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     

An optimization algorithm for separating land surface temperatureand emissivity from multispectral thermal infrared imagery

Land surface temperature (LST) and emissivity are important components of land surface modeling and applications. The only practical means of obtaining LST at spatial and temporal resolutions appropriate for most modeling applications is through remote sensing. While the popular split-window method has been widely used to estimate LST, it requires known emissivity values. Multispectral thermal infrared imagery provides us with an excellent opportunity to estimate both LST and emissivity simultaneously, but the difficulty is that a single multispectral thermal measurement with N bands presents N equations in N+1 unknowns (N spectral emissivities and LST). In this study, we developed a general algorithm that can separate land surface emissivity and LST from any multispectral thermal imagery, such as moderate-resolution imaging spectroradiometer (MODIS) and advanced spaceborne thermal emission and reflection radiometer (ASTER) data. The central idea was to establish empirical constraints, and regularization methods were used to estimate both emissivity and LST through an optimization algorithm. It allows us to incorporate any prior knowledge in a formal way, The numerical experiments showed that this algorithm is very effective (more than 43.4% inversion results differed from the actual LST within 0.5°, 70.2% within 1° and 84% within 1.5°), although improvements are still needed     

Impact of the atmospheric transmittance and total water vaporcontent in the algorithms for estimating satellite sea surfacetemperatures

Sea surface temperature (SST) algorithms for NOAA AVHRR data can determine SST with rms values of 0.7 K on a global basis. However, this figure is not compatible with the high accuracy of 0.3 K required by climate studies. Biases in the SST product, arising when the factors that increase the optical path-length (absorbents concentration in the atmosphere or viewing angles) are large, cause problems in the use of the split-window formulation for climate monitoring. The reason is that the split-window coefficients currently used are not adequate to cover for all the atmospheric variability. To show this, simulations of channels 4 and 5 of AVHRR/2 of NOAA-11 using a radiative transfer model have been made. The range of atmospheric conditions and surface temperatures introduced in the simulation covers the variability of these parameters on a worldwide scale. From these data, the authors present new split-window coefficients that take into account the atmospheric variability through the ratio of the channel transmittances, or else through the total water vapor content along the path. They also show, using simulated and actual data, that the proposed split-window algorithm has a real global character and represents an improvement over the conventional algorithms     

Thermal Land Surface Emissivity Retrieved From SEVIRI/Meteosat

The methodologies used by the Satellite Application Facility on Land Surface Analysis (Land SAF) for retrieving emissivity are presented here. In the first approach, i.e., the vegetation cover method (VCM), the land surface emissivity (EM) is computed for Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared channels and for the 3- to 14- range using information on the pixel fraction of vegetation cover (FVC). The VCM uses a lookup table, which takes into account the channel's spectral response function, and laboratory reflectance spectra for different materials. The accuracy of the VCM depends on the reliability of FVC and the land cover classification. The EM for SEVIRI split-window channels is primarily used as an internal product by Land SAF for land surface temperature (LST) estimations. However, sensitivity studies show that LST often fails to meet the required accuracy of 2 K over desert and semiarid regions, where the VCM is unable to model the EM spatial variability, which is mostly associated with soil composition. Moreover, it is also over such areas where the atmosphere is generally dry that the impact of EM uncertainties on LST is largest. A second approach to determine the EM for SEVIRI split-window channels is currently being tested. This methodology allows the simultaneous retrieval of LST and channel EMs with the assumption that the latter remain constant. The channel EMs are then averaged over a 22-day period to filter out the noise in the retrievals. A first analysis of the maps obtained for an area within Northern Africa shows spatial patterns with features also present in the surface albedo.     

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)     

A physics-based algorithm for retrieving land-surface emissivityand temperature from EOS/MODIS data

The authors have developed a physics-based land-surface temperature (LST) algorithm for simultaneously retrieving surface band-averaged emissivities and temperatures from day/night pairs of MODIS (Moderate Resolution Imaging Spectroradiometer) data in seven thermal infrared bands. The set of 14 nonlinear equations in the algorithm is solved with the statistical recession method and the least-squares fit method. This new LST algorithm was tested with simulated MODIS data for 80 sets of hand-averaged emissivities calculated from published spectral data of terrestrial materials in wide ranges of atmospheric and surface temperature conditions. Comprehensive sensitivity and error analysis has been made to evaluate the performance of the new LST algorithm and its dependence on variations in surface emissivity and temperature, upon atmospheric conditions, as well as the noise-equivalent temperature difference (NEΔT) and calibration accuracy specifications of the MODIS Instrument. In cases with a systematic calibration error of 0.5%, the standard deviations of errors in retrieved surface daytime and nighttime temperatures fall between 0.4-0.5 K over a wide range of surface temperatures for mid-latitude summer conditions. The standard deviations of errors in retrieved emissivities in bands 31 and 32 (in the 10-12.5 μm IR spectral window region) are 0.009, and the maximum error in retrieved LST values falls between 2-3 K     

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     

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.     

用HJ-1B卫星数据反演地表温度的修正单通道算法

目前用于地表温度反演的单通道算法主要针对窄视场传感器建立.HJ-1B卫星红外相机为宽视场传感器,其热红外通道(IRS4)观测天顶角可达±33°以上,在地表温度反演时必须剔除传感器观测角度的影响.以大气辐射传输模拟为基础,建立了基于传感器观测天顶角-大气函数系数的修正单通道算法.针对HJ-1B卫星与Terra卫星过境时间...     

基于MODIS数据的地表温度反演

利用MODIS影像数据,采用劈窗算法来反演安徽地区的地表温度。结合Sobrine、覃志豪等人提出的NDVITEM方法和地物监督分类方法,对地表比辐射率进行了估算,将反演结果与NASA的地表温度产品进行比较,平均误差在1 K左右。同时利用卫星过境当天从安徽省高密度自动监测站获取的实时数据对反演结果进行验证,发现反演温度与地面实测数据的曲线走势具有高度的一致性且有较高的相关性,能直观地反映安徽地区地表温度的空间分布。     

Microwave land emissivity calculations using AMSU measurements

Atmospheric parameter retrievals over land from Advanced Microwave Sounding Unit (AMSU) measurements, such as atmospheric temperature and moisture profiles, could be possible using a reliable estimate of the land emissivity. The land surface emissivities have been calculated using six months of data, for 30 beam positions (observation zenith angles from -58/spl deg/ to +58/spl deg/) and the 23.8-, 31.4-, 50.3-, 89-, and 150-GHz channels. The emissivity calculation covers a large area including Africa, Eurasia, and Eastern South America. The day-to-day variability of the emissivity is less than 2% in these channels. The angular and spectral dependence of the emissivity is studied. The obtained AMSU emissivities are in good agreement with the previously derived SSMI ones. The scan asymmetry problem has been evidenced for AMSU-A channels. And possible extrapolation of the emissivity from window channels to sounding ones has been successfully tested.     

A Practical Method for Retrieving Land Surface Temperature From AMSR-E Over the Amazon Forest

Remote sensing of land surface temperature (LST) using infrared (IR) sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is only capable of retrieval under clear-sky conditions. Such LST observations over tropical forests are very limited due to clouds and rainfall, particularly during the wet season and high atmospheric water-vapor content. In comparison, low-frequency microwave radiances are minimally influenced by meteorological conditions. Exploring this advantage, we have developed an algorithm to retrieve LST over the Amazonian forest. The algorithm uses multifrequency polarized microwave brightness temperatures from the Advanced Microwave Scanning Radiometer on NASA's Earth Observing System (AMSR-E). Relationships between polarization ratio and surface emissivity are established for forested and nonforested areas, such that LST can solely be calculated from microwave radiance. Results are presented over three time scales: at each orbit, daily, and monthly. Results are evaluated by comparing with available air-temperature records on daily and monthly intervals. Our findings indicate that the AMSR-E-derived LST agrees well with in situ measurements. Results during the wet season over the tropical forest suggest that the AMSR-E LST is robust under all-weather conditions and shows higher correlation to meteorological data (r = 0.70) than the IR-based LST approaches (r = 0.42).     

Accurate atmospheric correction of ASTER thermal infrared imagery using the WVS method

The water vapor scaling (WVS) method involves an atmospheric correction algorithm for thermal infrared (TIR) multispectral data, designed mainly for the five TIR spectral bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the Terra satellite. First, this method is improved for better applicability to ASTER/TIR imagery. The major improvement is the determination of a water vapor scaling factor on a band-by-band basis, which can reduce most of the errors induced by various factors such as algorithm assumptions. Next, the WVS method is validated by assessing the surface temperature and emissivity retrieved for a global-based simulation model (416 448 conditions), 183 ASTER scenes selected globally, and ASTER scenes from two test sites, Hawaii Island and Tokyo Bay. In situ lake surface temperatures measured in 13 vicarious calibration experiments, Moderate Resolution Imaging Spectroradiometer sea surface temperature products, and a climatic lake temperature are also used in validation. All the results indicate that although the ASTER/TIR standard atmospheric correction algorithm performs less well in humid conditions, the WVS method will provide more accurate retrieval of surface temperature and emissivity in most conditions including notably humid conditions. The expected root mean square error is about 0.6 K in temperature. Since the WVS method will be degraded by errors in gray pixel selection and cloud detection, these processing steps should be applied accurately.     

Rapid radiative transfer model for AMSU/HSB channels

The atmospheric transmittance model for the Advanced Microwave Sounding Unit-A (AMSU-A) and the Humidity Sounder for Brazil (HSB) channels on the Aqua spacecraft uses a polynomial approximation to the temperature dependence of oxygen-band opacity within atmospheric layers. It uses lookup tables to calculate local water-vapor line intensity and pressure-broadening parameters as well as contributions to absorption from the water-vapor continuum, distant lines, and cloud liquid water. The algorithm includes water-line self-broadening and the magnetic-field effect on AMSU-A channel 14. The microwave surface emission model is based on a preliminary classification of the surface type, with subsequent adjustments to the emissivity spectrum that are obtained from the retrieval algorithm. A simple approximate correction for surface nonspecularity is included. The algorithm has been tested by comparisons to a line-by-line calculation and to measurements made by the NOAA-15 AMSU-A.     

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     

地物目标热红外光谱发射率的野外测量方法研究

在航天红外遥感应用中,地物目标光谱发射率是卫星遥感测量地面温度的一个重要参数。野外测量的大气环境、目标背景和地物的热力学特性等因素的影响,使得野外测量地物目标表面光谱发射率变得较为复杂。重点讨论了利用傅里叶变换红外光谱仪野外测量地物目标光谱发射率的方法和程序,介绍了几种正确分离目标温度与发射率的方法。野外测量实验结果表明,按照文中所述的测量方法,测量得到的地物热红外光谱发射率具有良好的一致性,发射率测量误差小于0．02。     

Bayesian-based subpixel brightness temperature estimation from multichannel infrared GOES radiometer data

In this paper, a new image reconstruction scheme is devised for estimating a high-resolution temperature map of the top of the Earth's atmosphere using the Geostationary Operational Environmental Satellite (GOES) imager infrared channels 4 and 5. By simultaneously interpolating the image while estimating temperature, the proposed algorithm achieves a more accurate estimate of the subpixel temperatures than could be obtained by performing these operations independently of one another. The proposed algorithm differs from other Bayesian-based image interpolation schemes in that it estimates brightness temperature as opposed to image intensity and incorporates a detailed optical model of the GOES multichannel imaging system. In order to test the effectiveness of the proposed technique, high-resolution estimates of cloudtop temperatures using GOES channels 4 and 5 are compared to temperature estimates obtained from the Advanced Very High Resolution Radiometer (AVHRR). This test is achieved by examining sets of infrared data taken simultaneously by the GOES and AVHRR systems over the same geographic area. The AVHRR system collects longwave infrared data with a spatial resolution of 1 km, which is higher than the 4-km spatial resolution the GOES system achieves. In some cases, The estimated temperature differences between these systems are as high as 11.5 K. It is shown in this paper that the proposed algorithm improves the consistency between the cloudtop temperatures estimated with the GOES and AVHRR systems by allowing the GOES system to achieve substantially higher spatial resolution.     

参数弱固定法反演地表温度

提出了弱固定敏感参数、控制信息流向目标参数的地表温度反演方法.从热红外辐射传输机理出发,以MODIS为数据源,构建辐射传输方程,同时反演包括地表温度、大气平均温度、中红外(3～5μm)、远红外(8-14.5 μm)6个波段的大气透过率和发射率共计14个参数.以MODTRAN模拟数据和重庆地区MODIS遥感影像为实验数据...     

利用超光谱红外卫星数据反演大气廓线研究

建立新的物理反演法能同时反演大气温度廓线、水汽廓线、表层温度和地表发射率,该反演算法应用到我国黄海地区AIRS红外卫星资料中,可反演得到较高垂直分辨率的大气温度廓线和水汽廓+线,同时反演得到的表层温度和地表发射率、根据水汽廓线计算大气可降水量。     

An emissivity-based wind vector retrieval algorithm for the WindSat polarimetric radiometer

The Naval Research Laboratory WindSat polarimetric radiometer was launched on January 6, 2003 and is the first fully polarimetric radiometer to be flown in space. WindSat has three fully polarimetric channels at 10.7, 18.7, and 37.0 GHz and vertically and horizontally polarized channels at 6.8 and 23.8 GHz. A first-generation wind vector retrieval algorithm for the WindSat polarimetric radiometer is developed in this study. An atmospheric clearing algorithm is used to estimate the surface emissivity from the measured WindSat brightness temperature at each channel. A specular correction factor is introduced in the radiative transfer equation to account for excess reflected atmospheric brightness, compared to the specular assumption, as a function wind speed. An empirical geophysical model function relating the surface emissivity to the wind vector is derived using coincident QuikSCAT scatterometer wind vector measurements. The confidence in the derived harmonics for the polarimetric channels is high and should be considered suitable to validate analytical surface scattering models for polarized ocean surface emission. The performance of the retrieval algorithm is assessed with comparisons to Global Data Assimilation System (GDAS) wind vector outputs. The root mean square (RMS) uncertainty of the closest wind direction ambiguity is less than 20/spl deg/ for wind speeds greater than 6 m/s and less than 15/spl deg/ at 10 m/s and greater. The retrieval skill, the percentage of retrievals in which the first-rank solution is the closest to the GDAS reference, is 75% at 7 m/s and 85% or higher above 10 m/s. The wind speed is retrieved with an RMS uncertainty of 1.5 m/s.