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Land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing image requires atmospheric and land surface emissivity (LSE) data that are sometimes unattainable. To overcome this problem, a hybrid algorithm is developed to retrieve LST without atmospheric correction and LSE data input, by combining the split-window (SW) and temperature–emissivity separation (TES) algorithms. The SW algorithm is used to estimate surface-emitting radiance in adjacent TIR bands, and such radiance is applied to the TES algorithm to retrieve LST and LSE. The hybrid algorithm is implemented on five TIR bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Analysis shows that the hybrid algorithm can estimate LST and LSE with an error of 0.5–1.5 K and 0.007–0.020, respectively. Moreover, the LST error of the hybrid algorithm is equivalent to that of the original ASTER TES algorithm, involving 1%–2% uncertainty in atmospheric correction. The hybrid algorithm is validated using ground-measured LST at six sites and ASTER LST products, indicating that the temperature difference between the ASTER TES algorithm and the hybrid algorithm is 1.4 K and about 2.5–3.5 K compared to the ground measurement. Finally, the hybrid algorithm is applied to at two places.

Surface emissivity (ε) is used to characterize surfaces and to determine surface temperature from thermal radiation data. While in many applications it is treated as a constant, it is known to change with surface water content. Several ASTER/SEVERI based studies have speculated that diurnal changes in ε over deserts are linked to diurnal soil water content cycles resulting from water vapor adsorption during the night and subsequent evaporation during the day. This paper aims, for the first time, to validate the relationship between diurnal changes in surface ε and the changes in soil water content due to water vapor adsorption and evaporation under natural conditions. Measurements were conducted with a 6-band infrared radiometer, designed to validate ASTER bands 10–14, with study-specific recalibration for improved accuracy of ε. The evaluation included two different approaches to determine ε : using a single reference band (1B) and using the temperature/emissivity separation algorithm (TES). water content. While the TES has proven itself in many applications, it was found that for the soils studied (sand and loess) the use of 1B approach gave more consistent results for ε changes with soil water content than TES. Emissivity could be a powerful tool to characterize little studied soil water content changes in arid regions, but will require better characterization of surface properties to quantify the relationship between ε and soil water content for various soil types. Additional challenges to upscale this method include properly accounting for air irradiance and spatial heterogeneity. Meeting these main challenges will lead the way to detect small changes in soil water content under dry conditions at larger scales. Whether these are a result of water vapor adsorption or other processes, detecting such small changes in soil water content will provide new insights into desert hydrology. • On-ground validation of an infrared radiometer with ASTER bands. • Calibration and measuring protocol for minute changes in emissivity. • Observed emissivity changes of 0.2–0.6 due to water vapor adsorption. • Main challenges: air radiance, surface heterogeneity, and soil spectral properties. • Potential for new insights into desert hydrology. [ABSTRACT FROM AUTHOR]

Abstract The lunar surface temperature (LST) derived from thermal infrared (TIR) measurements can aid in understanding the physical properties of the lunar surface. The Diviner Lunar Radiometer Experiment (herein, Diviner) sensor provides global lunar surface observation in seven TIR channels. However, its retrieval of LST constantly uses a single emissivity value (i.e., 0.95) by ignoring the spatial variation of lunar surface, thereby reducing the accuracy of temperature and day–night temperature difference. To overcome this problem, this study developed a physical method called temperature–emissivity separation (TES) algorithm to retrieve LST and lunar surface emissivity from the daytime observation in three Christiansen Feature (CF) channels (7.55–8.05, 8.10–8.40, and 8.38–8.60 μm) of the Diviner, and then used the emissivity from daytime observation to inverse LST at nighttime observation. Findings showed that the TES algorithm could retrieve LST and emissivity with an error of less than 0.8 K and 0.008, respectively. However, observation noise significantly affected the retrieval accuracy, particularly for the low‐temperature pixels; moreover, high retrieval accuracy requires a surface temperature higher than 240 K. The new algorithm was applied to obtain the daytime and nighttime LST and emissivity from the Diviner images. Results showed that the LST retrieved from the algorithm differed approximately 3.9 K from that calculated from a single emissivity 0.95. Finally, an example of global surface temperature and emissivity were obtained. Consequently, the CF pixels were found to distribute in the latitude range from −60° to 60°; however, they did not have a large distribution in high‐latitude and near‐polar regions.

The lunar surface temperature (LST) derived from thermal infrared (TIR) measurements can aid in understanding the physical properties of the lunar surface. The Diviner Lunar Radiometer Experiment (herein, Diviner) sensor provides global lunar surface observation in seven TIR channels. However, its retrieval of LST constantly uses a single emissivity value (i.e., 0.95) by ignoring the spatial variation of lunar surface, thereby reducing the accuracy of temperature and day–night temperature difference. To overcome this problem, this study developed a physical method called temperature–emissivity separation (TES) algorithm to retrieve LST and lunar surface emissivity from the daytime observation in three Christiansen Feature (CF) channels (7.55–8.05, 8.10–8.40, and 8.38–8.60 μm) of the Diviner, and then used the emissivity from daytime observation to inverse LST at nighttime observation. Findings showed that the TES algorithm could retrieve LST and emissivity with an error of less than 0.8 K and 0.008, respectively. However, observation noise significantly affected the retrieval accuracy, particularly for the low‐temperature pixels; moreover, high retrieval accuracy requires a surface temperature higher than 240 K. The new algorithm was applied to obtain the daytime and nighttime LST and emissivity from the Diviner images. Results showed that the LST retrieved from the algorithm differed approximately 3.9 K from that calculated from a single emissivity 0.95. Finally, an example of global surface temperature and emissivity were obtained. Consequently, the CF pixels were found to distribute in the latitude range from −60° to 60°; however, they did not have a large distribution in high‐latitude and near‐polar regions. Key Points: A physical algorithm was proposed to estimate lunar surface temperature and emissivity from Diviner daytime and nighttime observationsLunar surface temperature retrieved from the new algorithm differed 3.9 K from that calculated from a fixed emissivity 0.95Global lunar Christiansen Feature pixels were identified and found to mainly distribute in the latitude range from −60° to 60° [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing image requires atmospheric and land surface emissivity (LSE) data that are sometimes unattainable. To overcome this problem, a hybrid algorithm is developed to retrieve LST without atmospheric correction and LSE data input, by combining the split-window (SW) and temperature–emissivity separation (TES) algorithms. The SW algorithm is used to estimate surface-emitting radiance in adjacent TIR bands, and such radiance is applied to the TES algorithm to retrieve LST and LSE. The hybrid algorithm is implemented on five TIR bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Analysis shows that the hybrid algorithm can estimate LST and LSE with an error of 0.5–1.5 K and 0.007–0.020, respectively. Moreover, the LST error of the hybrid algorithm is equivalent to that of the original ASTER TES algorithm, involving 1%–2% uncertainty in atmospheric correction. The hybrid algorithm is validated using ground-measured LST at six sites and ASTER LST products, indicating that the temperature difference between the ASTER TES algorithm and the hybrid algorithm is 1.4 K and about 2.5–3.5 K compared to the ground measurement. Finally, the hybrid algorithm is applied to at two places. [ABSTRACT FROM AUTHOR]

Validation of emissivity (ε) retrievals from spaceborne thermal infrared (TIR) sensors typically requires spatial extrapolations over several orders of magnitude for a comparison between centimeter-scale laboratory ε measurements and the common decameter and lower resolution of spaceborne TIR data. In the case of NASA’s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) temperature and ε separation algorithm (TES), this extrapolation becomes especially challenging because TES was originally designed for the geologic surface of Earth, which is typically heterogeneous even at centimeter and decameter scales. Here, we used the airborne TIR hyperspectral Mako sensor with its 2.2 m/pixel resolution, to bridge this scaling issue and robustly link between ASTER TES 90 m/pixel emissivity retrievals and laboratory ε measurements from the Algodones dune field in southern California, USA. The experimental setup included: (i) Laboratory XRD, grain size, and TIR spectral measurements; (ii) radiosonde launches at the time of the two Mako overpasses for atmospheric corrections; (iii) ground-based thermal measurements for calibration, and (iv) analyses of ASTER day and night ε retrievals from 21 different acquisitions. We show that while cavity radiation leads to a 2% to 4% decrease in the effective emissivity contrast of fully resolved scene elements (e.g., slipface slopes and interdune flats), spectral variability of the site when imaged at 90 m/pixel is below 1%, because at this scale the dune field becomes an effectively homogeneous mixture of the different dune elements. We also found that adsorption of atmospheric moisture to grain surfaces during the predawn hours increased the effective ε of the dune surface by up to 0.04. The accuracy of ASTER’s daytime emissivity retrievals using each of the three available atmospheric correction protocols was better than 0.01 and within the target performance of ASTER’s standard emissivity product. Nighttime emissivity retrievals had lower precision (

Emissivity mapping of the Earth’s surface is the prerequisite to thermal remote sensing. A precise determinationof a surface's temperature is dependent upon the availability of precise emissivity data for that surface. The presentstudy area is a part of sugarcane plantation fields in the west part of Khuzestan province. In this work, TemperatureEmissivity Separation algorithm (TES) was applied to five different ASTER L1B images. It was found out that TESmethod overestimates temperature in all the five thermal bands, and underestimates the emissivities as compared tothe laboratory values. The differences in the emissivity values (as compared to laboratory values) varied from 10% inband 10 to 3% in band 14. The main reasons for these discrepancies were a lack of proper calibration of the thermalbands, the possible presence of radiometric noises in the calculation of the emissivity Maximum MinimumDifferences (MMD) as well as mixed pixels. To overcome these uncertainties in the TES algorithm, an ImprovedTES method (ITES) was introduced. In the ITES method, the surface exiting thermal fluxes were simulated. Theemissivities of four different reference surfaces, along with air temperature measured at nearby weather stations(believed to represent LST of full vegetated pixels) and the band 14 temperature, were employed as inputs. Theresults show noticeable improvements in the predicted emissivity to around 1% for band 10 and less than 1% forbands 13 and 14 as compared to the corresponding laboratory values. The root mean square error (RMSE) ofemissivities for full vegetation cover was less than 0.015 and less than 0.01 for partial vegetated cover, bare soil, andsea water surface. Finally, emissivity maps for one sample image, employing the five thermal bands, were produced.It is believed that these maps can be used in other satellite images as layers of emissivity values for the purpose of aproper estimation of surface temperatures.

This paper presents a new algorithm for the analysis of linear spectral mixtures in the thermal infrared domain, with the goal to jointly estimate the abundance and the subpixel temperature in a mixed pixel, i.e., to estimate the relative proportion and the temperature of each material composing the mixed pixel. This novel approach is a two-step procedure. First, it estimates the emissivity and the temperature over pure pixels using the standard temperature and emissivity separation (TES) algorithm. Second, it estimates the abundance and the subpixel temperature using a new unmixing physics-based model, called Thermal Remote sensing Unmixing for Subpixel Temperature (TRUST). This model is based on an estimator of the subpixel temperature obtained by linearizing the black body law around the mean temperature of each material. The abundance is then retrieved by minimizing the reconstruction error with the estimation of the subpixel temperatures. The TRUST method is benchmarked on simulated scenes against the fully constrained least squares unmixing applied on the radiance and on the estimation of surface emissivity using the TES algorithm. The TRUST method shows better results on pure and mixed pixels composed of two materials. TRUST also shows promising results when applied on thermal hyperspectral data acquired with the Thermal Airborne Spectrographic Imager during the Detection in Urban scenario using Combined Airborne imaging Sensors campaign and estimates coherent localization of mixed-pixel areas. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss the structure and performance of the maximum entropy temperature-emissivity separation (MaxEnTES) algorithm for assessing land temperature and emissivity from thermal infrared hyperspectral images. This procedure derives the emissivity spectrum and the temperature of the target adopting the maximum entropy (MaxEnt) estimation approach. The main advantage of the MaxEnt statistical inference is the absence of any external hypothesis, which is, instead, the main critical point characterizing any other temperature-emissivity separation (TES) algorithm. The MaxEnTES algorithm carries out the TES task adopting a modified version of the subgradient Shor's r-algorithm adopted for numerical optimization of a MaxEnt objective function. For this purpose, we have utilized the C/C++ Solvopt code from the University of Gratz to develop a practical data processing implementation. In this paper, we discuss the mathematical structure of the MaxEnTES algorithm and analyze its performance in depth using numerical simulations and remote sensing Multispectral Infrared/Visible Imaging Spectrometer images. We show that the MaxEnTES algorithm provides improved accuracy for temperature and emissivity estimation, lowering the standard estimation error to a fraction of degree Kelvin. In agreement with previous investigations, we find that the estimation accuracy grows when increasing the number of available spectral channels. The systematic errors affecting the temperature estimates (e.g., bias) are thoroughly evaluated. We prove that the MaxEnTES algorithm retrieves the correct shape of the target emissivity spectrum even in presence of a significant temperature estimation error. [ABSTRACT FROM PUBLISHER]

Abstract: In this work a methodology to provide an emissivity map of an urban area is presented. The methodology is applied to the city of Madrid (Spain) using data provided by the Airborne Hyperspectral Scanner (AHS) in 2008. From the data a classification map with twelve different urban materials was created. Each material was then characterized by a different emissivity, whose values were obtained from the application of the TES algorithm to in situ measurements and values extracted from the ASTER spectral library. This new emissivity map could be used as a basis for determining the temperature of the city and to understand the urban heat island effect in terms of spatial distribution and size. [Copyright &y& Elsevier]

Emissivity mapping of the Earth's surface is the prerequisite to thermal remote sensing. A precise determination of a surfaces temperature is dependent upon the availability of precise emissivity data for that surface. The present study area is a part of sugarcane plantation fields in the west part of Khuzestan province. In this work, Temperature Emissivity Separation algorithm (TES) was applied to five different ASTER L1B images. It was found out that TES method overestimates temperature in all the five thermal bands, and underestimates the emissivities as compared to the laboratory values. The differences in the emissivity values (as compared to laboratory values) varied from 10% in band 10 to 3% in band 14. The main reasons for these discrepancies were a lack of proper calibration of the thermal bands, the possible presence of radiometric noises in the calculation of the emissivity Maximum Minimum Differences (MMD) as well as mixed pixels. To overcome these uncertainties in the TES algorithm, an Improved TES method (ITES) was introduced. In the ITES method, the surface exiting thermal fluxes were simulated. The emissivities of four different reference surfaces, along with air temperature measured at nearby weather stations (believed to represent LST of full vegetated pixels) and the band 14 temperature, were employed as inputs. The results show noticeable improvements in the predicted emissivity to around 1% for band 10 and less than 1% for bands 13 and 14 as compared to the corresponding laboratory values. The root mean square error (RMSE) of emissivities for full vegetation cover was less than 0.015 and less than 0.01 for partial vegetated cover, bare soil, and sea water surface. Finally, emissivity maps for one sample image, employing the five thermal bands, were produced. It is believed that these maps can be used in other satellite images as layers of emissivity values for the purpose of a proper estimation of surface temperatures. [ABSTRACT FROM AUTHOR]

Different from visible signals, thermal infrared radiances depend on both temperature and emissivity. It is a key problem for us to separate temperature and emissivity in thermal infrared remote sensing research. Another difficulty encountered in the retrieval of surface temperature is the correction of downwelling sky irradiance, because it is closely related to surface emissivity. When emissivity is unknown, the downwelling sky irradiance is difficult to be removed. In this paper, we introduce a correction term of downwelling sky irradiance developed by Li and Becker into Wien’s approximation, to derive an improved ALPHA difference spectrum which is independent of temperature, and furthermore develop a correction term to remove the error of Wien’s approximation. Under the support of the above work, attractive features of Alpha derived emissivity method and ASTER TES algorithm are combined together to acquire a new Improved TES algorithm based on Corrected ALPHA Difference Spectrum (ICADS TES). Because a multi-band inversion technique is applied, and the operations of band ratios and differences are included in the algorithm, it can partly remove the influence of atmosphere and noises. Numerical simulation experiments show that for various combinations of atmosphere, land covers and surface temperatures, the algorithm is applicable and stable. Its accuracy for temperature is 0–1.5 K, and that for emissivity is 0–0.015. Compared with current TES algorithms, our method has clear physical meaning, is easy to be implemented, and is applicable for a wide temperature range and surface types. The results are not influenced by the directional characteristic of emissivity. Because ICADS TES does not need the support of a priori information of surface types, it is also not influenced by the accuracy of classification and the problem of mixture pixels. Compared with our former TES algorithm based on corrected Alpha difference spectra (CADS TES), the new algorithm takes the effect of downwelling atmospheric radiation into account. When the quantity of atmosphere radiation can be estimated precisely, the performance of ICADS TES is much better. [ABSTRACT FROM AUTHOR]

Validation of emissivity (ε) retrievals from spaceborne thermal infrared (TIR) sensors typically requires spatial extrapolations over several orders of magnitude for a comparison between centimeter-scale laboratory ε measurements and the common decameter and lower resolution of spaceborne TIR data. In the case of NASA's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) temperature and ε separation algorithm (TES), this extrapolation becomes especially challenging because TES was originally designed for the geologic surface of Earth, which is typically heterogeneous even at centimeter and decameter scales. Here, we used the airborne TIR hyperspectral Mako sensor with its 2.2 m/pixel resolution, to bridge this scaling issue and robustly link between ASTER TES 90 m/pixel emissivity retrievals and laboratory ε measurements from the Algodones dune field in southern California, USA. The experimental setup included: (i) Laboratory XRD, grain size, and TIR spectral measurements; (ii) radiosonde launches at the time of the two Mako overpasses for atmospheric corrections; (iii) ground-based thermal measurements for calibration, and (iv) analyses of ASTER day and night ε retrievals from 21 different acquisitions. We show that while cavity radiation leads to a 2% to 4% decrease in the effective emissivity contrast of fully resolved scene elements (e.g., slipface slopes and interdune flats), spectral variability of the site when imaged at 90 m/pixel is below 1%, because at this scale the dune field becomes an effectively homogeneous mixture of the different dune elements. We also found that adsorption of atmospheric moisture to grain surfaces during the predawn hours increased the effective ε of the dune surface by up to 0.04. The accuracy of ASTER's daytime emissivity retrievals using each of the three available atmospheric correction protocols was better than 0.01 and within the target performance of ASTER's standard emissivity product. Nighttime emissivity retrievals had lower precision (<0.03) likely due to residual atmospheric effects. The water vapor scaling (WVS) atmospheric correction protocol was required to obtain accurate (<0.01) nighttime ASTER emissivity retrievals. [ABSTRACT FROM AUTHOR]