Land surface temperature retrieval from LANDSAT TM 5

Sobrino, José A.; Jiménez-Muñoz, Juan C.; Paolini, Leonardo

doi:10.1016/j.rse.2004.02.003

Cited by 1,753 publications

(1,091 citation statements)

References 14 publications

(17 reference statements)

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“…Previous studies have proven the presence of a relationship between the Normalized Difference Vegetation Index (NDVI) and the emissivities of terrestrial materials [9,34]. In this study, the NDVI based approach of LSE estimation has been used [9,35]. This algorithm has been applied in the estimation of LSE for various sensors [32,[36][37][38][39] with the use of Visible and Near Infrared (VNIR) data.…”

Section: Estimation Of Land Surface Emissivity (Lse)mentioning

confidence: 99%

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Ndossi

Avdan

2016

Remote Sensing

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Land Surface Temperature (LST) is an important measurement in studies related to the Earth surface's processes. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard the Terra spacecraft is the currently available Thermal Infrared (TIR) imaging sensor with the highest spatial resolution. This study involves the comparison of LSTs inverted from the sensor using the Split Window Algorithm (SWA), the Single Channel Algorithm (SCA) and the Planck function. This study has used the National Oceanic and Atmospheric Administration's (NOAA) data to model and compare the results from the three algorithms. The data from the sensor have been processed by the Python programming language in a free and open source software package (QGIS) to enable users to make use of the algorithms. The study revealed that the three algorithms are suitable for LST inversion, whereby the Planck function showed the highest level of accuracy, the SWA had moderate level of accuracy and the SCA had the least accuracy. The algorithms produced results with Root Mean Square Errors (RMSE) of 2.29 K, 3.77 K and 2.88 K for the Planck function, the SCA and SWA respectively.

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Section: Estimation Of Land Surface Emissivity (Lse)mentioning

confidence: 99%

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Ndossi

Avdan

2016

Remote Sensing

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“…It is estimated from the Normalized Difference Vegetation Index (NDVI) as descripted in [44,45,46,47]. Equation (5) is the basic equation to estimate the NDVI, then it is scaled to three main categories:…”

Section: Land Surface Emissivitymentioning

confidence: 99%

Spatial Variability of Urban Heat Islands in Cairo City, Egypt using Time Series of Landsat Satellite Images

El-Magd

Ismail

Zanaty

2016

IJARSG

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Global warming has obtained more attention because of increasing global mean surface temperature since the late 19th century. Urbanization is considered an important contributor to global warming in big cities. Cairo is one of the heavily populated cities in the world, which has rapid urbanization that resulted in remarkable temperature variation compared to the rural surrounding areas. This phenomenon is known as "Urban Heat Islands" (UHIs) that influence both local and regional climate, environment, and socio-economic development. In this research time series of satellite images were used to map the spatial variability of Land Surface Temperature (LST) and Heat Islands in Cairo city. Historical and contemporary record of Landsat images nearly 16 dates from 1990 to 2014 were used to retrieve LST and land cover categories. Results showed that LST was highly influenced by Land use and land cover changes; heat island effect was dominant in urbanized areas, bare/desert land and industrial zones (e.g. Shobra El-khema). The industrial zones, in particular, were possibly high due to the aluminum roof material plus the thermal energy from production activities and high emissions of air pollutants. The pattern of spatial distribution of heat islands has significantly changed from bare/desert land and built up areas (as warmer) to cultivated land and water bodies (as colder). To analyze the relationship between Heat Islands and land-cover changes a quantitative correlation between LST and Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Build-up Index (NDBI) was employed. It was found that the correlation between LST and NDVI and MNDWI was negative with r 2 of 0.8 and 0.57 respectively; however, the correlation with NDBI was strongly positive with r 2 of 0.81. It is anticipated that the outcomes of this research will contribute to GEOSS for global integration and correlation.

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“…A method allowing these objectives to be met simultaneously is the empirical line method (Karpouzli and Malthus 2003), which requires the spectral reflectance of targets on the ground to be measured at the moment the sensor overpasses, since the reflectance measured at ground level is not affected by any atmospheric effects. Night-and day-time Earth range bands were also calibrated, assuming a linear relationship between brightness and land surface temperature (Minacapilli et al 2009a), through ground truth data (brightness temperature) measured during both the night-and day-time flights, by taking into account surface emissivity through a vegetation index; indeed, since the ATM sensor has a single channel in the thermal infrared part of the spectrum, in this research we estimate the emissivity from the vegetation fractional cover assuming a value of emissivity for bare soil and dense vegetated areas according to Sobrino et al (2004). Once remote sensing images have been preprocessed, the thermal inertia modelling can be applied.…”

Section: Methodsmentioning

confidence: 99%

Critical analysis of thermal inertia approaches for surface soil water content retrieval

Maltese

Bates

Capodici

et al. 2013

Hydrological Sciences Journal

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The "thermal inertia" method to retrieve surface soil water content maps on bare or sparsely-vegetated soils is analysed. The study area is a small experimental watershed, where optical and thermal images (in day and night time) and in situ data were simultaneously acquired. The sensitivity of thermal inertia to the phase difference between incoming radiation and soil temperature is demonstrated. Thus, to obtain an accurate value of the phase difference, the temporal distance between thermographs using a three-temperature approach is evaluated. We highlight when a cosine correction of the temperature needs to be applied, depending on whether the thermal inertia formulation includes two generic acquisition times, or not. Finally, the deviation in soil water content retrieval is quantifies for given values of each parameter by performing a sensitivity analysis on the basic parameters of the thermal inertia method that are usually affected by calibration errors.Key words thermal inertia; surface soil water content; remote sensing Analyse critique des approches fondées sur l'inertie thermique pour la cartographie de la teneur en eau de la surface du sol Résumé La méthode de « l'inertie thermique » pour la cartographie de la teneur en eau de la surface des sols nus ou à végétation clairsemée est analysée. La zone d'étude est un petit bassin versant expérimental, où des images optiques et thermiques (de jour et de nuit) et des données in situ ont été acquises simultanément. On a mis en évi-dence la sensibilité de l'inertie thermique à la différence de phase entre le rayonnement incident et la température du sol. Ainsi, pour obtenir une valeur précise de la différence de phase, nous avons évalué la distance temporelle entre thermographes en utilisant une approche à trois températures. Cet article montre la nécessité éventuelle d'une correction cosinusoïdale de la température, selon que la formulation de l'inertie thermique comprend ou non deux temps d'acquisition génériques. Enfin, l'article quantifie la variabilité de la teneur en eau du sol pour des valeurs données de chaque paramètre en effectuant une analyse de sensibilité sur les paramètres de base de la méthode de l'inertie thermique qui sont généralement affectés par des erreurs d'étalonnage.

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Land surface temperature retrieval from LANDSAT TM 5

Cited by 1,753 publications

References 14 publications

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Spatial Variability of Urban Heat Islands in Cairo City, Egypt using Time Series of Landsat Satellite Images

Critical analysis of thermal inertia approaches for surface soil water content retrieval

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