scholarly journals Validation of Physical Radiative Transfer Equation-Based Land Surface Temperature Using Landsat 8 Satellite Imagery and SURFRAD in-situ Measurements

2019 ◽  
Vol 196 ◽  
pp. 105161 ◽  
Author(s):  
Aliihsan Sekertekin
Keyword(s):  
Radiative Transfer ◽  
Surface Temperature ◽  
Satellite Imagery ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
In Situ Measurements ◽  
Landsat 8

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 Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models

2020 ◽  
Vol 12 (17) ◽  
pp. 2776 ◽  
Author(s):  
Aliihsan Sekertekin ◽  
Stefania Bonafoni
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
The United States ◽  
In Situ Measurements ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Sensitivity Analyses ◽  
Landsat 8

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel Algorithm (SCA), Mono Window Algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models presented the best results for the daytime. Concerning the nighttime accuracy of the LST retrieval, the RMSE value ranged from 0.94 K to 3.34 K. SCA showed the best results, but MWA and RTE also provided very high accuracy. Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements.

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 Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

2020 ◽  
Vol 12 (5) ◽  
pp. 791 ◽  
Author(s):  
Jingjing Yang ◽  
Si-Bo Duan ◽  
Xiaoyu Zhang ◽  
Penghai Wu ◽  
Cheng Huang ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Landsat 8 ◽  
Atmospheric Profiles ◽  
Atmospheric Profile

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.

scholarly journals Validation of Collection 6 MODIS land surface temperature product using in situ measurements

2019 ◽  
Vol 225 ◽  
pp. 16-29 ◽  
Author(s):  
Si-Bo Duan ◽  
Zhao-Liang Li ◽  
Hua Li ◽  
Frank-M. Göttsche ◽  
Hua Wu ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
In Situ Measurements ◽  
Modis Land Surface Temperature

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 Retrieval of Land Surface Temperature of Lahore Through Landsat-8 TIRS Data

2019 ◽  
Vol 10 (1) ◽  
pp. 70-77
Author(s):  
Muhammad Nasar -u-Minallah
Keyword(s):  
Radiative Transfer ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Global Climate ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Barren Land ◽  
Data Continuity

Land surface temperature (LST) is an important parameter in global climate change and urban thermalenvironmental studies. The significance of land surface temperature is being acknowledged gradually and interest isincreasing in developing methodologies for the retrieval of LST from Satellite Remote Sensing (SRS) data. ThermalInfrared Sensor (TIRS) of Landsat-8 is the newest TIR sensor for the Landsat Data Continuity Mission (LDCM),offering two adjacent thermal infrared bands (10, 11), having significant beneficiary for the land surface temperatureinversion. The spectral radiance can be estimated through TIR bands 10 and 11 of Landsat-8 OLI_TIRS satellite image.In the present study, the radiative transfer equation-based method has been employed in estimating LST of Lahore andthe analysis demonstrated that estimated LST has the highest accuracy from the radiative transfer method through band10. Land Surface Emissivity (LSE) was derived with the aid of the NDVI’s threshold technique. The present studyresults show that as the built-up area increases and vegetation cover decreases in urban surface, they are linked toincrease in urban land surface temperature and conversely larger vegetation cover associated with lower urbantemperature. The output exposed that LST was high in built-up and barren land, whereas it was low in the area wherethere were more vegetation cover and water.

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