A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6

Zhang, Jinqu; Wang, Yunpeng; Li, Yan

doi:10.1016/j.cageo.2006.05.001

Cited by 131 publications

(61 citation statements)

References 16 publications

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“…where T s is the surface radiant temperature in Kelvin (k); T B is the effective at sensor brightness temperature in Kelvin; λ is the wavelength of emitted radiance, herein, λ = 11.5 µm [41]; α = hc/b 1.438 × 10 −2 m·K ; b is the Bolzmann constant 1.38 × 10 −23 J/K ; h is Plank's constant 6.626 × 10 −34 Js ; c is the velocity of light 2.998 × 10 8 m/s ; and ε is the surface emissivity which can be calculated from NDVI [42,43], the estimation was shown in Table 2 below.…”

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confidence: 99%

Evaluation of Urbanization Dynamics and its Impacts on Surface Heat Islands: A Case Study of Beijing, China

et al. 2017

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Abstract:As the capital of China, Beijing has experienced a continued and rapid urbanization process in the past few decades. One of the key environmental impacts of rapid urbanization is the effect of urban heat island (UHI). The objective of this study was to estimate the urbanization indexes of Beijing from 1992 to 2013 based on the stable nighttime light (NTL) data derived from the Defense Meteorological Satellite Program's Operational Line Scanner System (DMSP/OLS), which has became a widely used remote sensing database after decades of development. The annual average value nighttime light Digital Number (NTL-DN), and the total lit number and urban area proportion within Beijing's boundary were calculated and compared with social-economic statistics parameters to estimate the correlation between them. Four Landsat thematic mapper (TM) images acquired in 1995 and 2009 were applied to estimate the normalized difference vegetation index (NDVI) and normalized land surface temperature (LST nor ), and spatial correlation analysis was then carried out to investigate the relationship between the urbanization level and NDVI and LST nor . Our results showed a strong negative linear relationship between the NTL-DN value and NDVI; however, in contrast, a strong positive linear relationship between existed between the NTL-DN value and LST nor . By conducting a spatial comparison analysis of 1995 and 2009, the vegetation coverage change and surface temperature difference were calculated and compared with the NTL-DN difference. Our result revealed that the regions of fast urbanization resulted in a decrease of NDVI and increase of LST nor . In addition, choropleth maps showing the spatial pattern of urban heat island zones were produced based on different temperatures, and the analysis result indicated that the spatial distribution of surface temperature was closely related with the NTL-DN and NDVI. These findings are helpful for understanding the urbanization process as well as urban ecology, which both have significant implications for urban planning and minimize the potential environmental impacts of urbanization in Beijing.

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confidence: 99%

Evaluation of Urbanization Dynamics and its Impacts on Surface Heat Islands: A Case Study of Beijing, China

et al. 2017

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“…Then spectral radiance values of thermal bands were converted to brightness temperature by means of equation (1). where T = effective at-satellite temperature in Kelvin Lλ = spectral radiance at the sensor's aperture K1, K2 = calibration constants The other step is the estimation of Land Surface Emissivity (LSE) by using NDVI. A detailed estimation of LSE from NDVI was proposed by Zhang et al (2006).…”

Section: Methodsmentioning

confidence: 99%

Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

Şekertekin¹,

Kutoğlu

Kaya

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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 splitwindow 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.

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“…The value of land surface temperature (LST) was calculated on the basis of channels six of the Landsat ETM+ satellite images (spatial resolution of channels of visible light and near infrared: approximately 30 metres, thermal channel: 60 metres per pixel) with the use of the LST software (Zhang et al 2006). They described the methodology of obtaining the values of land surface temperature directly from channel six of the TM/ETM+ sensor on the basis of the algorithm of Qin et al (2001) as well as algorithm of Jimén-ez- Muňoz et al (2003).…”

Section: Methodolgymentioning

confidence: 99%

“…In order to calculate the LST in the LST software (Zhang et al 2006) with the use of the algorithm of Qin et al (2001), it is necessary to provide three parameters: T a (average actual temperature of the atmosphere), ε (soil emissiveness) as well as τ 6 (atmospheric transparency). On the other hand, the algorithm of Jiménez-Muňoz and Sobrino (2003) requires providing two values: the value of soil emissiveness ε (as in the case of the algorithm of Qin et al 2001) as well as the total water vapour content in the atmosphere w.…”

Section: Methodolgymentioning

confidence: 99%

Influence of Relief and Land Cover on the Distribution of Values of the Land Surface Temperature in Upper Parsęta River Area

Kubiak

Stach

2013

Quaestiones Geographicae

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Abstract. The primary research problem presented in the article is verification of the thesis on the influence of relief and land cover type on the spatial variability of the land surface temperature (LST) distribution in the area including the river catchment area of upper Parsęta. The paper presents the use of thermal channels from two Landsat ETM+ scenes pictures, Corine Land Cover database from 2000 as well as the DTED-2 digital elevation model. Two ETM+ thermal bands processing algorithms were used for calculation of the land surface temperature: Qin et al. (2001) and Jiménez-Muňoz et al. (2003). Conducted statistical tests show significant differences of the land surface temperature values between particular land cover forms as well as types of relief. LST maps can be applied in topoclimatology eg. to detail and verify the in situ measurements.

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A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band6

Cited by 131 publications

References 16 publications

Evaluation of Urbanization Dynamics and its Impacts on Surface Heat Islands: A Case Study of Beijing, China

Evaluation of Urbanization Dynamics and its Impacts on Surface Heat Islands: A Case Study of Beijing, China

Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

Influence of Relief and Land Cover on the Distribution of Values of the Land Surface Temperature in Upper Parsęta River Area

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