Atmospheric corrections in the thermal infrared: global and watervapor dependent split-window algorithms-applications to ATSR and AVHRRdata

The split-window method is an appropriate way to perform atmospheric corrections of satellite brightness temperatures in order to retrieve the surface temperature. A climatological data set of 1761 different radio soundings, the TIGR database, has been used to develop two different split-window methods. A global quadratic (QUAD) method, with global coefficients to be applied on a worldwide scale, and a water vapor dependent (WVD) algorithm. The first method includes a quadratic term in the split-window equation that roughly accounts for the water vapor amount. The other method explicitly includes the water vapor amount in each split-window coefficient. When applied to the 1761 radio soundings, the latter method gives better results than the global one, especially when the surface emissivity is far from unity (0.95 or less) and when the water vapor reaches great values. Both algorithms have been tested on ATSR/ERSI and AVHRR/NOAA data over sea pixels. The QUAD algorithm gives correct results for simulations (the standard error is 0.2 K) and experimental data (the bias ranges from -0.1 to 0.4 K). The WVD algorithm appears to be more accurate for both simulations (the standard error is less than 0.1 K) and AVHRR experimental data when climatological water vapor contents are used (the bias ranges from -0.2 to 0.1 K)     

Improvements in the split-window technique for land surfacetemperature determination

Land surface temperature (LST) retrievals obtained from NOAA Advanced Very High Resolution Radiometer (AVHRR) are of considerable importance for climatic research. However, the accurate evaluation of LST from space has been severely limited because of the difficulty in separating atmospheric from surface effects as the surface cannot be modeled as a black-body radiator. With this goal in mind, a novel extension of the split-window technique is presented in which the atmospheric contribution to the radiance measured by the satellite is investigated by the ratioing of covariance and variance of the brightness temperatures measured in channels 4 and 5 of AVHRR/2. Furthermore, the contribution of emissivity is evaluated from coefficients that depend on the spectral emissivities in both thermal channels. Using a wide range of simulations from an atmospheric radiative transfer model it is shown that the proposed algorithm provides an estimate of LST, to within 0.4 K if the spectral surface emissivity is known, which is better than that given by the currently used split-window algorithms for LST determination. Also the limitations on algorithm accuracy are discussed considering different values of noise equivalent temperature. Finally the authors present the preliminary results obtained using the proposed method from AVHRR data over a semi-arid region-of Northwestern Victoria in Australia provided by CSIRO, and a mountainous region of Northeast of France acquired in the frame of Regio Klimat Projekt     

A generalized split-window algorithm for retrieving land-surfacetemperature from space

Proposes a generalized split-window method for retrieving land-surface temperature (LST) from AVHRR and MODIS data. Accurate radiative transfer simulations show that the coefficients in the split-window algorithm for LST must vary with the viewing angle, if the authors are to achieve a LST accuracy of about 1 K for the whole scan swath range (±55° from nadir) and for the ranges of surface temperature and atmospheric conditions over land, which are much wider than those over oceans. The authors obtain these coefficients from regression analysis of radiative transfer simulations, and they analyze sensitivity and error over wide ranges of surface temperature and emissivity and atmospheric water vapor abundance and temperature. Simulations show that when atmospheric water vapor increases and viewing angle is larger than 45°, it is necessary to optimize the split-window method by separating the ranges of the atmospheric water vapor, lower boundary temperature, and the surface temperature into tractable subranges. The atmospheric lower boundary temperature and (vertical) column water vapor values retrieved from HIRS/2 or MODIS atmospheric sounding channels can be used to determine the range for the optimum coefficients of the split-window method. This new algorithm not only retrieves land-surface temperature more accurately, but is also less sensitive to uncertainty in emissivity and to instrument quantization error     

单通道物理法反演海表温度的参数敏感性分析及验证

定量分析了反演海表温度的单通道物理法对海水比辐射率、海面风速、海水盐度、大气透过率、大气上下行辐射等参数的敏感性,发现海水比辐射率、大气透过率对算法精度影响较大,是单通道物理法反演海表温度的主要误差来源.在不同的波段,单通道物理法对参数敏感性也有较大差别,中红外波段的敏感性要小于热红外波段.为了验证单通道物理法的可行性、精度及参数敏感性分析的结果,选择墨西哥湾海域2009年全年夜间MODIS实测数据进行实验.结果表明,中红外波段的单通道物理法反演海表温度的精度高于热红外波段,达到MODIS劈窗算法海表温度标准产品同等精度,这与参数敏感性分析的结果一致.由于中红外波段单通道物理法精度较高,一方面,可以满足常规的业务观测需求,为海表温度反演提供新的技术手段;另一方面,可用来标定劈窗算法系数,弥补海洋现场观测站位空间分布不足的问题.     

Directional effects in a daily AVHRR land surface temperature dataset over Africa

Land surface temperature (LST) is a key indicator of the land surface state and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. Split-window algorithms have been used with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to estimate instantaneous LST for nearly 20 years. However, the low accuracy of LST retrievals associated with intractable variability has often hindered its wide use. In this study, we developed a six-year daily (day and night) NOAA-14 AVHRR LST dataset over continental Africa. By combining vegetation structural data available in the literature and a geometric optics model, we estimated the fractions of sunlit and shaded endmembers observed by AVHRR for each pixel of each overpass. Although our simplistic approach requires many assumptions (e.g., only four endmember types per scene), we demonstrate through correlation that some of the AVHRR LST variability can be attributed to angular effects imposed by AVHRR orbit and sensor characteristics, in combination with vegetation structure. These angular effects lead to systematic LST biases, including "hot spot" effects when no shadows are observed. For example, a woodland case showed that LST measurements within the "hot-spot" geometry were about 9 K higher than those at other geometries. We describe the general patterns of these biases as a function of tree cover fraction, season, and satellite drift (time past launch). In general, effects are most pronounced over relatively sparse canopies (tree cover <60%), at wet season sun-view angle geometries (principal plane viewing) and early in the satellite lifetime. These results suggest that noise in LST time series may be strongly reduced for some locations and years, and that long-term LST climate data records should be normalized to a single sun-view geometry, if possible. However, much work remains before these can be accomplished.     

A simplified method for estimating the total water vapor contentover sea surfaces using NOAA-AVHRR channels 4 and 5

A simplified method for estimating the total amount of atmospheric water vapor, W, over sea surfaces using NOAA-AVHRR Channels 4 and 5 is presented. This study has been carried out using simulated AVHRR data at 11 and 12 μm (with MODTRAN 3.5 code and the TIGR database) and AVHRR, PODAAC, and AVISO databases provided by the Louis Pasteur University (Strasbourg-France), NASA-NOAA, and Meteo France, respectively. The method is named linear atmosphere-surface temperature relationship (LASTR). It is based on a linear relationship between the effective atmospheric temperature in AVHRR Channel 4 and sea surface temperature. The LASTR method was compared with the linear split-window relationship (LSWR), which is based on a linear regression between W and the difference of brightness temperature measured in the same channels (ΔT=T4-TS). The results demonstrate the advantage of the LASTR method, which is capable of estimating W from NOAA-14 afternoon passes with a bias accuracy of 0.5 g cm^-2 and a standard deviation of 0.3 g cm^-2, compared with the W obtained by the AVISO database. In turn, a global bias accuracy of 0.1 g cm^-2 and a standard deviation within 0.6 g cm^-2 have been obtained in comparison with the W included in the PODAAC database derived from the special sensor microwave/imager (SSM/I) instrument     

Detection of Sea Surface Temperature (SST) Using Infrared Band Data of Advanced Very High Resolution Radiometer (AVHRR) in the Gulf of Finland

This paper presents the detection of sea surface temperature (SST) in the Gulf of Finland using infrared band data of Advanced Very High Resolution Radiometer (AVHRR). AVHRR imagery is evaluated as a main data source for monitoring SST as a measure of upwelling's dynamic. Sea surface effects (SSE), however, cause a temperature difference between the sea surface skin and water below the surface. Therefore, SSE is taken into account as one of the major error factors in the SST esimation. Further studies will be investigated using both AVHRR and MODIS in the future.     

Detection of Sea Surface Temperature (SST) and Salinity Using Thermal Infrared Data of AVHRR and MODIS in the Gulf of Bohai Sea of China

This paper presents the detection of sea surface temperature (SST) and salinity in the Gulf of Bohai Sea of China using thermal infrared (TIR) data of Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). Both AVHRR and MODIS imageries are evaluated as main data sources for monitoring SST as a measure of upwelling's dynamic. The relationship between SST and salinity in the area is also discussed during 1997-2000 derived from AVHRR data and then examined using MODIS data of 2000. The obtained results indicated that both AVHRR and MODIS are useful to detect SST and salinity in the study area.     

An overview of MODIS capabilities for ocean science observations

The Moderate Resolution Imaging Spectroradiometer (MODIS) will add a significant new capability for investigating the 70% of the Earth's surface that is covered by oceans, in addition to contributing to the continuation of a decadal scale time series necessary for climate change assessment in the oceans. Sensor capabilities of particular importance for improving the accuracy of ocean products include high SNR and high stability for narrow or spectral bands, improved onboard radiometric calibration and stability monitoring, and improved science data product algorithms. Spectral bands for resolving solar-stimulated chlorophyll fluorescence and a split window in the 4-μm region for SST will result in important new global ocean science products for biology and physics. MODIS will return full global data at 1-km resolution. The complete suite of Levels 2 and 3 ocean products is reviewed, and many areas where MODIS data are expected to make significant, new contributions to the enhanced understanding of the oceans' role in understanding climate change are discussed. In providing a highly complementary and consistent set of observations of terrestrial, atmospheric, and ocean observations, MODIS data will provide important new information on the interactions between Earth's major components     

Formulation and validation of simulated data for the Atmospheric Infrared Sounder (AIRS)

Models for synthesizing radiance measurements by the Atmospheric Infrared Sounder (AIRS) are described. Synthetic radiances have been generated for developing and testing data processing algorithms. The radiances are calculated from geophysical states derived from weather forecasts and climatology using the AIRS rapid transmission algorithm. The data contain horizontal variability at the spatial resolution of AIRS from the surface and cloud fields. This is needed to test retrieval algorithms under partially cloudy conditions. The surface variability is added using vegetation and International Geosphere Biosphere Programme surface type maps, while cloud variability is added randomly. The radiances are spectrally averaged to create High Resolution Infrared Sounder (HIRS) data, and this is compared with actual HIRS2 data on the NOAA 14 satellite. The simulated data under-represent high-altitude equatorial cirrus clouds and have too much local variability. They agree in the mean to within 1-4 K, and global standard deviation agrees to better than 2 K. Simulated data have been a valuable tool for developing retrieval algorithms and studying error characteristics and will continue to be so after launch.     

Detection of interannual vegetation responses to climaticvariability using AVIRIS data in a coastal savanna in California

Ecosystem responses to interannual weather variability are large and superimposed over any long-term directional climatic responses making it difficult to assign causal relationships to vegetation change. Better understanding of ecosystem responses to interannual climatic variability is crucial to predicting long-term functioning and stability. Hyperspectral data have the potential to detect ecosystem responses that are undetected by broadband sensors and can be used to scale to coarser resolution global mapping sensors, e.g., advanced very high resolution radiometer (AVHRR) and MODIS. This research focused on detecting vegetation responses to interannual climate using the airborne visible-infrared imaging spectrometer (AVIRIS) data over a natural savanna in the Central Coast Range in California. Results of linear spectral mixture analysis and assessment of the model errors were compared for two AVIRIS images acquired in spring of a dry and a wet year. The results show that mean unmixed fractions for these vegetation types were not significantly different between years due to the high spatial variability within the landscape. However, significant community differences were found between years on a pixel basis, underlying the importance of site-specific analysis. Multitemporal hyperspectral coverage is necessary to understand vegetation dynamics     

Land-surface temperature measurement from space: physicalprinciples and inverse modeling

To apply the multiple-wavelength (split-window) method used for satellite measurement of sea-surface temperature from thermal-infrared data to land-surface temperatures, the authors statistically analyze simulations using an atmospheric radiative transfer model. The range of atmospheric conditions and surface temperatures simulated is wide enough to cover variations in clear atmospheric properties and surface temperatures, both of which are larger over land than over sea. Surface elevation is also included as the most important topographic effect. Land covers characterized by measured or modeled spectral emissivities include snow, clay, sands, and tree leaf samples. The empirical inverse model can estimate the surface temperature with a standard deviation less than 0.3 K and a maximum error less than 1 K. A band in the region from 10.2 to 11.0 μm will usually give the most reliable single-band estimate of surface temperature. A band in either the 3.5-4.0-μm region or in the 11.5-12.6-μm region must be included for accurate atmospheric correction     

利用大洋浮标数据和NCEP再分析资料对FY-2C红外分裂窗通道的绝对辐射定标

介绍利用大洋浮标数据和NCEP再分析资料对FY-2C红外分裂窗通道进行在轨绝对辐射定标的方法,并选择了2006年10个时次的卫星数据进行辐射定标试验.将利用这种方法获得的定标结果与FY-2C卫星数据产品中提供的定标查找表进行比较分析,结果表明两套不同的定标系数反演的大气层顶(TOA)亮度温度的主要差别集中在云顶、冰雪覆盖区域等低温像元;而在常温区的陆表和海表像元定标结果差别较小,反演的TOA亮温差在2K左右.提出的替代定标方法可以极大地提高定标频次,为实现FY-2C红外分裂窗通道的实时绝对辐射定标提供了重要的方法基础.     

基于遥感反演的地球红外背景建模

模拟地球红外辐射背景的前提是要获取可靠的全球表面温度。利用卫星遥感数据可以反演得到较精确的地表温度。反演算法采用了应用广泛的分裂窗算法,并将反演结果绘制成了全球表面温度分布图。同时,建立了完整的地球红外辐射亮度模型,考虑不同地域、纬度和季节模式下的大气辐射亮度和大气衰减。通过遥感反演技术与红外辐射建模技术的结合,最终完成了任意波段全球红外辐射背景亮度的计算与图像生成。结果表明:模拟的地球红外辐射图像效果精细,真实反映了地球背景的红外辐射特征,在空间目标的热环境分析,以及空间红外探测与隐身技术的研究等方面具有重要的应用价值。     

Aerosol retrieval over land from AVHRR data-application foratmospheric correction

Correction of Advanced Very High Resolution Radiometer (AVHRR) imagery for the aerosol effect requires retrieval of the aerosol loading from the images. Two retrieval algorithms that were previously developed for Landsat are modified for the AVHRR. The methods determine the aerosol optical thickness over land surfaces from AVHRR band one data independently of ancillary information. The first method retrieves aerosols based on the atmospheric effect on the path radiance. This method requires the surface reflectance to be 0.02±0.01, which is found over forests in the red channel. Two techniques are used to screen an AVHRR scene for pixels that have this low reflectance. The qualifying requirements for these techniques are discussed, and the method is demonstrated to retrieve aerosol optical thicknesses to ~±0.1. The second method uses the change in contrast for several scenes to determine the change in the optical thickness between the scenes. A reference scene allows absolute determination. The method has an rms error of ~0.1     

基于卫星图像的火山灰云识别方法研究

大规模的火山灰云既会引起全球气候和环境系统的巨大变化,又会威胁航空安全。卫星遥感技术能够快速准确地获取大范围的空间变化信息,实现对火山灰云发生、扩散状况的识别和预警。首先阐述了火山灰云的光谱特征和常用的卫星图像类型;然后从紫外吸收法、模式识别法、分裂窗量温差和改进型分裂窗量温差算法方面系统地介绍了火山灰云的识别方法;最后对国内现有的基于卫星图像的火山灰云的研究进行了评述,并对其发展趋势进行了总结和讨论。     

Adjusting for Long-Term Anomalous Trends in NOAA's Global Vegetation Index Data Sets

The weekly 0.144 resolution global vegetation index from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) has a long history, starting late 1981, and has included data derived from Advanced Very High Resolution Radiometer (AVHRR) sensors onboard NOAA-7, -9, -11, -14, -16, -17, and -18 satellites. Even after postlaunch calibration and mathematical smoothing and filtering of the normalized difference vegetation index (NDVI) derived from AVHRR visible and near-infrared channels, the time series of global smoothed NDVI (SMN) still has apparent discontinuities and biases due to sensor degradation, orbital drift [equator crossing time (ECT)], and differences from instrument to instrument in band response functions. To meet the needs of the operational weather and climate modeling and monitoring community for a stable long-term global NDVI data set, we investigated adjustments to substantially reduce the bias of the weekly global SMN series by simple and efficient algorithms that require a minimum number of assumptions about the statistical properties of the interannual global vegetation changes. Of the algorithms tested, we found the adjusted cumulative distribution function (ACDF) method to be a well-balanced approach that effectively eliminated most of the long-term global-scale interannual trend of AVHRR NDVI. Improvements to the global and regional NDVI data stability have been demonstrated by the results of ACDF-adjusted data set evaluated at a global scale, on major land classes, with relevance to satellite ECT, at major continental regions, and at regional drought detection applications.     

南海海域海表面温度对低空大气波导数值模拟的影响研究

为了研究海表面温度(sea surface temperature,SST)对低空大气波导数值模拟的影响,针对南海海域基于天气研究与预报(weather research and forecasting,WRF)模式开展了不同SST对低空大气波导数值模拟的影响研究.结果表明:精确的SST对低空大气波导数值模拟影响最大,...     

Early Warning and Crop Condition Assessment Research

The Early Warning Crop Condition Assessment Project of AgRISTARS was a multiagency and multidisciplinary effort. Its mission and objectives were centered around development and testing of remote-sensing techniques that enhance operational methodologies for global crop-condition assessments. The project developed crop stress indicator models that provide data filter and alert capabilities for monitoring global agricultural conditions. The project developed a technique for using NOAA-n satellite advanced very-high-resolution radiometer (AVHRR) data for operational crop-condition assessments. This technology was transferred to the Foreign Agricultural Service of the USDA. The project developed a U. S. Great Plains data base that contains various meteorological parameters and vegetative index numbers (VIN) derived from AVHRR satellite data. It developed cloud screening techniques and scan angle correction models for AVHRR data. It also developed technology for using remotely acquired thermal data for crop water stress indicator modeling. The project provided basic technology including spectral characteristics of soils, water, stressed and nonstressed crop and range vegetation, solar zenith angle, and atmospheric and canopy structure effects.     

Atmospheric correction against algorithm for NOAA-AVHRR products:theory and application

Investigation of the effect of atmospheric constituents on NOAA Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared data is presented. The general remote sensing equation, including scattering, absorption, and bidirectional reflectance effects for the AVHRR solar bands, is described. The magnitude of the atmospheric effects for AVHRR solar bands with respect to their impact on the normalized difference vegetation index (NDVI) and the surface bidirection reflectance is examined. Possible approaches for acquiring atmospheric information are discussed, and examples of atmospheric correction of surface reflectance and NDVI are given. Invariant effects (ozone absorption and molecular scattering) and variant effects (water vapor absorption and aerosol scattering) are shown to dominate the atmospheric effects in the AVHRR solar bands