2006年春季我国东部海域气溶胶光学厚度与沙尘天气

结合船基的太阳光度计观测资料和空基卫星遥感的MODIS气溶胶光学厚度资料,研究了我国东部海域气溶胶光学厚度与沙尘天气的关系。通过对不同天气条件下500 nm气溶胶光学厚度的分析,得出晴天(背景天气)、有云和浮尘以及只有浮尘时的平均值分别约为0.2、0.6和1.3以上;将MODIS的气溶胶光学厚度与船基观测资料进行对比之后发现,两者随时间的变化趋势非常一致,但前者在数值上明显偏高;利用订正后的MODIS资料,分析了2006年春季我国东部海域气溶胶光学厚度的时空分布特征,并与我国北方发生的沙尘天气进行了对比,发现两者之间关系密切。     

MODIS地表温度产品在青藏高原冻土模拟中的适用性评价

利用MODIS地表温度反演产品,以青藏高原为研究区域,通过单点、区域、模型3方面来验证MODIS地表温度产品在青藏高原冻土模拟中的适用性.通过69个气象站点观测的地表O cm温度数据与所在位置的MODIS地表温度数据比较,二者在时间序列上的变化趋势基本一致,但是平均误差较大.在区域验证中,实测地表温度数据采用经纬度海拔...     

撒哈拉地区沙尘气溶胶南北空间型分布特征及其成因

本次研究利用MODIS、CALIPSO等卫星观测资料以及MERRA-2再分析资料分析了2007–2017年撒哈拉地区气溶胶光学厚度的空间分布特征。结果表明,撒哈拉地区气溶胶光学厚度的空间分布具有明显的季节变化,夏季沙尘气溶胶光学厚度高值区位于撒哈拉北部地区,高达0.6以上;而冬季沙尘气溶胶光学厚度高值区位于撒哈拉南部地区,最大值约为0.5。此外,撒哈拉地区在不同季节的主要气溶胶类型均为沙尘,但在撒哈拉南部地区沙尘气溶胶光学厚度对总气溶胶光学厚度的贡献有明显的季节性差异。基于CALIPSO体积退偏比的研究结果表明,在撒哈拉南部地区,夏季人为气溶胶占比大,气溶胶粒子趋于球形,冬季气溶胶粒子的退偏比则明显高于夏季,粒子非球形程度更高。夏季撒哈拉北部地区位于脊前槽后的位置,以南地区近地面主要为偏西风,携带了大量水汽的气流由大西洋吹向撒哈拉地区,使撒哈拉南部地区进入雨季,增强了沙尘气溶胶的沉降,因此夏季撒哈拉地区沙尘气溶胶光学厚度分布北高南低;冬季高压控制着撒哈拉北部地区,撒哈拉南部地区近地面盛行偏东风,且冬季温度偏低,容易形成逆温,不利于沙尘气溶胶和局地污染物扩散,导致沙尘气溶胶光学厚度南高北低。此外,局地温度变化也进一步促进了撒哈拉地区沙尘气溶胶空间分布的季节性差异。     

MODIS地表温度产品在青藏高原连续多年冻土区的适用性分析

利用自动气象站观测的长波辐射计算得到的地表温度对MODIS地表温度(LST)产品在青藏高原中部连续多年冻土区的精度进行验证, 并利用具有较高空间分辨率的Landsat 5 TM和Landsat 7 ETM+反演的地表温度与MODIS LST产品进行了对比分析. 结果表明: 白天MODIS LST产品的平均绝对误差(MAE)和均方根误差(RMSE)分别约为3.42~4.41 ℃和4.41~5.29 ℃, 夜晚MODIS产品MAE和RMSE分别为2.15~2.90 ℃和3.05~3.78 ℃, 精度高于白天; MODIS LST与TM、ETM+反演的地表温度一致性较好, 相关系数分别达到0.85和0.95. 说明MODIS LST产品在连续多年冻土区的适用性较高, 是研究多年冻土地表热状况的一个非常好的数据源. 而且, 不同空间尺度的遥感数据之间一致性较好, 可考虑将多源遥感数据应用于多年冻土热状况监测研究.     

用GMS-5气象卫星遥测地面太阳总辐射

由GMS-5静止气象卫星测量的可见先通道的行星反照率。根据地球-大气系统的物理模式反演得到了地面的太阳辐射，该模式以平均气候模式和晴天的气溶胶光学厚度计算晴天的大气吸收、分子和气溶胶散射，其它情况下的散射由行星反照率和晴天的地表反照率推算而得。模式中考虑了水汽和气溶胶的变化对地表太阳辐射的影响。卫星反演的地表太阳辐射与地面观测结果的相关系数高达95%以上。日平均方差约为10％。从比较的结果来看。卫星反演的每时次地面太阳辐射的精度较已有的结果有所提高。     

多源遥感数据反演土壤水分方法

基于ASAR-APP影像数据和光学影像数据,根据水云模型研究了小麦覆盖下地表土壤含水量的反演方法。利用TM和MODIS影像构建的植被生物、物理参数与实测小麦含水量进行回归分析,发现TM影像提取的归一化水分指数(NDWI)反演精度较好,相关系数达到0.87。根据这一关系,结合水云模型并联立裸露地表土壤湿度反演模型,建立了基于多源遥感数据的土壤含水量反演模型和参数统一求解方案。反演结果表明:该方案可得到理想的土壤水分反演精度,并可控制参数估计的误差。反演土壤含水量和准同步实测数据的相关系数为0.9,均方根误差为3.83%。在此基础上,分析了模型参数的敏感性,并制作了研究区土壤缺水量分布图。     

基于光谱-环境随机森林回归模型的MODIS积雪面积比例反演研究

积雪面积比例（Fractional Snow Cover, FSC）数据能在亚像元尺度上定量的描述像元内积雪覆盖的程度,相比二值积雪面积数据可以更加精确地估计积雪覆盖的面积。基于机器学习的随机森林回归模型可以表示高维的非线性关系,可显著提高MODIS FSC的反演精度。采用随机森林回归模型结合光谱、环境信息构建了一个新的回归模型——光谱-环境随机森林回归（Spectral Environment Random Forest Regressor, SE-RFR）模型,用于MODIS数据反演中国区域的FSC。利用中国典型积雪区内由Landsat 8地表反射率数据获取的FSC数据作为参考值,对SE-RFR模型的反演精度进行评估。研究表明,利用“SE-RFR”获取的FSC数据RMSE、MAE分别为0.160、0.104,精度较高。此外,根据SE-RFR模型与未加入环境信息的随机森林回归（S-RFR）模型比较结果可知,加入环境信息的随机森林回归模型提高了FSC反演的精度,特别是在受环境信息影响较大的青藏高原地区,RMSE从0.200降低到0.181。最后,将SE-RFR模型与目前使用广泛的MODIS FSC反演模型FSC_NDSI、MODSCAG和SSEmod进行了比较,结果表明SE-RFR模型的RMSE与FSC_NDSI、MODSCAG和SSEmod模型的RMSE相比,平均RMSE分别提高了12.0%、8.3%和5.5%。总体来说,SE-RFR模型可以准确地提取MODIS FSC,对于区域乃至全球FSC产品制备具有广泛的应用前景。     

应用MODIS影像估测太湖水体悬浮物浓度

以太湖为研究区域,同步获取悬浮物浓度实测数据、水体反射光谱数据和MODIS卫星影像数据,构建基于中分辨率成像光谱仪(MODIS)的悬浮物遥感估测模型.为了削弱大气效应,对MODIS影像了进行了粗略大气纠正.通过悬浮物特征光谱分析,将MODIS各敏感波段及波段组合与悬浮物浓度实测值进行相关分析,并应用实测光谱数据进行验证.在此基础上,运用回归分析建立半经验反演模型,并对模型进行了评价和应用.研究结果表明,MODIS影像可以很好地对大型内陆湖泊的悬浮物浓度进行遥感估测.250 m波段2 500 m波段4与1 000 m波段14是探测悬浮物的敏感波段.波段组合上,500 m组合因子r4/r3、r4-r3估测悬浮物含量的精度很高,适于构建反演模型;1 000 m波段8、11、131、4的多元组合也是构建模型的较好选择(R2均不低于0.85).     

二连盆地宽频大地电磁法数据精细反演处理研究——以满都拉图地区的数据为例

基于二连盆地满都拉图地区的宽频大地电磁(BMT)数据,开展了精细反演处理研究,旨在提高数据的反演精度与效果,为其他地区BMT数据的精细反演处理提供示范与借鉴。通过研究区BMT数据精细反演处理研究,选择OCCAM反演方法与TM+TE数据模式,反演参数选择背景模型为二维移动平均电阻率模型、正则因子为0.4、第一层厚度为40 m时,有效提升了数据的反演准确度,为后续资料的精细解释奠定了基础。研究结果表明：利用已知钻孔或地震资料等先验信息,首先开展有针对性的反演方法、反演数据模式以及反演参数等适用性试验研究,是确保资料减少多解性、获得可靠反演精度与效果的关键。     

南海珊瑚礁光学浅水海域多光谱遥感水深反演

海洋水深信息对研究珊瑚礁海域资源与环境具有重要作用。南海珊瑚礁海域测深数据受多种条件限制施测困难,在时间与空间方面数量非常有限。文章针对南海岛礁海域以I类水体为主导的海水光学特性,以南沙群岛库归沙洲海域为例,使用Sentinel-2多光谱卫星遥感影像和同期过境的MODIS卫星数据,构建底质光谱,采用半分析半经验模型计算海水表面遥感反射率与海水叶绿素浓度,通过对数比值模型进行该地区光学浅水海域遥感水深反演分析,并进一步通过多时相反演水深融合提升精度。经与多波束实测水深数据验证,研究区域反演水深总体均方根误差和平均相对误差分别为2.68 m 和9.99%。该方法通过叶绿素浓度推演部分海水光学特性,可以从多光谱卫星影像中快速获取南海岛礁光学浅水海域初步水深信息,供相关海洋领域分析与应用。     

Impact of satellite soundings on the simulation of heavy rainfall associated with tropical depressions

The present study is carried out to examine the impact of temperature and humidity profiles from moderate resolution imaging spectroradiometer (MODIS) or/and atmospheric infrared sounder (AIRS) on the numerical simulation of heavy rainfall events over the India. The Pennsylvania State University–National Centre for Atmospheric Research fifth-generation mesoscale model (MM5) and its three-dimensional variational (3D-Var) assimilation technique is used for the numerical simulations. The heavy rainfall events occurred during October 26–29, 2005, and October 27–30, 2006, were chosen for the numerical simulations. The results showed that there were large differences observed in the initial meteorological fields from control experiment (CNT; without satellite data) and assimilation experiments (MODIS (assimilating MODIS data), AIRS; (assimilating AIRS data); BOTH (assimilating MODIS and AIRS data together)). The assimilation of satellite data (MODIS, AIRS, and BOTH) improved the predicted thermal and moisture structure of the atmosphere when compared to CNT. Among the experiments, the predicted track of tropical depressions from MODIS was closer to the observed track. Assimilation of MODIS data also showed positive impact on the spatial distribution and intensity of predicted rainfall associated with the depressions. The statistical skill scores obtained for different experiments showed that assimilation of satellite data (MODIS, AIRS, and BOTH) improved the rainfall prediction skill when compared to CNT. Root mean square error in quantitative rainfall prediction is less in the experiment which assimilated MODIS data when compared to other experiments.     

A SOAP Web Service for accessing MODIS land product subsets

Remote sensing data from satellites have provided valuable information on the state of the earth for several decades. Since March 2000, the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board NASA’s Terra and Aqua satellites have been providing estimates of several land parameters useful in understanding earth system processes at global, continental, and regional scales. However, the HDF-EOS file format, specialized software needed to process the HDF-EOS files, data volume, and the high spatial and temporal resolution of MODIS data make it difficult for users wanting to extract small but valuable amounts of information from the MODIS record. To overcome this usability issue, the NASA-funded Distributed Active Archive Center (DAAC) for Biogeochemical Dynamics at Oak Ridge National Laboratory (ORNL) developed a Web service that provides subsets of MODIS land products using Simple Object Access Protocol (SOAP). The ORNL DAAC MODIS subsetting Web service is a standard based way of serving satellite data that exploits a fairly established and popular Internet protocol to allow users access to massive amounts of remote sensing data. The Web service provides MODIS land product subsets up to 201 × 201 km in a non-proprietary comma delimited text file format. Users can programmatically query the Web service to extract MODIS land parameters for real time data integration into models, decision support tools or connect to workflow software. Information regarding the MODIS SOAP subsetting Web service is available on the World Wide Web (WWW) at .     

基于多尺度遥感数据估算地表通量的方法及其验证分析

地表水热通量(显热通量、潜热通量)的遥感估算在全球气候变化、水资源、生态环境等研究领域具有重要的应用价值.MODIS数据的空间分辨率较低(热红外波段星下点为1 km),而地球表面的几何物理属性又具有高度非均匀性,因而在实际应用中面临较严重的尺度问题.探讨了多源卫星数据(中高分辨率Landsat TM与中低分辨率MODIS)相结合佑算像元通量的2种方法,分别利用高分辫率的地表分类及植被指数信息在混合像元内部进行亚像元处理,以提高非均匀地表混合像元的通量估算精度.研究数据来自于2008年黑河流域综合实验获取的遥感数据和辅助数据,验证数据来自于实验期间获取的不同下垫面的地表通量数据,包括涡度相关(EC)数据,以及大孔径闪烁仪(LAS)数据.计算结果表明,2种方法皆可在下垫面不均匀或者地表类型较复杂的情况下得到比较明显的纠正效果,纠正后的通量与观测更加接近.相比之下,利用植被指数分解温度的方法适用性更广,纠正效果更好.在地面验证中,对比分析了EC和LAS数据在TM尺度和MO-DIS尺度通量验证的适用性.LAS数据测量尺度与MODIS卫星像元尺度相匹配,可以直接验证MODIS通量计算结果,EC数据虽然可以直接验证TM计算的通量,但与MODIS数据对比,还需要进行尺度转换,即先用EC验证TM通量,然后将TM通量降尺度,与MODIS进行对比.最后对利用LAS验证通量的不确定性进行了分析,发现图像中LAS测点的几何定位误差以及LAS测量路径中像元的选取都对验证结果有一定影响.     

MODIS在水文水资源中的应用与展望

MODIS是新一代遥感技术,其遥测数据与其他单独的遥感平台(如NOAA和Land Sat)所获得的数据相比,具有免费、较高时间分辨率(0.5d)、空间分辨率(250m)和光谱分辨率(波谱范围0.4～14μm,36个光谱通道)等优势和特点.详细介绍了国内外的研究现状,着重对MODIS在洪水过程和洪灾范围实时动态监测、冰川和积雪、降水、植被、土壤水分、蒸发、水文模型、水质等方面的应用和研究进展进行了评述,指出MODIS在水文水资源中具有广阔的应用前景.     

利用MODIS反演长江中游悬浮泥沙含量的初步研究

在众多卫星传感器中,中等分辨率成像光谱仪(MODIS)数据因其高的时间分辨率和中等的空间分辨率,对于水质研究具有自身的潜力和优越性.选取长江中游主河道武汉至宜昌段为例,利用MODIS 250 m波段数据定量反演了内陆河流悬浮泥沙的质量浓度.研究结果表明,地面实测的悬浮泥沙质量浓度与MODIS1,2波段的反射率组合(R1-R2)/(R1 R2)有很好的相关关系(相关系数 R2=0.72,样品数n=41),基于这种相关性建立了长江中游主河道武汉至宜昌段表层悬浮泥沙的遥感定量反演经验模型.     

Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

This study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship. The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model. It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.     

An approach for estimating MODIS reflectance at top-of-atmosphere at 667 and 678 nm from reflectance at 645 nm in turbid waters

Owing to the defaults of MODIS sensor at 667 and 678 nm, data loss may occur when water bodies are highly turbid. A model for estimating MODIS signals at 667 and 678 nm from the signals at 645 nm (MMSS) is developed and constructed here to estimate the MODIS reflectance at top-of-atmosphere at 667 and 678 nm from the reflectance at 645 nm in highly turbid waters. The model is calibrated and validated by a dataset which consists of a bio-optical dataset and six MODIS images collected from the Yellow River Estuary, Changjiang River Estuary, and Taihu Lake, China. It is discovered that the linear relationships between signals above water surface are still available at top-of-atmosphere in a small region, even if atmospheric absorption and scattering are present in the process of photon transmission. The MODIS band at 645 nm is the optimal band for constructing the MMSS model. It may be rational that reflectance at 667 and 678 nm at top-of-atmosphere cannot be recorded by MODIS sensors while the values are larger than 0.76. The MMSS model produces strong performances in retrieving reflectance at top-of-atmosphere at 667 and 678 nm from the reflectance at 645 nm in turbid waters, with an uncertainty of less than 1.21 %. By comparison with the uncertainty of 2–3 % associated with “noise equivalent reflectance”, the uncertainty in the MMSS algorithm predictions (<1.21 %) is not significant.     

Optical remote sensing of turbidity and total suspended matter in the Gulf of Gabes

Optical remote sensing was used to provide scientific information to support environmental management in the Gulf of Gabes that is located in the southeastern coast of Tunisia. This region is characterized by shallow continental shelf subjected to semi-diurnal tides. Industrial activities in this area since the early 1970s may have contributed to the degradation of the biodiversity of the ecosystem with eutrophication problems and disappearance of benthic and planktonic species. To assess the long-term effect of anthropogenic and natural discharges on the Gulf of Gabes, the optical environment of the coastal waters is assessed from in situ measurements of total suspended matter concentration (TSM), Secchi depth and turbidity (TU). This monitoring requires regular seaborne measurements (monthly), which is very expensive and difficult to obtain. The objective of the present study is the evaluation of the Moderate Resolution Imaging Spectrometer (MODIS) AQUA data compared with two sampling campaigns realized at the study area. To map turbidity data from MODIS images, a semi-empirical algorithm was applied at band 667 nm. This bio-optical algorithm has already been calibrated and validated on the Belgian coast. The validation of this algorithm on the Gulf of Gabes using in situ measurements of turbidity and remotely sensed turbidity obtained from MODIS imagery shows a correlation coefficient of 68.9%. Seasonal and annual average maps for TSM and TU were then computed over the Gulf of Gabes using MODIS imagery. The obtained results of TSM and TU from remotely sensed data are conformable with those obtained through the analysis of in situ measurements. Therefore, remote sensing techniques offer a better and efficient tool for mapping and monitoring turbidity over the whole region.     

Dynamic monitoring of drought using HJ-1 and MODIS time series data in northern China

From early November 2008 to February 2009, lack of rainfall led to severe drought in northern China. More than 9.3 million ha of wheat in six major crop production provinces, including Henan, Anhui, Shandong, Shanxi, Gansu, and Shaanxi, were hit by drought. Supported by Chinese HJ-1 satellite images together with NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, dynamic monitoring of the drought was conducted. HJ-1 CCD data with 30-m resolution were used to identify cropland information. Spatial–temporal variation of drought was detected using Vegetation Index and Water Index time series data derived from MODIS visible, infrared, and short-wave infrared bands. The influences of drought were classified into five levels based on MODIS-derived 8-day composite Anomaly Water Index (AWI) and field survey data. The results indicated that the drought deteriorated beginning in November 2008 and became most serious in late January 2009. HJ-1 data together with MODIS data proved to be valuable data sources for monitoring soil moisture and drought at a both regional and national scale.