scholarly journals Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method

2014 ◽  
Vol 6 (10) ◽  
pp. 9829-9852 ◽  
Author(s):  
Xiaolei Yu ◽  
Xulin Guo ◽  
Zhaocong Wu
Keyword(s):  
Radiative Transfer ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Transfer Equation ◽  
Single Channel ◽  
Radiative Transfer Equation ◽  
Landsat 8 ◽  
Split Window Algorithm ◽  
Temperature Retrieval

scholarly journals LAND SURFACE TEMPERATURE RETRIEVAL FROM THE THERMAL RADIANCE COLLECTED BY LANDSAT 8 TIRS SENSOR: APPLICATIONS OF FIELD AND LABORATORY MEASUREMENTS

2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Pâmela Suélen Käfer
Keyword(s):  
Fourier Transform ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Transfer Equation ◽  
Single Channel ◽  
Radiative Transfer Equation ◽  
Landsat 8 ◽  
Laboratory Measurements ◽  
Ft Ir

ABSTRACT. Land surface temperature (LST) is an important parameter in the investigation of environmental and climatic changes at various scales. However, estimating this parameter from the radiation emitted in the thermal infrared (TIR) region is a difficult task because the radiation measured by the satellite sensors is strongly affected by atmospheric effects. All LST retrieval methods require validation with field measurements. Nonetheless, the validation of this type of data is a challenge because the LST changes rapidly in time and the measurements must be performed together with the sensor overpass. In addition, most methodologies are developed and tested focusing on the Northern Hemisphere. Considering that operational ways of obtaining LST should be constantly investigated, the aim of this paper was to study the effect of the use of temperature-based laboratory measurements in the determination of the emissivity and LST retrieval from orbital remote sensing data. Moreover, it was intended to perform a comparative analysis among the most recent single-channel algorithms available on the literature, applied to band 10 (10.6-11.19 μm) of the Landsat 8 TIRS. The algorithms considered were: Single-Channel generalized (SC), Improved Single-channel (ISC) and Improved Mono-window (IMW). A field of coastal dunes was chosen as study area. Two sets of laboratory emissivity measurements were performed with field samples at different temperatures using a Fourier Transform Infrared (FT-IR). Emissivity and temperature data were obtained in the study area concomitantly with the satellite overpass. The Radiative Transfer Equation (RTE) with parameters of global atmospheric profiles was tested as a method of validation. A variation of approximately 2% in the emissivity in relation to the temperature was observed, which could be neglected. The FT-IR presents limitations on the period to acquire stability, however as long as this limitation is respected and the calibration approach correctly carried out, laboratory measurements can achieve optimum accuracy and replace field validation. Available spectral libraries of emissivity have also proved to be a good alternative. All evaluated single-channel methods are suitable for obtaining LST; however, ISC provided superior results in all analyzes, producing higher R² (0.99978) and lower RMSE (0.019) relative to the other algorithms tested.RECUPERAÇÃO DE TEMPERATURA DE SUPERFÍCIE TERRESTRE DA RADIÂNCIA TERMAL COLETADA PELO SENSOR TIRS/LANDSAT 8: APLICAÇÕES DE MEDIDAS DE CAMPO E LABORATÓRIO RESUMO. A temperatura da superfície terrestre (Land surface temperature - LST) é um importante parâmetro na investigação de mudanças ambientais e climáticas em várias escalas. Entretanto, estimar esse parâmetro da radiação emitida na região do infravermelho termal (TIR) é uma tarefa difícil, pois as radiações medidas pelos sensores dos satélites são fortemente afetadas por efeitos atmosféricos. Todos métodos de recuperação de LST requerem validação com medidas de campo. Porém, a validação deste tipo de dado é um desafio, visto que a LST muda rapidamente no tempo e as medidas devem ser realizadas em conjunto com a passagem do sensor. Além disso, a maioria das metodologias são desenvolvidas e testadas com foco no hemisfério norte. Tendo em vista que maneiras operacionais de se obter LST devem ser constantemente investigadas, o objetivo desta pesquisa foi estudar o efeito do uso de medidas de emissividade de laboratório tomadas com base em temperaturas na determinação da LST a partir de dados de sensoriamento remoto orbital. Ademais, pretendeu-se realizar uma análise comparativa entre os algoritmos single-channel mais recentes existentes na literatura, aplicados à banda 10 (10,6-11,19 μm) do Landsat 8 TIRS. Os algoritmos considerados foram: Single-Channel Generalizado (SCG), Improved Single-Channel (ISC) e Improved Mono-Window (IMW). Um campo de dunas costeiras foi escolhido como área de estudo. Dois conjuntos de medidas de emissividade de laboratório foram construídos com amostras de campo em diferentes temperaturas com uso de um Fourier Transform Infrared (FT-IR). Dados de emissividade e temperatura foram obtidos na área de estudo concomitamente com a passagem do sensor. A equação de transferência radiativa (Radiative Transfer Equation - RTE) com parâmetros de perfis atmosféricos globais foi testada como forma de validação de dados. Uma variação de aproximadamente 2% na emissividade em relação à temperatura foi observada, podendo ser negligenciada. O FT-IR apresenta limitações quanto ao período para adquirir estabilidade, porém respeitando esta limitação e realizando abordagem correta de calibração, medidas laboratoriais podem atingir ótima acurácia e substituir a validação de campo. Bibliotecas espectrais disponíveis de emissividade demonstraram ser também uma alternativa válida. Todos métodos Single-Channel avaliados são adequados para obter LST; no entanto, o ISC forneceu resultados superiores em todas as análises, produzindo maior R² (0,99978) e menor RMSE (0.019) em relação aos demais.

scholarly journals LEAST SQUARE APPROACH FOR ESTIMATING OF LAND SURFACE TEMPERATURE FROM LANDSAT-8 SATELLITE DATA USING RADIATIVE TRANSFER EQUATION

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 97-100
Author(s):  
Y. Jouybari-Moghaddam ◽  
M. R. Saradjian ◽  
A. M. Forati
Keyword(s):  
Radiative Transfer ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
Least Square ◽  
Landsat 8 ◽  
Least Square Approach

Land Surface Temperature (LST) is one of the significant variables measured by remotely sensed data, and it is applied in many environmental and Geoscience studies. The main aim of this study is to develop an algorithm to retrieve the LST from Landsat-8 satellite data using Radiative Transfer Equation (RTE). However, LST can be retrieved from RTE, but, since the RTE has two unknown parameters including LST and surface emissivity, estimating LST from RTE is an under the determined problem. In this study, in order to solve this problem, an approach is proposed an equation set includes two RTE based on Landsat-8 thermal bands (i.e.: band 10 and 11) and two additional equations based on the relation between the Normalized Difference Vegetation Index (NDVI) and emissivity of Landsat-8 thermal bands by using simulated data for Landsat-8 bands. The iterative least square approach was used for solving the equation set. The LST derived from proposed algorithm is evaluated by the simulated dataset, built up by MODTRAN. The result shows the Root Mean Squared Error (RMSE) is less than 1.18°K. Therefore; the proposed algorithm can be a suitable and robust method to retrieve the LST from Landsat-8 satellite data.

scholarly journals Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1778 ◽  
Author(s):  
Md Qutub Uddin Sajib ◽  
Tao Wang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Cross Validation ◽  
Single Channel ◽  
Landsat 8 ◽  
Atmospheric Parameters ◽  
Moderate Resolution ◽  
Split Window Algorithm

The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (A ρ ρ EEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of 1.19 ° C . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of 1.50 ° C . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of 2.47 ° C and 4.11 ° C , respectively. For Du et al.’s algorithm, the w range of 0.0 to 6.3 g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from A ρ ρ EEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

scholarly journals A Comparative Analysis of Retrieval Algorithms of Land Surface Temperature from Landsat-8 Data: A Case Study of Shanghai, China

2021 ◽  
Vol 18 (11) ◽  
pp. 5659
Author(s):  
Yue Jiang ◽  
WenPeng Lin
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Thermal Infrared Remote Sensing ◽  
Retrieval Algorithms ◽  
Split Window Algorithm

In the trend of global warming and urbanization, frequent extreme weather has a severe impact on the lives of citizens. Land Surface Temperature (LST) is an essential climate variable and a vital parameter for land surface processes at local and global scales. Retrieving LST from global, regional, and city-scale thermal infrared remote sensing data has unparalleled advantages and is one of the most common methods used to study urban heat island effects. Different algorithms have been developed for retrieving LST using satellite imagery, such as the Radiative Transfer Equation (RTE), Mono-Window Algorithm (MWA), Split-Window Algorithm (SWA), and Single-Channel Algorithm (SCA). A case study was performed in Shanghai to evaluate these existing algorithms in the retrieval of LST from Landsat-8 images. To evaluate the estimated LST accurately, measured data from meteorological stations and the MOD11A2 product were used for validation. The results showed that the four algorithms could achieve good results in retrieving LST, and the LST retrieval results were generally consistent within a spatial scale. SWA is more suitable for retrieving LST in Shanghai during the summer, a season when the temperature and the humidity are both very high in Shanghai. Highest retrieval accuracy could be seen in cultivated land, vegetation, wetland, and water body. SWA was more sensitive to the error caused by land surface emissivity (LSE). In low temperature and a dry winter, RTE, SWA, and SCA are relatively more reliable. Both RTE and SCA were sensitive to the error caused by atmospheric water vapor content. These results can provide a reasonable reference for the selection of LST retrieval algorithms for different periods in Shanghai.

scholarly journals Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5049 ◽  
Author(s):  
Lei Wang ◽  
Yao Lu ◽  
Yunlong Yao
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Atmospheric Temperature ◽  
Landsat 8 ◽  
Input Parameters ◽  
Successful Launch ◽  
Urban Heat ◽  
Split Window Algorithm

The successful launch of the Landsat 8 satellite provides important data for the monitoring of urban heat island effects. Since the Landsat 8 TIRS data has two thermal infrared bands, it is suitable for many algorithms to retrieve the land surface temperature (LST). However, the selection of algorithms for retrieving the LST, the acquisition of algorithm input parameters, and the verification of the results are problems without obvious solutions. Taking Changchun City as an example, this paper used the mono-window algorithm (MWA), the split window algorithm (SWA), and the single-channel (SC) method to extract the LST from the Landsat 8 image and compared the three algorithms in terms of input parameters, accuracy, and sensitivity. The results show that all three algorithms can achieve good results in retrieving the LST. The SWA is the least sensitive to the error of the input parameters. The MWA and the SC method are sensitive to the error of the input parameters, and compared with the error of the LSE, these two algorithms are more sensitive to the error of atmospheric water vapor content. In addition, the MWA is also very sensitive to the error of the effective mean atmospheric temperature.

scholarly journals The Optimum Method for Urban Land Surface Temperature Estimation

2020 ◽  
Author(s):  
Ekkaluk Salakkham ◽  
Pantip Piyatadsananon
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Landsat 8 ◽  
Optimal Method ◽  
Temperature Estimation ◽  
Retrieval Method ◽  
Window Method ◽  
Split Window Algorithm

Land Surface Temperature (LST) estimation has been studied for several purposes, while the optimal method of estimating the LST has not been criticized yet. This research explores the optimum method in Land Surface Temperature (LST) estimation using LANDSAT-8 imagery data. Four different LST retrieval approaches, the Radiative Transfer Equation-based method (RTE), the Improved Mono-Window method (IMW), the Generalized Single-Channel method (GSC), and the Split-Window algorithm (SW), were calculated to present the LSTs over Buriram Town Municipality, Thailand. The calculated LSTs from these four methods were compared with the ground-based temperature data, taken on the same date and time of the employed LANDSAT-8 images. For this reason, the optimum method of the LST calculation was justified by considering the lowest normalized root means square error (NRMSE) values. As a result, the SW algorithm presents an optimum method in LST estimation. Regarding the SW, this algorithm requires not only the atmospheric profiles during satellite acquisition but also the retrieval of several coefficients. Besides, the LST retrieval method based on the SW algorithm is sensitive to water vapor content and coefficients. Although the SW algorithm is an optimum method explored in this study, it is emphasized that the adjustable values of coefficient response to the atmospheric state may be recommended. With these conditions, the SW algorithm can generate the land-surface temperature over the mixed land-use and land cover on the LANDSAT-8 images.

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