Surface emissivity (e) 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 wate...
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Surface emissivity (e) 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 e 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 e 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 e. The evaluation included two different approaches to determine e: 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 e 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 e 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.
Land surface temperature(LST)retrieval from thermal infrared(TIR)remote sensing image requires atmospheric and land surface emissivity(LSE)data that are sometimes *** overcome this problem,a hybrid algorithm is develo...
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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 *** 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)*** 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 *** 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,***,the LST error of the hybrid algorithm is equivalent to that of the original ASTER tes algorithm,involving 1%–2%uncertainty in atmospheric *** 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 ***,the hybrid algorithm is applied to at two places.
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) sen...
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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 mu m) 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 degrees to 60 degrees;however, they did not have a large distribution in high-latitude and near-polar regions.
Validation of emissivity (epsilon) retrievals from spaceborne thermal infrared (TIR) sensors typically requires spatial extrapolations over several orders of magnitude for a comparison between centimeter-scale laborat...
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Validation of emissivity (epsilon) retrievals from spaceborne thermal infrared (TIR) sensors typically requires spatial extrapolations over several orders of magnitude for a comparison between centimeter-scale laboratory epsilon 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 epsilon 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 epsilon 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 epsilon 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 epsilon 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 emiss
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. ...
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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. (C) 2012 Elsevier B.V. All rights reserved.
On IGARSS'04 conference, we introduced a new tes algorithm we developed basing on corrected ALPHA difference spectra. Although that algorithm can separate temperature and emissivity successfully, it didn't tak...
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ISBN:
(纸本)0780390504
On IGARSS'04 conference, we introduced a new tes algorithm we developed basing on corrected ALPHA difference spectra. Although that algorithm can separate temperature and emissivity successfully, it didn't take downwelling sky irradiance into account. That means it can only be applied in very special circumstances where downwelling sky irradiance can be neglected. In fact, the surface thermal infrared radiance can be expressed as: L-J=epsilon B-j(j)(T-s)+(1-epsilon(j))L-atj down arrow. According to that equation, we must take the influence of downwelling sky irradiance into account in most cases to separate temperature and emissivity correctly. In this paper, we developed an improved tes algorithm which takes downwelling sky irradiance into account on the basis of our old algorithm. The new algorithm is applicable in most cases. To validate our algorithm, we compare it with ASTER tes algorithm and found that they agree quite well, especially for the inverted temperatures.
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