Single-channel Method Research Articles

Single-channel speech enhancement method using reconstructive NMF with spectrotemporal speech presence probabilities

In this paper, a novel single microphone channel-based speech enhancement technique is presented. While most of the conventional nonnegative matrix factorization-based approaches focus on generating a basis matrix of speech and noise for enhancement, the proposed algorithm performs an additional process to reconstruct speech from noisy speech when these two elements are highly overlapped in selected spectral bands. This process involves a log-spectral amplitude based estimator, which provides the spectrotemporal speech presence probability to obtain a more accurate reconstruction. Moreover, the proposed algorithm applies an unsupervised learning method to the input noise, so it is adaptable to any type of environmental noise without a pre-trained dictionary. The experimental results demonstrate that the proposed algorithm obtains improved speech enhancement performance compared with conventional single channel-based approaches.

Evaluation of 1-D and 2-D models for discharge prediction in straight compound channels with smooth and rough floodplain

Accurate estimation of discharge capacity of flooding rivers is extremely important in flood control projects. Floodplains are often covered with vegetation which generally increases the flow resistance, and affects the conveyance capacity. Several numerical methods have been proposed by researchers for predicting the stage-discharge relationship in compound channels. The hydraulic behavior of compound channel flow with vegetated floodplains is very complex. Hence, the accuracy of numerical discharge prediction methods must be investigated in compound channels with vegetated floodplain. In this paper, experimental results are presented for flow capacity of an asymmetric compound channel with vegetated and non-vegetated floodplain. An attempt has been made to compute the discharge with different 1-D and 2-D methods, including Single Channel Method (SCM), Divided Channel Methods (DCMs), Coherence Method (COHM) and Shiono and Knight Method (SKM). The results are then compared with experimental data. The results show that the SKM model is more accurate in discharge prediction than other models studied in the present study. Also, the DCM-ID model is found to be less accurate in discharge prediction in compound channels with smooth or rough floodplain.

An intercomparison of Landsat land surface temperature retrieval methods under variable atmospheric conditions using in situ skin temperature

Land surface temperature retrieved from Landsat is a valuable resource for understanding land cover change, monitoring the urban heat island effect, and modeling hydrological and carbon cycles, among other applications. However, this dataset is underutilized, in part because it is difficult to accurately correct for atmospheric interference, and in part because it is difficult to validate the resulting LST dataset. As a result, it is often challenging to verify the accuracy of LST calculated from historical data. Currently, three correction methods are commonly used to retrieve land surface temperature from single-band Landsat TIR data—the radiative transfer equation (RTE), the mono-window algorithm (MWA), and the generalized single-channel (GSC) method. Based on current research, it is often unclear which method is best applied in different circumstances and what the actual achieved accuracy is—especially when these methods are employed as they would be for actual applications, rather than under validation conditions. This study retrieves LST from two years’ worth of clear-sky Landsat 5 TM data using all three methods, as well as LST with no atmospheric correction, and validates the results against on-the-ground skin temperature measurements from twenty-five Oklahoma Mesonet stations. Additionally, LST results using both modeled transmittance values and transmittance values based on precipitable water vapor are assessed, as are results from dates with both high and low precipitable water vapor. Results suggest that the MWA method using modeled transmittance is the most robust, with results statistically indistinguishable from Mesonet skin temperature for the complete dataset and a cloud-free subset, as well as for subsets above and below 2g/cm2 precipitable water vapor. The RTE method using modeled atmospheric parameters is also appropriate in some circumstances.

Using spatiotemporal remote sensing data to assess the status and effectiveness of the underground coal fire suppression efforts during 2000–2015 in Wuda, China

China, the largest coal consumer in the world, is one of the countries that experience the most coal fires. Hence, it is critical for China to monitor coal fires and carry out coal fire monitoring, assessment, and appropriate suppression work. The Wuda coalfield, located in China's Inner Mongolia Autonomous Region, was selected as the study area. This paper was aiming to analyze spatiotemporal changes of coal fires located in Wuda of China from 2000 to 2015 and focused on the assessment of fire protection project during 2006–2008. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images were used to detect changes in coal fires over the last 16 years by utilizing a generalized single-channel method to retrieve land surface temperature data and natural breaks extracting coal fire zones. The results showed that: 1) the changes of coal fires area represented obvious three stages: Stage I (2000–2006) was before the fire suppression project with fire spread and expansion; Stage II (2006–2008) was the implementation of this project with rapid reduction of fire area; Stage III (2008–2015) was after this project with stable fire area at a low level; 2) the fire suppression work was highly successful; the area of coal fires basically remained at 192.98 ha, a reduction by 45.74% from 2000 and 62.40% from 2006 (before fire suppression projects); 3) fire suppression work in the south (zone F) and in the northwest (zone A) was the most successful, while some new patches sporadically appeared in other regions (zone B, C and D); 4) coal fire suppression methods have led to significant destruction of local geomorphology; 5) it was difficult to completely extinct coal fires underground with current suppression method. Compared with previous studies, this research paid more attention to the estimation of fire suppression projections which provided an available way for environment decision makers and coal energy producers to qualitatively assess the status and effectiveness of coal fire suppression projects.

Online Noisy Single-Channel Source Separation Using Adaptive Spectrum Amplitude Estimator and Masking

A novel single-channel source separation method is presented to recover the original signals given only a single observed mixture in noisy environment. The proposed separation method is an online adaptive process and independent of parameters initialization. In this paper, a noisy pseudo-stereo mixing model is developed by formulating an artificial mixture from the observed mixture where the signals are modeled by the autoregressive process. The proposed demixing process composes of two steps: First, the noisy mixing model is enhanced by selecting the time-frequency (TF) units of signal presence and computing the mixture spectral amplitude, and second, an adaptive estimation of the parameters associated with each source is computed frame-by-frame, which is then used to construct a TF mask for the separation process. To assess the performance of the proposed method, noisy mixtures of real-audio sources with nonstationary noise have been conducted under various SNRs. Experiments show that the proposed algorithm has yielded superior separation performance especially in low input SNR compared with existing methods.

Effect of emissivity uncertainty on surface temperature retrieval over urban areas: Investigations based on spectral libraries

Land surface emissivity (LSE) is a prerequisite for retrieving land surface temperature (LST) through single channel methods. According to error model, a 0.01 (1%) uncertainty of LSE may result in a 0.5K error in LST under a moderate condition, while an obvious error (approximately 1K) is possible under a warmer and less humid situation. Significant emissivity variations are presented among the anthropogenic materials in three spectral libraries, which raise a critical question that whether urban LSE can be estimated accurately to meet the needs for LST retrieval. Methods widely used for urban LSE estimation are investigated, including the classification-based method, the spectral-index based method, and the linear spectral mixture model (LSMM). Results indicate that the classification-based method may not be effectively applicable for urban LSE estimation, due mainly to the insignificant relation between the short-wave multispectral reflectance and the long-wave thermal emissivity shown by the spectra. Compared with the classification-based method, the LSMM shows relatively more accurate predictions, whereas, the performance of the LSMM largely depends on the determination of endmembers. Obvious uncertainties in LSE estimation likely appear if endmembers are determined improperly. Increasing the spectra for endmembers is a practical and beneficial means for LSMM when there is not a priori knowledge, which emphasizes the necessity of building a comprehensive spectral library of urban materials. Furthermore, the LST retrieval from a single channel of Landsat 8 is more challenging as compared with the retrieval from the channels of its predecessors—Landsat 4/5/7.

Analyzing the distribution of momentum and energy coefficients in compound open channel

Study on the open channel hydraulic is a main part in the river engineering. Compound open channel is a new and accurate concept for modeling of the river engineering modeling. Studies on the flow discharge, velocity distribution, shear stress distribution are the main parameters in the compound open channel studies. Flow discharge calculation in rivers is one of the parameter in flood management. In this paper the hydraulic characteristic of open compound open channel is studied. In this regard distribution of Momentum Coefficient (β), Energy Coefficient (α), head discharge relationship, specific energy curve was evaluated. The head discharge curve of compound open channel was calculated by Single-Channel Method, Divided-Channel Method, and modified divided-channel method (MDCM). The results show that the compound section for canal has significant effect on the distribution of α and β. Calculation of flow discharge in compound open channel by MDCM shows the more values in compere to other analytical approaches.

Comparison of water surface temperature retrieval algorithms from HJ-1B/IRS thermal infrared data over Lake Taihu, China

太湖是我国典型的富营养化湖泊,水温是影响太湖藻类生长的重要环境因子之一,我国环境减灾卫星HJ-1B搭载的红外多光谱相机IRS对太湖水温动态遥感监测具有较大的性能优势.利用6景过境太湖的IRS热红外遥感影像,分别采用单通道普适性算法、辐射传输模型法和单窗算法反演太湖水温,并与实测水温和同期的TERRA/MODIS温度产品进行对比.结果表明,普适性单通道算法反演水温偏高,而辐射传输模型法和单窗算法则偏低;3种算法反演水温的均方根误差在1.001 K以内,单窗算法反演精度最高,其次是辐射传输模型法,再次为普适性单通道算法,而同期MODIS温度产品的均方根误差为1.507 K.3种算法从IRS热红外数据反演的水温直方图均呈正峰态、尖峰状态分布,反演结果能真实地反映太湖水温的空间分布特征.本研究对只有单个热红外通道的卫星传感器开展内陆水体水温遥感监测具有一定的参考意义.;Lake Taihu is a typical eutrophic lake in China, and water surface temperature(WST) becomes one of the most important environmental factors for algae growing in Lake Taihu. The infrared scanner on board Chinese environment and disaster monitoring satellite HJ-1B has a good capability to monitor periodically WST by remote sensing technique. Generalized single-channel method(GSCM), radiative transfer code method(RTM) and mono-window algorithm(MWA) are applied to retrieve WST from six IRS thermal infrared images passing over Lake Taihu, then the retrieved WST is compared with measured one and TERRA/MODIS temperature product. The results show that the WST retrieved by GSCM is a little higher than measured one while that by RTM and WMA is a bit lower, in which the root mean square error(RMSE) of WST calculated by three algorithms is within 1.001 K. The accuracy of WMA is at the best, RTM is at the second good and the third good for GSCM. The RMSE of MODIS temperature product is 1.507 K. The WST histograms retrieved from IRS thermal infrared data by three algorithms showed a positive deviation and a steep normal distribution, suggesting that WST thematic map can display correctly the spatial variability of temperature in Lake Taihu. This will play a guide role to carry out WST monitoring in inland waters by sensor which has only one thermal infrared channel on board satellite.

Development of microfluidization methods for efficient production of concentrated nanoemulsions: Comparison of single- and dual-channel microfluidizers

Nanoemulsions are being increasingly utilized within the pharmaceutical, food, personal care, and chemical industries because of their unique physicochemical properties and functional performances: high optical clarity; prolonged stability; enhanced bioavailability; and novel rheology. For commercial applications, it is important to be able to produce nanoemulsions containing small droplets using efficient homogenization processes. In this study, we compared two microfluidization methods for fabricating nanoemulsions: (i) single-channel microfluidization and (ii) dual-channel microfluidization. The influence of emulsifier concentration, homogenization pressure, disperse phase volume fraction, and initial emulsifier location (oil versus water phase) on particle size was examined. For both devices, the mean particle diameter decreased with increasing emulsifier concentration and homogenization pressure, and there was a linear log–log relationship between mean particle diameter and homogenization pressure. At a similar emulsifier level and homogenization pressure, dual-channel microfluidization produced smaller droplets and narrower distributions than single-channel microfluidization. This effect was attributed to a higher droplet disruption efficiency and/or lower droplet recoalescence rate for the dual-channel system. The dual-channel method could successfully produce nanoemulsions even at high oil concentrations (50%), whereas the single-channel method was only effective at producing nanoemulsions at relatively low oil concentrations (10%). This study demonstrates that dual-channel microfluidization is an efficient means of producing fine nanoemulsions with high oil loading levels, which may be advantageous for many commercial applications.

ANALYSING THE EFFECTS OF DIFFERENT LAND COVER TYPES ON LAND SURFACE TEMPERATURE USING SATELLITE DATA

Abstract. Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

Sparse Hidden Markov Models for Speech Enhancement in Non-Stationary Noise Environments

We propose a sparse hidden Markov model (HMM)-based single-channel speech enhancement method that models the speech and noise gains accurately in non-stationary noise environments. Autoregressive models are employed to describe the speech and noise in a unified framework and the speech and noise gains are modeled as random processes with memory. The likelihood criterion for finding the model parameters is augmented with an ${l_p}$ regularization term resulting in a sparse autoregressive HMM (SARHMM) system that encourages sparsity in the speech- and noise- modeling. In the SARHMM only a small number of HMM states contribute significantly to the model of each particular observed speech segment. As it eliminates ambiguity between noise and speech spectra, the sparsity of speech and noise modeling helps to improve the tracking of the changes of both spectral shapes and power levels of non-stationary noise. Using the modeled speech and noise SARHMMs, we first construct a noise estimator to estimate the noise power spectrum. Then, a Bayesian speech estimator is derived to obtain the enhanced speech signal. The subjective and objective test results indicate that the proposed speech enhancement scheme can achieve a larger segmental SNR improvement, a lower log-spectral distortion and a better speech quality in stationary noise conditions than state-of-the-art reference methods. The advantage of the new method is largest for non-stationary noise conditions .

Thruster fault feature extraction for autonomous underwater vehicle in time-varying ocean currents based on single-channel blind source separation

This article presents a novel thruster fault feature extraction method for autonomous underwater vehicle in time-varying ocean currents. The novel method is based on the combination of ensemble empirical mode decomposition and independent component analysis, which is a universal single-channel blind source separation method. When using ensemble empirical mode decomposition to decompose the autonomous underwater vehicle surge speed signal into a set of intrinsic mode functions, the original number of intrinsic mode functions is big, making the independent components that are separated and reconstructed from the intrinsic mode functions based on independent component analysis contain many false components. To reduce the original number of intrinsic mode functions, a method combining wavelet decomposition with empirical mode decomposition is proposed. When extracting fault feature value from independent components based on modified Bayes’ classification algorithm, the results show that the difference of fault feature value and noise feature value is small, and the ratio of fault feature value and noise feature value is small as well, which make it difficult to distinguish fault feature value from noise feature value. To increase the difference and the ratio, a wavelet detail component–assisted feature extraction method is proposed. The method combining wavelet decomposition with empirical mode decomposition and the wavelet detail component–assisted feature extraction method together constitute a novel thruster fault feature extraction approach which is suitable for autonomous underwater vehicle in time-varying ocean currents. The effectiveness of the proposed methods is verified by the pool experiments of the experimental prototype.

Speech enhancement based on Bayesian decision and spectral amplitude estimation

In this paper, a single-channel speech enhancement method based on Bayesian decision and spectral amplitude estimation is proposed, in which the speech detection module and spectral amplitude estimation module are included, and the two modules are strongly coupled. First, under the decisions of speech presence and speech absence, the optimal speech amplitude estimators are obtained by minimizing a combined Bayesian risk function, respectively. Second, using the obtained spectral amplitude estimators, the optimal speech detector is achieved by further minimizing the combined Bayesian risk function. Finally, according to the detection results of speech detector, the optimal decision rule is made and the optimal spectral amplitude estimator is chosen for enhancing noisy speech. Furthermore, by considering both detection and estimation errors, we propose a combined cost function which incorporates two general weighted distortion measures for the speech presence and speech absence of the spectral amplitudes, respectively. The cost parameters in the cost function are employed to balance the speech distortion and residual noise caused by missed detection and false alarm, respectively. In addition, we propose two adaptive calculation methods for the perceptual weighted order p and the spectral amplitude order β concerned in the proposed cost function, respectively. The objective and subjective test results indicate that the proposed method can achieve a more significant segmental signal-noise ratio (SNR) improvement, a lower log-spectral distortion, and a better speech quality than the reference methods.

Study of the geometry effect on land surface temperature retrieval in urban environment

This study presents a Single Channel Method using Urban Exitance Model (UEM-SCM) to retrieve land surface temperature (LST) from satellite data in an urbanized city, and evaluates the geometry effect on land surface temperature retrieval using single channel method and split-window algorithm. The UEM-SCM incorporates the effect of urban geometry and considers both reflection caused by the target pixel and its neighboring pixels. In order to evaluate the geometry effect, the retrieved LSTs with and without geometry effect were studied. Results show that the LSTs without geometry effect are generally higher than the LSTs with geometry effect. The temperature difference occurs because the material emissivity is always lower than the effective emissivity caused by multiple scattering and reflection in urban areas (cavity effect). The LST without geometry effect also cannot fully capture the variability and complexity of urban thermal patterns. The temperature difference between with and without the geometry effect can reach 2K in built-up areas. A comparison was also conducted between LST retrieved by split-window algorithm with and without geometry effect. Results show that the LST retrieved by split-window algorithm without geometry effect has generally higher values than the one with the geometry effect, e.g. 1.1K on average and 1.5–2K in built-up areas. The geometry effect will be removed and mis-deemed as atmospheric effect when the split-window algorithm without geometry effect is applied in urban areas. The split-window algorithm with the geometry effect can be used to distinguish between geometry and atmospheric effect in further study.

Binary mask based method for enhancement of mixed noise speech of low SNR input

This paper presents a noise reduction method based on binary mask thresholding function for enhancement in single channel speech patterns of mixed highly non-stationary noises with low (negative) input SNR. For this purpose, a mixed highly non-stationary noisy speech database is generated by using noise and clean speech database of AURORA and INDIC speech, respectively. Results are compared with widely used methods such as Daubechies13, Daubechies40, Symlet13, Coiflet5, Wiener, Spectral Subtraction, and log-MMSE for performance evaluation in terms of SNR, PESQ, and Cepstrum distance parameters. In comparison to other methods the proposed single-channel speech enhancement method shows satisfactory results and obtained significant improvement in speech quality and intelligibility.

Effectiveness of dereverberation, feature transformation, discriminative training methods, and system combination approach for various reverberant environments

The recently released REverberant Voice Enhancement and Recognition Benchmark (REVERB) challenge includes a reverberant automatic speech recognition (ASR) task. This paper describes our proposed system based on multi-channel speech enhancement preprocessing and state-of-the-art ASR techniques. For preprocessing, we propose a single-channel dereverberation method with reverberation time estimation, which is combined with multichannel beamforming that enhances direct sound compared with the reflected sound. In addition, this paper also focuses on state-of-the-art ASR techniques such as discriminative training of acoustic models including the Gaussian mixture model, subspace Gaussian mixture model, and deep neural networks, as well as various feature transformation techniques. Although, for the REVERB challenge, it is necessary to handle various acoustic environments, a single ASR system tends to be overly tuned for a specific environment, which degrades the performance in the mismatch environments. To overcome this mismatch problem with a single ASR system, we use a system combination approach using multiple ASR systems with different features and different model types because a combination of various systems that have different error patterns is beneficial. In particular, we use our discriminative training technique for system combination that achieves better generalization by making systems complementary with the modified discriminative criteria. Experiments show the effectiveness of these approaches, reaching 6.76 and 18.60 % word error rates on the REVERB simulated and real test sets. These are 68.8 and 61.5 % relative improvements over the baseline.

Speech enhancement based on wavelet packet of an improved principal component analysis

In this paper, we propose a single-channel speech enhancement method, based on the combination of the wavelet packet transform and an improved version of the principal component analysis (PCA). Our method integrates ability of PCA to de-correlate the coefficients by extracting a linear relationship with what of wavelet packet analysis to derive feature vectors used for speech enhancement. This allows us to operate with a convenient shrinkage function on these new coefficients, removing the noise without degrading the speech. Then, the enhanced speech obtained by the inverse wavelet packet transform is decomposed into three subspaces: low rank, sparse, and the remainder noise components. Finally, we calculate the components as a segregation problem. The performance evaluation shows that our method provides a higher noise reduction and a lower signal distortion even in highly noisy conditions without introducing artifacts.

Validation and Performance Evaluations of Methods for Estimating Land Surface Temperatures from ASTER Data in the Middle Reach of the Heihe River Basin, Northwest China

Validation and performance evaluations are beneficial for developing methods that estimate the remotely sensed land surface temperature (LST). However, such evaluations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data are rare. By selecting the middle reach of the Heihe River basin (HRB), China, as the study area, the atmospheric correction (AC), mono-window (MW), single-channel (SC), and split-window (SW) methods were evaluated based on in situ measured LSTs. Results demonstrate that the influences of surface heterogeneity on the validation are significant in the study area. For the AC, MW, and SC methods, the LSTs estimated from channel 13 are more accurate than those from channel 14 in general cases. When the in situ measured atmospheric profiles are available, the AC method has the highest accuracy, with a root-mean squared error (RMSE) of about 1.4–1.5 K at the homogenous oasis sites. In actual application without sufficient in situ measured inputs, the MW method is highly accurate; the RMSE is around 1.5–1.6 K. The SC method systematically overestimates LSTs and it is sensitive to error in the water vapor content. The two SW methods are simple to use but their performances are limited by accuracies, revealed by the simulation dataset. Therefore, when the in situ atmospheric profiles are available, the AC method is recommended to generate reliable ASTER LSTs for modeling the eco-hydrological processes in the middle reach of the HRB. When sufficient in situ measured inputs are not available, the MW method can be used instead.

Change in urban wetlands and their cold island effects in response to rapid urbanization

The cold-island effect of urban wetlands has received increasing attention in recent years due to its important role in the alleviation of urban heat islands. Hangzhou, a representative rapidly urbanizing city with rich wetlands in China, was selected as a case study for researching the changes that the urban wetlands have undergone and their impact on the urban thermal environment. Land surface temperature (LST) was acquired from the thermal infrared data of Landsat 5 Thematic Mapper (TM) images in 1990, 1995, 2000, 2006, and 2010, using the single-channel method. The results are as follows: 1) considering the changes in land use, the urban wetlands located to the west of Hangzhou have decreased significantly during 1990–2010 because of rapid urbanization. In the Xixi Wetland, the change in land use was relatively small and most of the water body and vegetation were preserved. However, to the east of the Xixi Wetland, large areas of water body and vegetation have been replaced by built-up land as a result of the urbanization process; 2) considering the change in LST, it was found from land surface temperature retrieval that the changing spatial pattern of the thermal field was highly correlated with land use changes. Low temperature regions of the eastern Xixi Wetland were gradually eroded by high temperature regions, and the centroid of the heat island in East Xixi was found to be constantly shifting westward. In addition, the difference in LST between the Xixi Wetland and East Xixi has increased; 3) considering the impact factors for this area, land use structure and patch shape were found to have a significant impact on LST, shown by the results of multiple linear stepwise regressions. Increasing the size of the wetlands in urban planning is considered to be the most effective measure in alleviating the urban heat island effect. Moreover, reducing the spatial complexity of landscape patches also contributes to the alleviation of the urban heat island effect.

Comparing different profiles to characterize the atmosphere for three MODIS TIR bands

Accurate land surface temperature (LST) retrievals from sensors aboard orbiting satellites are dependent on the corresponding atmospheric correction, especially in the thermal infrared (TIR) spectral domain (8–14μm). To remove the atmospheric effects from at-sensor measured radiance in the TIR range it is needed to characterize the atmosphere by means of three specific variables: the upwelling path and the hemispherical downwelling radiances plus the atmospheric transmissivity. Those variables can be derived from the previous knowledge of vertical atmospheric profiles of air temperature and relative humidity at different geo-potential heights and pressures.In this work, the above mentioned atmospheric variables were analyzed for three specific weather station sites located in Spain, at three different altitudes. These variables were calculated with atmospheric profiles retrieved from three different sources: The National Centers for Environmental Prediction (NCEP) web-tool atmospheric profiles calculator, the MODIS (MOD07) product and the radiosoundings available on the web of the University of Wyoming (WYO), which are launched by the Agencia Estatal de Meteorologia (AEMET), in the particular case of Spain. Atmospheric profiles from 2010 to 2013 were obtained to carry out the present study.Results from comparison of these three different atmospheric profiles show that the NCEP profiles characterize the atmosphere in a better manner than MOD07. Average result values of the three MODIS spectral bands 29, 31 and 32 show a BIAS of 0.06Wm−2μm−1sr−1 and RMSE of ±0.2Wm−2μm−1sr−1 for upwelling radiance, a BIAS of 0.13Wm−2μm−1sr−1 and RMSE of ±0.3Wm−2μm−1sr−1 for the donwelling radiance and a BIAS of −0.008 and RMSE of ±0.03 for the atmospheric transmissivity.In terms of simulated LST, these errors yield a deviation of ±0.9K when applying a single-channel method.