerosol-cloud relationship would require a longer time series of data that includes a variety of climate conditions. The caveat of this analysis is that differences in aerosol absorption and its vertical distribution may have contributed to the observed interannual change of smoke-cloud relationship but could not be determined due to lack of adequate measurements.