FY-2静止卫星红外通道的高光谱交叉定标

介绍利用高光谱探测器IASI和AIRS对FY-2静止气象卫星VISSR红外通道进行绝对交叉定标的方法,并且基于FY-2E业务运行以来的历史数据对该方案进行可靠性检验和误差分析。结果表明,基于两个独立仪器得到的高光谱定标结果间具有很好的一致性;高光谱定标的亮温偏差基本上呈正态随机分布没有明显的系统偏差,平均亮温偏差小于0.07K,标准差约1.4K,其中亮温大于290K时,平均偏差小于0.2K,亮温大于220K时,平均偏差小于1K,低温端平均偏差约为3K;本文方法与目前FY-2EL1文件中提供的定标结果相比有明显改善,精度平均提高大于1K,低温端2—3K。长时间统计分析结果证明文中采用高光谱交叉定标方案稳定可靠,精度能够满足定量应用需求。     

利用AIRS数据交叉辐射定标SVISSR分裂窗通道

利用高精度和稳定的AIRS/Aqua(Atmospheric InfraRed Sounder on board Aqua)数据对SVISSR/FY- 2C(Stretched Visible and Infrared Spin Scan Radiometer onboard FengYun 2C)的两个分裂窗通道IR1(InfraRed 1, 10.9 μm)和IR2(InfraRed 2, 11.9μm)进行交叉辐射定标的方法, 并利用赤道附近2006年12月和2007年12月的AIRS和SVISSR数据完成了交叉辐射定标, 结果表明, SVISSR数据与卷积得到的AIRS数据高度线性相关, SVISSR/FY-2C传感器的两个分裂窗通道不仅存在定标误差, 而且定标误差随时间的变化呈现增大的趋势。相对于AIRS/Aqua测量值, 当SVISSR的通道亮温从220 K变化到340 K时, 2006年12月IR1通道的温度调整量从5.8 K变化到-4.4 K, 而2007年12月IR1通道的温度调整量从6.9 K变化到-5.1 K; 2006年12月IR2通道的温度调整量从2.2 K变化到-1.5 K, 而2007年12月IR2通道的温度调整量从6.3 K变化到-6.1 K。     

应用卫星与气象数据及其关系研究黄河流域的荒漠化现状

本文应用20年（1981—2000年）的卫星数据反演归一化差值植被指数（NDVI）,同时获取地面格网的温度与降雨数据,并分析这些数据之间的关系。基于地面的温度和降雨格网数据将研究区划分为8个气候区域,再利用NDVI数据把降雨量最少的3个气候区——区1,2,3各划分为10个等级。此外,分析这3个气候区在1983—1998年15年间的NDVI变化状况,结果显示出研究区荒漠化状况的加剧。     

Spatial and Temporal Relationships among NDVI,Climate Factors,and Land Cover Changes in Northeast Asia from 1982 to 2009

This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact.     

用被动微波AMSR数据反演地表温度及发射率的方法研究

 针对对地观测卫星多传感器的特点，提出了借助MODIS地表温度产品从被动微波数据中反演地表温度的方法。即利用MODIS地表温度产品和AMSR不同通道之间的亮度温度，建立地表温度的反演方程。该方法克服了以往需要测量同步数据的困难，为不同传感器之间的参数反演相互校正和综合利用多传感器的数据提供实际应用和理论依据。文中以MODIS地表温度产品作为评价标准，对方法进行检验，其平均误差为2~3℃。另外，微波的发射率是土壤水分反演的关键参数，在对微波地表温度反演的基础上，进一步对发射率进行了研究。     

Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region

Using NOAA/AVHRR 10-day composite NDVI data and 10-day meteorological data, including air temperature, precipitation, vapor pressure, wind velocity and sunshine duration, at 19 weather stations in the three-river-source region in the Qinghai–Tibetan Plateau in China from 1982 to 2000, the variations of NDVI and climate factors were analyzed for the purpose of studying the correlation between climate change and vegetation growth as represented by NDVI in this region. Results showed that the NDVI values in this region gradually grew from the west to the east, and the distribution was consistent with that of moisture status. The growing season came earlier due to climate warming, yet because of the reduction of precipitation, maximal NDVI during 1982–2000 did not show a significant change. NDVI related positively to air temperature, vapor pressure and precipitation, but negatively related to sunshine duration and wind velocity. Furthermore, the response of NDVI to climate change showed time lags for different climate factors. Water condition and temperature were found to be the most important factors effecting the variation of NDVI during the growing season in both the semi-arid and the semi-humid areas. In addition, NDVI had a better correlation with vapor pressure than with precipitation. The ratio of precipitation to evapotranspiration, representing water gain and loss, can be regarded as a comprehensive index to analyze NDVI and climate change, especially in areas where the water condition plays a dominant role.     

三江源区植被 NDVI 对区域气候的时滞效应分析

以三江源区为研究区,主要利用一元线性趋势法和简单相关分析法分析了源区1982～2004年生长季累积NDVI的时间序列变化特征及其与气温、降雨、光照时间、风速、地表温度这些气候因子之间的相关性,从月尺度上研究了三江源区植被NDVI对气候因子响应的滞后性特征。最后表明,生长季累积NDVI对气温的滞后期为1个月,对风速的滞后期为2个月,对地表温度的滞后期为4个月,而对降雨量和日照时数不存在滞后响应或者滞后期小于1个月。     

雾霾与GPS对流层天顶延迟相关性探究

利用国际GNSS服务（International GNSS Service,IGS）提供的对流层天顶延迟（zenith path delay,ZPD）产品,研究其与雾霾的相关性,并探究了造成雾霾的“元凶”——悬浮颗粒物与气压、温度和湿度的变化关系。首先,研究了中国境内4个IGS站30 d的日平均ZPD与量化评定雾霾的空气质量指数（air quality index,AQI）的变化趋势,发现二者基本同步增大（减小）。内陆3个站点的相关系数绝对值均大于0.5,说明ZPD与表征雾霾的AQI有着较强的相关关系,雾霾对对流层延迟产生影响。其次,对1 h采样率的北京房山空气质量分指数（individual air quality index,IAQI）与ZPD进行分析,二者的变化趋势基本一致,其中PM2.5、PM10、AQI与ZPD的相关系数分别为0.504 2、0.539 1和0.555 4。同时,当AQI达到300以上重度污染时,会对ZPD产生5 cm以上差值的显著影响。最后,利用IGS的M文件探究了北京房山各IAQI与气压、温度、湿度24 h变化,一天中IAQI、气压、湿度均呈“U”变化趋势,而温度则呈现倒“U”变化,说明雾霾的形成与气压、温度、湿度相关,并利用逐步线性回归给出了概略模型。     

An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model

Significant advances have been achieved in generating soil moisture (SM) products from satellite remote sensing and/or land surface modeling with reasonably good accuracy in recent years. However, the discrepancies among the different SM data products can be considerably large, which hampers their usage in various applications. The bias of one SM product from another is well recognized in the literature. Bias estimation and spatial correction methods have been documented for assimilating satellite SM product into land surface and hydrologic models. Nevertheless, understanding the characteristics of each of these SM data products is required for many applications where the most accurate data products are desirable. This study inter-compares five SM data products from three different sources with each other, and evaluates them against in situ SM measurements over 14-year period from 2000 to 2013. Specifically, three microwave (MW) satellite based data sets provided by ESA's Climate Change Initiative (CCI) (CCI-merged, -active and -passive products), one thermal infrared (TIR) satellite based product (ALEXI), and the Noah land surface model (LSM) simulations. The in-situ SM measurements are collected from the North American Soil Moisture Database (NASMD), which involves more than 600 ground sites from a variety of networks. They are used to evaluate the accuracies of these five SM data products. In general, each of the five SM products is capable of capturing the dry/wet patterns over the study period. However, the absolute SM values among the five products vary significantly. SM simulations from Noah LSM are more stable relative to the satellite-based products. All TIR and MW satellite based products are relatively noisier than the Noah LSM simulations. Even though MW satellite based SM retrievals have been predominantly used in the past years, SM retrievals of the ALEXI model based on TIR satellite observations demonstrate skills equivalent to all the MW satellite retrievals and even slightly better over certain regions. Compared to the individual active and passive MW products, the merged CCI product exhibits higher anomaly correlation with both Noah LSM simulations and in-situ SM measurements.     

Remotely Sensed Phenology of Coffee and Its Relationship to Yield

Due to complex microclimatic interactions, a biannual phenological cycle, and the generally small scale of coffee plantations, there have been few applications of satellite observations to examine coffee yield. Using 2001-2006 data, surface precipitation and air temperature are related to MODIS surface temperature and fractional vegetation. Using lagged correlation analysis and deviations from the annual cycle, yield is related to accumulated deviations in fractional vegetation. Results imply that the coarse spatial resolution of MODIS data is compensated for by high temporal coverage, which allows for determination of coffee phenology.     

Urban aerosol mapping over Athens using the differential textural analysis (DTA) algorithm on MERIS-ENVISAT data

Low and moderate spatial resolution satellite sensors (such as TOMS, AVHRR, SeaWiFS) have already shown their capability in tracking aerosols at a global scale. Sensors with moderate to high spatial resolution (such as MODIS and MERIS) seem also to be appropriate for aerosol retrieval at a regional scale. We investigated in this study the potential of MERIS-ENVISAT data to resolve the horizontal spatial distribution of aerosols over urban areas, such as the Athens metropolitan area, by using the differential textural analysis (DTA) code. The code was applied to a set of geo-corrected images to retrieve and map aerosol optical thickness (AOT) values relative to a reference image assumed to be clean of pollution with a homogeneous atmosphere. The comparison of satellite retrieved AOT against PM₁₀ data measured at ground level showed a high positive correlation particularly for the AOT values calculated using the 5th MERIS’ spectral band (R²=0.83). These first results suggest that the application of the DTA code on cloud free areas of MERIS images can be used to provide AOT related to air quality in this urban region. The accuracy of retrieved AOT mainly depends on the overall quality, the pollution cleanness and the atmospheric homogeneity of the reference image.     

Parameterization of air temperature in high temporal and spatial resolution from a combination of the SEVIRI and MODIS instruments

Some applications, e.g. from traffic or energy management, require air temperature data in high spatial and temporal resolution at two metres height above the ground (T_2m), sometimes in near-real-time. Thus, a parameterization based on boundary layer physical principles was developed that determines the air temperature from remote sensing data (SEVIRI data aboard the MSG and MODIS data aboard Terra and Aqua satellites). The method consists of two parts. First, a downscaling procedure from the SEVIRI pixel resolution of several kilometres to a one kilometre spatial resolution is performed using a regression analysis between the land surface temperature (LST) and the normalized differential vegetation index (NDVI) acquired by the MODIS instrument. Second, the lapse rate between the LST and T_2m is removed using an empirical parameterization that requires albedo, down-welling surface short-wave flux, relief characteristics and NDVI data. The method was successfully tested for Slovenia, the French region Franche-Comté and southern Germany for the period from May to December 2005, indicating that the parameterization is valid for Central Europe. This parameterization results in a root mean square deviation RMSD of 2.0 K during the daytime with a bias of −0.01 K and a correlation coefficient of 0.95. This is promising, especially considering the high temporal (30 min) and spatial resolution (1000 m) of the results.     

FY-3B MWRI在轨辐射校准及其对旱涝灾害监测应用

根据中国风云气象卫星FY-3B MWRI与近年在轨业务运行的Aqua卫星AMSR-E的技术参数,基于晴空大气辐射传输模型的模拟,分析了技术参数入射角等对辐射亮度温度Tb(Brightness temperature)的影响。以此依据,对AMSR-E原始多通道Tb数据进行入射角修正。采用二次多项式拟合方法,用入射角修正后的AMSR-ETb数据对FY-3B MWRITb数据进行各通道的辐射校准,并进行了数据对比。基于辐射校准后的FY-3BMWRI各通道Tb特征,用极化与散射特征指数,对2011年长江流域两湖地区的连续发生的干旱、降雨、水涝灾害等进行了评估与分析,并与AMSR-E的对应结果及地面观测站的记录结果进行了一致性的比较。研究表明,这些分析结果与地面观测记录比对的结果是一致的,FY-3B MWRI可以有效地监测旱涝灾害。     

Snow permittivity retrieval inversion algorithm for estimating snow wetness

The environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) offers the opportunity for monitoring snow parameters with dual polarization and multi-incidence angle. Snow wetness is an important index for indicating snow avalanche, snowmelt runoff modelling, water supply for irrigation and hydropower stations, weather forecasts and understanding climate change. We used a first-order scattering model that includes both volume and air/snow surface scattering based on a developed inversion model to estimate snow dielectric constant, which can be further related for estimating snow wetness. Comparison with field measurement showed that the correlation coefficient for snow permittivity estimated from ASAR data was observed to be 0.8 at 95% confidence interval and model bias was observed as 2.42% by volume at 95% confidence interval. The comparison of ASAR-derived snow permittivity with ground measurements shows the average absolute error 2.5%. The snow wetness range varies from 0 to 15% by volume.     

Modelling and Remote Sensing of Land Surface Temperature in Turkey

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26?C45°E and 36?C42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eski?ehir, ?stanbul, ?zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST_ANN) and Becker and Li temperature(LST_B-L) method values have been found as 0.077?K and 0.091?K (training stations), 0.045?K and 0.003?K (testing stations), respectively.     

Satellite-observed changes in terrestrial vegetation growth trends across the Asia-Pacific region associated with land cover and climate from 1982 to 2011

The Asia-Pacific (AP) region has experienced faster warming than the global average in recent decades and has experienced more climate extremes, however little is known about the response of vegetation growth to these changes. The updated Global Inventory Modeling and Mapping Studies third-generation global satellite Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index dataset and gridded reanalysis climate data were used to investigate the spatiotemporal changes in both trends of vegetation dynamic indicators and climatic variables. We then further analyzed their relations associated with land cover across the AP region. The main findings are threefold: (1) at continental scales the AP region overall experienced a gradual and significant increasing trend in vegetation growth during the last three decades, and this NDVI trend corresponded with an insignificant increasing trend in temperature; (2) vegetation growth was negatively and significantly correlated with the Pacific Decadal Oscillation index and the El Niño/Southern Oscillation (ENSO) in AP; and (3) at pixel scales, except for Australia, both vegetation growth and air temperature significantly increased in the majority of study regions and vegetation growth spatially correlated with temperature; In Australia and other water-limited regions vegetation growth positively correlated with precipitation.     

黑河流域遥感物候产品验证与分析

植被物候遥感产品对全球变化响应、农业生产管理、生态学的应用等多领域研究具有重要意义。但现有植被物候遥感产品还有较多问题,主要包括一方面使用不同参数的时间序列数据以及不同提取算法导致的产品结果差异较大,另一方面在地面验证中地面观测数据与遥感反演数据的物理含义不一致导致的验证方法的系统性误差。本文以黑河流域为研究区,对比验证基于EVI(Enhanced Vegetation Index)时间序列数据提取的MLCD(MODIS global land cover dynamics product)植被遥感物候产品和基于LAI(Leaf Area Index)时间序列数据提取的UMPM(product by universal multi-life-cycle phenology monitoring method)植被遥感物候产品的有效性及精度等。同时,通过验证分析进一步评估基于EVI和LAI时间序列提取的物候特征的差异及特点,探讨由于地面观测植被物候与遥感提取植被物候的物理意义的不一致问题导致的直接验证结果偏差。结果表明:UMPM产品有效性整体高于MLCD产品,但在以草地和灌木为主的稀疏植被区,由于LAI取值精度的原因,UMPM产品存在较多缺失数据,且时空稳定性较低;基于玉米地面观测数据表明,EVI对植被开始生长的信号比LAI更加敏感,更适合提取生长起点,但植被指数易饱和,峰值起点普遍提前,基于LAI提取的峰值起点更加合理。由于地面观测的物候期在后期更加关注果实生长,遥感观测仅关注叶片的生长,遥感定义的峰值终点和生长终点与玉米的乳熟期和成熟期差异较大。     

25年来秦岭NDVI指数的气候响应

利用1982—2006年的植被指数和研究区域内4个气象站的气温、降水数据,研究陕西秦岭地区植被指数、气温、降水的多年变化趋势,分析植被指数与气温和降水的相关关系。利用植被类型数据分析不同植被种类的NDVI与不同气候因子的相关程度。结果表明,1982—2006年,研究区域年均气温有明显的上升,升幅达2.1℃,而年总降水量每10年下降约72 mm,秦岭地区NDVI略有上升。整体而言,植被指数的变化与气温之间的相关性在中部最大,向东西两侧递减;与降水之间的相关性在中部最小,向东西两侧递增。气温对果树园、经济林的影响最大,降水对阔叶林的影响最大。气温是影响该地区植被指数变化的主要因素。     

Quantifying water stress effect on daily light use efficiency in Mediterranean ecosystems using satellite data

ABSTRACT

The capacity of six water stress factors (ε′_i) to track daily light use efficiency (ε) of water-limited ecosystems was evaluated. These factors are computed with remote sensing operational products and a limited amount of ground data: ε′₁ uses ground precipitation and air temperature, and satellite incoming global solar radiation; ε′₂ uses ground air temperature, and satellite actual evapotranspiration and incoming global solar radiation; ε′₃ uses satellite actual and potential evapotranspiration; ε′₄ uses satellite soil moisture; ε′₅ uses satellite-derived photochemical reflectance index; and ε′₆ uses ground vapor pressure deficit. These factors were implemented in a production efficiency model based on Monteith’s approach in order to assess their performance for modeling gross primary production (GPP). Estimated GPP was compared to reference GPP from eddy covariance (EC) measurements (GPP_EC) in three sites placed in the Iberian Peninsula (two open shrublands and one savanna). ε′_i were correlated to ε, which was calculated by dividing GPP_EC by ground measured photosynthetically active radiation (PAR) and satellite-derived fraction of absorbed PAR. Best results were achieved by ε′₁, ε′₂, ε′₃ and ε′₄ explaining around 40% and 50% of ε variance in open shurblands and savanna, respectively. In terms of GPP, R²?≈?0.70 were obtained in these cases.     

Quantification of LST and CO2 levels using Landsat-8 thermal bands on urban environment

Land surface temperature (LST) is an important aspect in global to regional change studies, for control of climate change and balancing of high temperature. Urbanization is one of the influencing factors increasing land surface and atmospheric temperature, by the emission of greenhouse gases (e.g. CO₂, NO and methane). In the present study, LST was derived from Landsat-8 of multitemporal data sets to analyse the spatial structure of the urban thermal environment in relation to the urban surface characteristics and land use–land cover (LULC). LST is influenced by the greenhouse gases i.e. CO₂ plays an important role in increasing the earth’s surface temperature. In order to provide the evidence of influence of CO₂ on LST, the relationship between LST, air temperature and CO₂ was analysed. Landsat-8 satellite has two thermal bands, 10 and 11. These bands were used to accurately to calculate the temperature over the study area. Results showed that the strength of correlation between ground monitoring data and satellite data was high. Based on correlation values of each month April (R² = 0.994), May (R² = 0.297) and June (R² = 0.934), observed results show that band 10 was significantly correlating with air temperature. Relationship between LST and CO₂ levels were obtained from linear regression analysis. band 11 was correlating significantly with CO₂ values in each of the months April (R² = 0.217), May (R² = 0.914) and June, (R² = 0.934), because band 11 is closer to the 15-micron band of CO₂. From the results, it was observed that band 10 can be used for calculating air temperature and band 11 can be used for estimation of greenhouse gases.