Comparison of Thermal Infrared Emissivities Retrieved With the Two-Lid Box and the TES Methods With Laboratory Spectra

Knowledge of surface emissivity in the thermal infrared (TIR) region is critical for determining the land surface temperature (LST) from remote-sensing measurements. If emissivity is not well determined, it can cause a significant systematic error in obtaining the LST. The main aim of this paper is to compare different methods for measuring accurate land surface emissivity in the field, namely, the Box method and the Temperature and Emissivity Separation (TES) algorithm. Field emissivities were compared with soil spectra from laboratory measurements. Emissivities were measured for the bands of a multispectral radiometer CE312-2 with effective wavelengths at 8.4, 8.7, 9.1, 10.6, and 11.3 $muhbox{m}$, similar to the Advanced Spaceborne Thermal Emission and Reflection Radiometer TIR bands, and a wide channel 8–13 $muhbox{m}$. The measurements were made at two sites in New Mexico: the White Sands National Monument and an open shrub land in the Jornada Experimental Range. The measurements show that for both sites the emissivities derived with the Box method agree with those derived with the TES algorithm for the 10.6 and 11.3 $muhbox{m}$ bands. However, the emissivities for the shorter wavelength bands are higher when derived with the Box method than those with the TES algorithm, with differences ranging from 2% to 7%. The field emissivities agree within 2% with the laboratory spectrum for the 8–13-, 11.3-, and 10.6-$muhbox{m}$ bands. However, the field and laboratory measurements in general differ from 2.4% to 9% for the shorter wavelength bands, with the larger value most likely caused by variations in soil moisture.      

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     

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...     

Atmospheric correction of Landsat ETM+ land surface imagery. II. Validation and applications

For pt.I see ibid., vol.39, no.11, p.2490-8 (2001). This is the second paper of the series on atmospheric correction of Enhanced Thematic Mapper-Plus (ETM+) land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to (1) evaluate the accuracy of this new atmospheric correction algorithm using ground radiometric measurements, (2) apply this algorithm to correct Moderate-Resolution Imaging Spectroradiometer (MODIS) and SeaWiFS imagery, and (3) demonstrate how much atmospheric correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that atmospheric correction does matter.     

Use of a One-Dimensional Variational Retrieval to Diagnose Estimates of Infrared and Microwave Surface Emissivity Over Land for ATOVS Sounding Instruments

A 1-D variational retrieval of surface emissivity is developed and applied for the Advanced Microwave Sounding Unit modules A and B, along with the High-resolution Infrared Radiation Sounder. This algorithm offers simultaneous retrieval of infrared and microwave emissivity and increases the separation of the emissivity and land surface temperature signals. The initial estimate of the emissivity for the surface-sensitive channels is made by a combination of physical and empirical microwave emissivity models and, in the infrared, by indexing laboratory measurements to vegetation databases. It is found that the initial estimates of emissivity for snow-free vegetated land areas are within 1% of the retrieved infrared values and, in the microwave, within 4% for all snow-free points and within 2% for the vast majority. The emissivity for snow-covered and sea-ice areas remains problematic, and further investigation is required. It is also shown that the average zenith angle dependence of the emissivity is less than 0.0024 for infrared wavelengths and less than 0.0055 for microwave frequencies if the viewing zenith angles greater than 40 are neglected.     

Wind vector retrieval using ERS-1 synthetic aperture radar imagery

An automated algorithm intended for operational use is developed and tested for estimating wind speed and direction using ERS-1 SAR imagery. The wind direction comes from the orientation of low frequency, linear signatures in the SAR imagery that the authors believe are manifestations of roll vortices within the planetary boundary layer. The wind direction thus has inherently a 180° ambiguity since only a single SAR image is used. Wind speed is estimated by using a new algorithm that utilizes both the estimated wind direction and σ ₀ values to invert radar cross section models. The authors show that: 1) on average the direction of the roll vortices signatures is approximately 11° to the right of the surface wind direction and can be used to estimate the surface wind direction to within ±19° and 2) utilizing these estimated wind directions from the SAR imagery subsequently improves wind speed estimation, generating errors of approximately ±1.2 m/s, for ERS-1 SAR data collected during the Norwegian Continental Shelf Experiment in 1991     

Separating temperature and emissivity in thermal infraredmultispectral scanner data: implications for recovering land surfacetemperatures

The accuracy of three techniques for recovering surface kinetic temperature from multispectral thermal infrared data acquired over land is evaluated. The three techniques are the reference channel method, the emissivity normalization method, and the alpha emissivity method. The methods used to recover the temperature of artificial radiance derived from a wide variety of materials. The results indicate that the emissivity normalization and alpha emissivity techniques are the most accurate, and recover the temperature of the majority of the artificial radiance spectra to within 1.5 K; the reference channel method produces less accurate results. The primary advantage of the alpha emissivity method over the emissivity normalization method is that it works well in terrains of widely varying emissivities, e.g.,those dominated by vegetation and igneous rocks. By contrast, the emissivity normalization method works well only if the emissivity used for normalization is close to the maximum emissivity of the spectra in the scene     

基于线性光谱混合模型的地表温度像元分解方法

以北京市Landsat TM为数据源,提出了一种新的地表温度光谱分解模型(Temperature Unmixing with Spectral,TUS),以期将地表温度的空间分辨率提高到30 m.首先,基于线性光谱混合模型获得地表组分的丰度值.然后,基于温度/植被指数选取典型端元的地表温度.最后,综合地表组分的比辐射率数据实现地表温度的分解.结果表明,TUS模型能够有效地提高地表温度的空间分辨率,反映不同地表组分地表温度的空间差异性,平均绝对误差(MAE)和均方根误差(RMSE)分别为1.25 K和2.27 K,非常适合于复杂地表覆盖地区的地表温度降尺度处理.     

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     

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.     

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).     

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.     

A temperature and emissivity separation algorithm for AdvancedSpaceborne Thermal Emission and Reflection Radiometer (ASTER)images

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scanner on NASA's Earth Observing System (EOS)-AM1 satellite (launch scheduled for 1998) will collect five bands of thermal infrared (TIR) data with a noise equivalent temperature difference (NEΔT) of ⩽0.3 K to estimate surface temperatures and emissivity spectra, especially over land, where emissivities are not known in advance. Temperature/emissivity separation (TES) is difficult because there are five measurements but six unknowns. Various approaches have been used to constrain the extra degree of freedom. ASTER's TES algorithm hybridizes three established algorithms, first estimating the normalized emissivities and then calculating emissivity band ratios. An empirical relationship predicts the minimum emissivity from the spectral contrast of the ratioed values, permitting recovery of the emissivity spectrum. TES uses an iterative approach to remove reflected sky irradiance. Based on numerical simulation, TES should be able to recover temperatures within about ±1.5 K and emissivities within about ±0.015. Validation using airborne simulator images taken over playas and ponds in central Nevada demonstrates that, with proper atmospheric compensation, it is possible to meet the theoretical expectations. The main sources of uncertainty in the output temperature and emissivity images are the empirical relationship between emissivity values and spectral contrast, compensation for reflected sky irradiance, and ASTER's precision, calibration, and atmospheric compensation     

改进的三层分解模型热红外影像空间降尺度研究

地表温度(Land surface temperature,LST)是地-气相互作用和能量交换的重要参数之一.为了获取高空间分辨率地表温度数据,研究改进了一种热红外遥感数据降尺度方法,并以上海市Landsat8 OLI/TIRS影像为数据源进行了实验验证,归一化植被指数(Normalized Difference Vegetation Index,NDVI)被分解为低频层、边缘层和细节层,其中边缘层和细节层按比例增加到热红外数据中.并与经典的热红外降尺度方法 Dis Trad算法和Ts HARP算法作为对比,将模拟的地表温度(270 m)作为降尺度数据源实现LST降尺度(90 m).实验结果表明,三种降尺度方法都保留原有的地表温度的空间特征,但Dis Trad算法和Ts HARP算法增加了真实数据中并不存在的温度差异;改进的三层分解模型地表温度的均方根误差为0. 913 K,与Dis Trad方法和Ts HARP算法相比精度分别提高了0. 937 K和0. 832K.     

热红外发射率光谱的野外测量方法与土壤热红外发射率特性研究

基于光谱平滑的温度／发射率迭代算法，提出热红外发射率光谱的野外测量与反演方法；分析了不同组分、粒径及含水量土壤的热红外发射率变化规律。结果表明，在8～9．5μm波长范围内土壤的发射率随SiO2含量的增加而降低，随含H2O量的增加而增大；在11～13μm波长范围内土壤的发射率基本保持不变，基于此分析结果，提出利用热红外光谱数据反演土壤含沙量和含水量的方法。     

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...     

A nonlinear multispectral statistical CLEAN-based precipitationparameter-retrieval algorithm

An iterative algorithm incorporating CLEAN deconvolution concepts for precipitation parameter retrieval using passive microwave imagery is presented. The CLEAN algorithm was originally designed to deconvolve single-channel radio astronomy images. In order to use CLEAN to retrieve precipitation parameters from multispectral passive-microwave imagery, extensions of the algorithm to accommodate nonlinear, multispectral, and statistical data mere designed and implemented. The primary advantage of the nonlinear multispectral statistical (NMS) CLEAN retrieval algorithm relative to existing algorithms is the use of high-resolution (high-frequency) imagery to guide the retrievals of precipitation parameters from lower resolution (Low-frequency) imagery. The NMS-CLEAN retrieval algorithm was used to estimate rain rate (RR) and integrated ice content (IIC) using simulated imagery of oceanic convection as would be observed from six channels of the proposed Advanced Microwave-Scanning Radiometer. Both the accuracy and structural detail of the retrieved rain rate were improved relative to the retrievals from a single-step, nonlinear, statistical algorithm. Reduced error and improved spatial resolution of a more minor magnitude was also seen in the integrated ice-content retrievals. This study also showed that spatially-simple storm structures resulted in better performance of the NMS-CLEAN retrieval algorithm     

Spectral mixture analysis of simulated thermal infraredspectrometry data: An initial temperature estimate bounded TESSMA searchapproach

At sensor thermal infrared (TIR) radiation varies depending on the temperature and emissivity of surface materials and the modifying impact of atmospheric absorption and emission. TIR imaging spectrometry often involves extracting temperature, emissivity, and/or surface composition, which are useful in diverse studies ranging from climatology to land use analyses. A two-stage application of temperature emissivity separation (TES) using spectral mixture analysis (SMA) or TESSMA, was employed to characterize isothermal mixtures on a subpixel basis. This two-stage approach first uses the relationship between a virtual cold endmember fraction and surface temperature to extract initial image temperature estimates. Second, an isothermal SMA application searches the region within the maximum temperature error range of the initial estimate, selecting the best subpixel spectral mixture fit. Work presented includes characterizations of synthetically generated temperature and constituent mixture gradient test images, and a discussion of errors associated with selecting temperature search ranges 25% and 75% smaller than the initial temperature calculation error range. Results using this two-stage approach indicate improved overall temperature estimates, constituent estimates, and constituent fraction estimates using simulated TIR data     

Cloud fraction and cloud shadow property retrievals fromcoregistered TIMS and AVIRIS imagery: the use of cloud morphology forregistration

The Thermal Infrared Multispectral Scanner (TIMS) and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) were operated simultaneously from the ER2 aircraft during a March 1990 test over the Rio Bravo region, Belize. Coregistration of the imagery obtained by these two instruments is necessary to utilize the data effectively. A technique for registering the TIMS imagery to AVIRIS imagery is presented. It takes advantage of the morphology of the fair weather cumulus (FWC) clouds present in the imagery for estimating inter-sensor distortions. It relies on an iterative process in which skew, nearest neighbor sampling, and cross-correlation (1D and 2D) are applied. Comparison between the AVIRIS three-band ratio (3BR) imagery and the coregistered TIMS imagery shows that TIMS is superior in detecting thin cloud and cloud edge pixels, especially over shadowed background. Although the seven scenes analyzed in the study were obtained within the same one-hour time period and over the same geographical region, the optimum temperature threshold for cloud detection, with respect to the background temperature, varies significantly from 2.1 to 3.3°C. These values agree with the AVIRIS 3BR cloud fraction equivalent temperature thresholds to within 0.5°C. When applying a cloud shadow mask from the AVIRIS near infrared imagery to the coregistered TIMS background imagery, a 1°C temperature differential is found between the shadowed and nonshadowed background. This significant radiative cooling by Fair Weather Cumulus cloud shadows could introduce errors in surface emissivity retrievals by other Earth Observing System (EOS) investigators