Temperature Emissivity Separation Research Articles

Lunar Surface Temperature and Emissivity Retrieval From SDGSAT-1 Thermal Imager Spectrometer

The lunar surface temperature is one of the important thermophysical parameters of the Moon, which helps to study the radiative properties of the lunar surface. Thermal infrared emission spectra is sensitive to the thermophysical properties of the lunar surface material, and the emissivity data can be used for lunar surface composition inversion. In the past, humans have conducted hundreds of lunar exploration missions, but only a small number have been conducted in the infrared band, typical examples include the Apollo program and the Diviner lunar radiometer experiment of the LRO satellite. Only part of the lunar surface has been explored by these missions. SDGSAT-1 carries out a complete observation of the lunar surface in three infrared wavelength bands (B1: 8-10.5μm; B2: 10.3-11.3μm; B3: 11.5-12.5μm) by its thermal imager as part of its mission. In this study, the temperature-emissivity separation (TES) algorithm is used to retrieve the lunar surface temperature and calculate the thermal infrared band emissivity using the radiometric data obtained by SDGSAT-1 thermal imager, the temperature distribution of full-disk Moon and the emissivities distribution of three bands are also mapped. The temperature retrieval error is verified less than 1K.

A Combined Vegetation Cover and Temperature-Emissivity Separation (V-TES) Method to Estimate Land Surface Emissivity

A Combined Vegetation Cover and Temperature-Emissivity Separation (V-TES) Method to Estimate Land Surface Emissivity

Temperature and Emissivity Retrieval From Hyperspectral Thermal Infrared Data Using Dictionary-Based Sparse Representation for Emissivity

The separation of land surface temperature (LST) and land surface emissivity (LSE) is an ill-posed problem in thermal infrared (TIR) remote sensing. By building a new observation matrix to compress the LSE unknows and a dictionary training method to reconstruct complete LSE spectra, a new dictionary-based sparse representation for emissivity (DSRE) method has been proposed to retrieve LST and LSE from the atmospherically corrected hyperspectral TIR data. The proposed method fully utilizes the sparsity of compressed sensing and the empirical knowledge of the trained emissivity dictionary. The sensitivity analysis shows that the modeling accuracies of the proposed method are 0.215 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> and 0.0060 for LST and LSE, respectively. Even with the instrument noise of 0.3 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> and the uncertainties in atmospheric transmittance, atmospheric upwelling, and downwelling radiance of 10 %, the retrieval accuracies are 0.811 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> for LST and 0.0241 for LSE, respectively. Then a field experiment was conducted to validate the proposed method, and a comparison was executed to three published methods, including ASTER temperature-emissivity separation (ASTERTES), linear spectral emissivity constraint TES (LSECTES), and iterative spectrally smooth TES (ISSTES). The accuracies of retrieved LST and spectral LSE are 1.41 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> / 0.009, 2.57 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> / 0.071, 1.59 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> / 0.038, and 2.00 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> / 0.077 for DSRE, ASTERTES, LSECTES, and ISSTES. In contrast to the three published methods, our proposed method is more accurate and effective than other published methods. Especially in the atmospheric absorption band, the proposed method has a strong anti-noise capability to the residuals of environmental downwelling radiance.

Quantifying Sub-Meter Surface Heterogeneity on Mars Using Off-Axis Thermal Emission Imaging System (THEMIS) Data.

Surface heterogeneities below the spatial resolution of thermal infrared (TIR) instruments result in anisothermality and can produce emissivity spectra with negative slopes toward longer wavelengths. Sloped spectra arise from an incorrect assumption of either a uniform surface temperature or a maximum emissivity during the temperature-emissivity separation of radiance data. Surface roughness and lateral mixing of different sub-pixel surface units result in distinct spectral slopes with magnitudes proportional to the degree of temperature mixing. Routine Off-nadir Targeted Observations (ROTO) of the Thermal Emission Imaging Spectrometer (THEMIS) are used here for the first time to investigate anisothermality below the spatial resolution of THEMIS. The southern flank of Apollinaris Mons and regions within the Medusae Fossae Formation are studied using THEMIS ROTO data acquired just after local sunset. We observe a range of sloped TIR emission spectra dependent on the magnitude of temperature differences within a THEMIS pixel. Spectral slopes and wavelength-dependent brightness temperature differences are forward-modeled for a series of two-component surfaces of varying thermal inertia values. Our results imply that differing relative proportions of rocky and unconsolidated surface units are observed at each ROTO viewing geometry and suggest a local rock abundance six times greater than published results that rely on nadir data. High-resolution visible images of these regions indicate a mixture of surface units from boulders to dunes, providing credence to the model.

In-flight performance of the Multi-band Uncooled Radiometer Instrument (MURI) thermal sensor

NASA's Earth Science and Technology Office (ESTO) encourages and promotes new and innovative science technologies to improve how the Earth is observed from space. Through their Instrument Incubator Program (IIP), an uncooled multispectral thermal instrument was fabricated and flight-tested to demonstrate its potential utility to support future spaceborne missions. While uncooled systems offer the attractive advantage of eliminating the need for large and expensive cryocoolers, they naturally introduce challenges that must be overcome to achieve the fidelity observed in image data acquired by existing cooled spaceborne instruments.In this work, the Multi-band Uncooled Radiometer Instrument (MURI) system, designed and built by Leonardo DRS, is fabricated using custom microbolometer detector arrays also developed by Leonardo DRS. Potential issues associated with thermal imaging using microbolometers from a spaceborne platform are discussed and innovative engineering solutions to overcome these issues are highlighted. Details of three airborne campaigns designed to assess the fidelity of MURI image data, using Landsat 8's Thermal Infrared Sensor's (TIRS) radiometric & geometric requirements as a baseline, are presented. Results of these campaigns show that the as-built MURI system significantly outperforms these requirements, as compared to ground-based reference measurements. Sustainable Land Imaging (SLI) requirements indicate that a five-band instrument is desirable to improve future Landsat science while maintaining continuity with previous thermal instruments. Considering its multiband design, temperature/emissivity separation (TES) is applied to MURI flight data and compared to ground-based spectrometer measurements for several materials. Results of this study indicate that MURI's TES performance is in-line with existing spaceborne systems. When considered in conjunction with its radiometric fidelity, the MURI uncooled system represents an intriguing option for future space-based missions.

Optimizing TRISHNA TIR channels configuration for improved land surface temperature and emissivity measurements

In preparation of the Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment (TRISHNA) mission, we conducted a thorough analysis of sensitivity for the Temperature-Emissivity Separation (TES) method to the position of the four TRISHNA spectral channels, notably to find an optimal spectral configuration. To that purpose, we designed a fast-computing end-to-end simulator including several components, which we implemented to simulate both pixel-size TRISHNA measurements and land surface temperature (LST) retrievals. Firstly, simulations were conducted over a wide range of realistic scenarii, notably by including vegetation canopy-scale cavity effect. Secondly, the experimental design included the features of second generation Mercury-Cadmium-Telluride (MCT) cooled detectors with lower instrumental noises and finer channels. Thirdly, as opposed to previous studies that used predefined spectral configurations to determine the most suited one, we conducted an optimization of the spectral configuration by crossing, on a pair basis, several positions of the four TIR channels over a range of wavelengths. Fourthly, we quantified the TES sensitivity to atmospheric perturbations, by comparing LST retrievals with and without atmospheric noise.We observed an overall moderate sensitivity of TES LST retrievals to the spectral channel positions, with a maximum RMSE variation of 0.31 K within the atmospheric spectral windows. Furthermore, the TES method was sensitive to three main parameters, namely the instrumental noise, the atmospheric downwelling irradiance, and the transmittance due to ozone and water vapor, with RMSEs larger than 1 K for specific channel locations. Moreover, by considering possible superimposition of two channels, we noted that the TES method could achieve similar performance by considering three or four channels. Eventually, our study enabled us to recommend a new spectral configuration for the TRISHNA TIR instrument, that is more robust to atmospheric perturbations and to uncertainties on channel positions and bandwidths.

A Simulation-Based Error Budget of the TES Method for the Design of the Spectral Configuration of the Micro-Bolometer-Based MISTIGRI Thermal Infrared Sensor

In preparation of the micro-bolometer-based MIcro Satellite for Thermal Infrared GRound surface Imaging (MISTIGRI) mission, we study the error budget of the Temperature-Emissivity Separation (TES) method using several spectral configurations that differ in channel numbers, locations, and widths. The error budget quantifies the contribution of 1) the TES underlying assumption about emissivity spectral contrast, 2) the errors on atmospheric corrections, and 3) the instrumental noise. When dealing with atmospheric corrections, we consider errors in atmospheric temperature, water vapor content, and concentrations of CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> . To that end, we design an end-to-end simulator of MISTIGRI measurements in order to simulate the radiative and biophysical quantities involved in the data processing. We conduct numerous simulations over a wide range of realistic setups that include cavity effect, i.e., radiance trapping within vegetation canopy. In the case of micro-bolometer-based sensing, the current study highlights that atmospheric and instrumental noises have similar impacts on the TES retrievals, with resulting errors twice as large as those due to the TES intrinsic assumption about spectral contrast, where the latter contributes to the TES error budget within the [0.005–0.009] interval for emissivity, and within the [0.3–0.4 K] interval for land surface temperature (LST). Also, we show that retrieval performance of surface temperature is very similar across all considered MISTIGRI spectral configurations, with RMSE variation within 0.2 K. Eventually, our study permits us to select a 4-channels spectral configuration as the most suited for the MISTIGRI instrument, notably because it enables a moderately better capture of the emissivity contrast than a 3-channels one.

Direct impact of solar farm deployment on surface longwave radiation

Motivated by a previous study of using the Moderate Resolution Imaging Spectroradiometers (MODIS) observations to quantify changes in surface shortwave spectral reflectances caused by six solar farms in the southwest United States, here we used a similar method to study the longwave effects of the same six solar farms, with emphases on surface emissivities and land surface temperature (LST). Two MODIS surface products were examined: one relying on generalized split-window algorithm while assuming emissivities from land cover classifications (MYD11A2), the other based on Temperature Emissivity Separation algorithm capable of dynamically retrieving emissivities (MYD21A2). Both products suggest that, compared to adjacent regions without changes before and after solar farm constructions, the solar farm sites have reduced outgoing radiances in three MODIS infrared window channels. Such reduction in upward longwave radiation is consistent with previous in situ measurements. The MYD11A2 results show constant emissivities before and after solar farm constructions because its land type classification algorithm is not aware of the presence of solar farms. The estimated daytime and nighttime LST reduction due to solar farm deployment are ∼1–4K and ∼0.2–0.9K, respectively. The MYD21A2 results indicate a decrease in Band 31 (10.78–11.28 μm) emissivity up to −0.01 and little change in Band 32 (11.77–12.27 μm) emissivity. The LST decreases in the MYD21A2 is slightly smaller than its counterpart in the MYD11A2. Laboratory and in situ measurements indicate the longwave emissivity of solar panels can be as low as 0.83, considerably smaller than MODIS retrieved surface emissivity over the solar farm sites. The contribution of exposed and shaded ground within the solar farm to the upward longwave radiation needs to be considered to fully explain the results. A synthesis of MODIS observations and published in situ measurements is presented. Implication for parameterizing such solar farm longwave effect in the climate models is also discussed.

Field-Based High-Quality Emissivity Spectra Measurement Using a Fourier Transform Thermal Infrared Hyperspectral Imager

Emissivity information derived from thermal infrared (TIR) hyperspectral imagery has the advantages of both high spatial and spectral resolutions, which facilitate the detection and identification of the subtle spectral features of ground targets. Despite the emergence of several different TIR hyperspectral imagers, there are still no universal spectral emissivity measurement standards for TIR hyperspectral imagers in the field. In this paper, we address the problems encountered when measuring emissivity spectra in the field and propose a practical data acquisition and processing framework for a Fourier transform (FT) TIR hyperspectral imager—the Hyper-Cam LW—to obtain high-quality emissivity spectra in the field. This framework consists of three main parts. (1) The performance of the Hyper-Cam LW sensor was evaluated in terms of the radiometric calibration and measurement noise, and a data acquisition procedure was carried out to obtain the useful TIR hyperspectral imagery in the field. (2) The data quality of the original TIR hyperspectral imagery was improved through preprocessing operations, including band selection, denoising, and background radiance correction. A spatial denoising method was also introduced to preserve the atmospheric radiance features in the spectra. (3) Three representative temperature-emissivity separation (TES) algorithms were evaluated and compared based on the Hyper-Cam LW TIR hyperspectral imagery, and the optimal TES algorithm was adopted to determine the final spectral emissivity. These algorithms are the iterative spectrally smooth temperature and emissivity separation (ISSTES) algorithm, the improved Advanced Spaceborne Thermal Emission and Reflection Radiometer temperature and emissivity separation (ASTER-TES) algorithm, and the Fast Line-of-sight Atmospheric Analysis of Hypercubes-IR (FLAASH-IR) algorithm. The emissivity results from these different methods were compared to the reference spectra measured by a Model 102F spectrometer. The experimental results indicated that the retrieved emissivity spectra from the ISSTES algorithm were more accurate than the spectra retrieved by the other methods on the same Hyper-Cam LW field data and had close consistency with the reference spectra obtained from the Model 102F spectrometer. The root-mean-square error (RMSE) between the retrieved emissivity and the standard spectra was 0.0086, and the spectral angle error was 0.0093.

Determination of global land surface temperature using data from only five selected thermal infrared channels: Method extension and accuracy assessment

Land surface temperature (LST) is an essential input for modeling the processes of energy exchange and balance of the earth's surface. Thermal infrared (TIR) remote sensing is considered to be the most efficient way to obtain accurate LST, both regionally and globally. Currently, many LST retrieval algorithms have been developed, including the up-to-date SW-TES (SW: split window; TES: temperature-emissivity separation) method, which is claimed to be able to accurately derive LST without the need for atmospheric information and land surface emissivity (LSE) based on the selected multiple TIR channel configuration. However, this hybrid method is actually not applicable to observations with large viewing angles and was only preliminarily evaluated in Australia. In this study, this method was extended for application to global TIR measurements with different viewing angles. Additionally, the performance of this extended SW-TES method was assessed globally for different seasons by using the MODIS LST product as a reference, and was also validated using in-situ LST measurements from the SURFRAD (SURFace RADiation budget network) sites. The results showed that the LST retrievals using the extended SW-TES method were comparable to the MODIS LST product, with discrepancies of <2.7 K and < 1.8 K for global daytime and nighttime observations, respectively. Validations based on the SURFRAD in-situ LST measurements indicated that the extended method could be used to retrieve LST accurately with a root-mean-square error (RMSE) of approximately 3.6 K during the daytime and 2.4 K during the nighttime. However, special attention should be paid when applying the extended method to daytime observations on grasslands and shrublands during hot seasons, considering the relatively large discrepancy when using this method compared with that obtained with the MODIS LST product (>4.0 K). Overall, in this study, the SW-TES method was extended, and the performance was comprehensively evaluated at the global scale, which may help in facilitating its potential applications.

Joint VSWIR-TIR retrievals of earth's surface and atmosphere

Remote spectroscopy of terrestrial ecosystems generally falls into one of two categories: UV, Visible and ShortWave InfraRed (VSWIR) methods measuring reflected solar illumination, and Thermal InfraRed (TIR) methods measuring radiation emitted from the Earth's surface. Both interpret the measured radiance with inversion algorithms that estimate surface reflectance, emissivity, surface temperatures, and the atmospheric state. However, the two regimes have traditionally used independent inversion methods. Here we present the first simultaneous inversion of the upwelling radiance spectrum from the UV through the VSWIR and the Thermal Infrared. Maximum A Posteriori estimation determines the surface and atmosphere state that is most consistent with measured radiance across the entire range. We find that the complementary information in the two intervals improves estimates of atmospheric properties, surface emissivity, and surface temperature. Posterior uncertainty is 40% lower in surface temperature and 60% in air temperature. Spurious correlations between atmospheric and surface state are also reduced, with 90% lower correlated uncertainty between total column water vapor and both surface and air temperature. Temperature-emissivity separation also improves, with a 33% reduction in correlated posterior uncertainty between the parameters. We demonstrate the method on coincident data from the Classic Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) operating in the VSWIR range, and the Hyperspectral Thermal Emission Spectrometer (HyTES) in the TIR range, during flights on NASA's high-altitude ER-2 aircraft. Measurements spanning 0.4 to 12 microns can improve estimates of surface and atmospheric properties, significantly advancing studies in terrestrial ecosystems, geology, agriculture, and urban management.

Geometry and adjacency effects in urban land surface temperature retrieval from high-spatial-resolution thermal infrared images

Urban land surface temperature (ULST) is a key surface feature parameter in urban heat island studies. However, the geometry and adjacency effects are usually neglected in conventional land surface temperature (LST) retrieval methods. In this study, considering the geometry and adjacency effects, a new urban canopy multiple-scattering thermal radiative transfer (UCM-RT) model and an urban temperature-emissivity separation (UTES) algorithm were developed for ULST retrieval from Chinese GaoFen-5 (GF-5) satellite thermal infrared (TIR) images. The UCM-RT model and the UTES algorithm incorporate multiple scattering within the urban building canopy and consider the thermal radiance contribution from adjacent pixels and the atmosphere. Two schemes were designed to evaluate the geometry and adjacency effects quantitatively in this study: (1) evaluating their impact on ground temperature and top of atmosphere (TOA) brightness temperature for GF-5 four TIR bands from the simulation dataset, and (2) comparing the retrieval temperature difference between the UTES algorithm and conventional temperature-emissivity separation (TES) algorithm (without considering geometry and adjacency effects). For a specific simulation situation, the magnitude of the geometric effects on urban ground temperatures were found to be approximately 3.2 K, 4.2 K, 4.0 K, and 3.9 K for the four GF-5 TIR bands, while the effects on the TOA brightness temperature reached 2.4 K, 3.3 K, 3.3 K, and 3.0 K for those bands, respectively. The results show that the geometry effects have a significant influence on the thermal radiative transfer process. The temperature error of the UTES algorithm was generally up to 0.8 K, much lower than that of the conventional TES algorithm (~2.2 K) in urban surface. The results indicate that the UTES algorithm is suitable for ULST retrieval, and the retrieval temperature difference between the UTES algorithm and conventional TES algorithm ranges from 0.1 K to 2.1 K, especially for dense building pixels. Additionally, the thermal radiation from adjacent pixels in high-spatial-resolution TIR images has significant influence on ULST retrieval. The comparison between the ULST algorithm for urban land surface and the conventional TES algorithm for the flat surface indicated that the three-dimensional structure inside the pixel has a significant influence on the ULST retrieval. The findings of this study indicate that the geometry and adjacency effects must be considered in ULST retrieval for highly accurate results.

Retrieval of Land Surface Temperature, Emissivity, and Atmospheric Parameters From Hyperspectral Thermal Infrared Image Using a Feature-Band Linear-Format Hybrid Algorithm

Thermal infrared remote sensing can acquire large-scale land surface thermal radiance effectively. However, the observed data are affected by surface and atmospheric conditions. Traditional methods require some prior knowledge, such as emissivity in split-window algorithm and atmospheric correction in temperature–emissivity separation algorithm. This information is difficult to obtain directly and accurately. Hyperspectral thermal infrared data provide the possibility for simultaneous retrieval of atmospheric parameters, land surface temperature (LST), and emissivity because of their abundant band information. This study proposed a feature-band linear-format hybrid (FebLihy) algorithm by combining a deep neural network (DNN) model and a physical model with thermal airborne hyperspectral imager (TASI) data. The proposed algorithm was divided into three steps. First, the radiative transfer equation was converted into a linear form, and seven feature bands were chosen to reduce the unknowns. Second, the initial values of atmospheric and land surface parameters were estimated with the DNN model. Finally, least-squares optimization was used in the physical model to retrieve the final results. Results of the simulation data showed that the root-mean-square error (RMSE) of LST was 0.86 K, the RMSE of emissivity was less than 0.015, and the accuracy of atmospheric parameters was improved effectively by the physical model. The FebLihy algorithm was applied in a real TASI image in Fuyun County and verified with CE312 ground measurement data. Accurate results were achieved. The FebLihy algorithm will be optimized in terms of model and data in the future study.

Land Surface Temperature and Emissivity Retrieval from Nighttime Middle and Thermal Infrared Images of Chinese Fengyun-3D MERSI-II

The second-generation Chinese polar-orbit meteorological satellite Fengyun-3D carries the MEdium-Resolution Spectral Imager (MERSI-II). MERSI-II has 19 solar channels and 6 middle and thermal infrared channels and can provide daily global coverage observation at daytime and nighttime. Land surface temperature (LST) products have a wide range of application requirements. Unfortunately, there is no official LST product of MERSI-II yet. This article proposed a temperature-emissivity separation algorithm to estimate LST and emissivity from two mid-infrared (MIR) and two thermal infrared (TIR) nighttime MERSI-II images. The sensitive analysis and ground validation show the following. First, the algorithm can theoretically retrieve LST with an error of less than 0.7 K, emissivity with errors of about 0.025 for MIR channel and 0.01 for TIR channel, respectively. Second, the nighttime LST retrieval error was approximately 2.19 K, and the bias was approximately 0.60 K among 6 SURFRAD sites and 2 PKULSTNet sites. The retrieved emissivity was cross-validated using MODIS emissivity product over a desert area and its difference was obtained as 0.01 and 0.016 for two TIR channels. Finally, the TES algorithm was applied to obtain LST and emissivity images over North China Plain as an example, and the nighttime LST and land surface emissivity (LSE) were obtained well. As a result, this study shows that the proposed TES algorithm provides an effective way to get LST and LSE image from FY-3D/ MERSI-II nighttime observation, which will improve their applications in different fields.

Lunar Surface Temperature and Emissivity Retrieval From Diviner Lunar Radiometer Experiment Sensor

AbstractThe 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.

Comparison of emissivity retrieval methods from ASTER data using Fourier-Transform Infrared Spectroscopy

Land surface emissivity retrieval is important for the remote identification of natural materials and can be used to identify the presence of silicate minerals. However, its estimation from passive sensors involves an undetermined function related to radiance data, which is influenced by the atmosphere. We tested three methods for temperature emissivity retrieval in a dune field composed of 99.53% quartz (SiO2) using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. The tested methods were the reference channel method (RCM), emissivity normalization method (ENM), and temperature emissivity separation (TES) method. An average quartz reference spectrum for the dune samples was calculated from an emissivity database based on temperature and used to evaluate the emissivity products of four ASTER images. In general, the three tested methods had a good approximation when analysed the emissivity reference curve, especially for longer wavelengths that ranged between 2 and 4% of emissivity. The RCM and ENM produced very similar results with the coefficients of determination (R2) as 0.9960 (RMSE 0.0184) and 0.9959 (RMSE 0.0185), respectively. RCM method presented superior results (R2: 0.9960, RMSE: 0.0184), compared to the TES method (R2: 0.9947, RMSE: 0.0197). The TES method showed good results only for shorter wavelengths and, hence, to identify specific targets using ASTER data, such as silicate minerals, it is better to use the RCM method. The emissivity value selected at the saturation point of the spectral library based on temperature is fundamental in acquiring more reliable data.

Noise-sensitivity Analysis and Improvement of Automatic Retrieval of Temperature and Emissivity Using Spectral Smoothness

There are numerous algorithms that can be used to retrieve land surface temperature (LST) and land surface emissivity (LSE) from hyperspectral thermal infrared (HTIR) data. The algorithms are sensitive to a number of factors, where noise is difficult to handle due to its unpredictability. Although there is a lot of research regarding the influence of noise on retrieval errors, few studies have focused on the mechanism. In this study, we selected the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) algorithm—the representative of the iterative spectral smoothness temperature-emissivity separation algorithm family—as the research object and proposed an improved algorithm. First, we analyzed the influence mechanism of noise on the retrieval errors of ARTEMISS in theory. Second, we carried out a simulation and inversion experiment and analyzed the relationship between instrument spectral resolution, noise level, the ARTEMISS parameter setting and the retrieval errors separately. Last, we proposed an improved method (resolution-degrade-based spectral smoothness algorithm, RDSS) based on the mechanism and law of the influence of noise on retrieval errors and provided corresponding suggestions on instrument design. The results show that RDSS improves the accuracy of temperature inversion and is more effective for thermal infrared data with a high noise level and high spectral resolution, which can reduce the LST inversion error by up to 0.75 K and the LSE median absolute deviation (MAD) by 31%. In the presence of noise in HTIR data, the RDSS algorithm performs better than the ARTEMISS algorithm in terms of temperature-emissivity separation.

Land Surface Temperature and Emissivity Retrieval From Nighttime Middle-Infrared and Thermal-Infrared Sentinel-3 Images

The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the two Sentinel-3 satellites provides daily global coverage observation at daytime and nighttime. The split-window (SW) algorithm is currently used to retrieve the land surface temperature (LST) from SLSTR images; however, this algorithm has to utilize visible and near-infrared (VNIR) images and land cover to determine pixel emissivity. For nighttime observation, VNIR cannot be observed, and this limitation complicates the LST retrieval from nighttime images using the SW algorithm. This article proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the nighttime LST and emissivity from one middle-infrared (MIR) and two thermal-infrared (TIR) nighttime Sentinel-3 SLSTR images. The sensitive analysis showed that the algorithm could theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.015, respectively. Ground validation showed that the nighttime LST retrieval error was approximately 1.84 K and the bias was approximately −0.33 K. Finally, the TES algorithm was applied to obtain the LST and emissivity images over northern China as an example. The emissivity retrieved from the nighttime observation can be used in the daytime SW algorithm to improve its feasibility in the LST retrieval process.

New hybrid algorithm for land surface temperature retrieval from multiple-band thermal infrared image without atmospheric and emissivity data inputs

ABSTRACT 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.

Wavelength Calibration Correction Technique for Improved Emissivity Retrieval

Accurate retrieval of surface emissivity from long-wave infrared hyperspectral imaging data is necessary for many scientific and defense applications. Emissivity retrieval requires an atmospheric model for the scene and consists of two interwoven steps: atmospheric compensation (AC) and temperature–emissivity separation (TES). AC converts the at-aperture radiance to ground radiance, and TES uses the ground radiance to produce a temperature and emissivity estimate. TES assumes that emissivity spectra for solids are smooth, compared to atmospheric features. Model-based techniques find an atmospheric model, which produces the smoothest emissivity estimates. The high-resolution model must be band averaged to the sensor's spectral response function (SRF), which is difficult to characterize and maintain. Any errors in the SRF cause errors in the atmospheric spectra and roughness in the emissivity estimates. We propose a technique that improves the quality of the retrieved emissivity by correcting band-averaging errors of the model from the SRF. An in-scene AC (ISAC) technique is used to find pixels containing a high emissivity material. Atmospheric model bands far from absorption features and a material library are then used to identify the material and estimate the pixel temperatures. At-aperture radiance spectra are generated for the pixels, instead of the ground radiance spectra generated by ISAC. The linear relationship between the generated and measured at-aperture radiances is used to determine model correction factors. Using simulated data, we demonstrate the capability of this technique to substantially reduce calibration error effects in the corrected atmospheric spectra and retrieved emissivities.