Brightness temperature algorithms for landsat thematic mapper data

Singh, Surinder

doi:10.1016/0034-4257(88)90022-3

Cited by 19 publications

(10 citation statements)

References 1 publication

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“…The atmospheric correction software ATCOR2 (Richter 1996) processed both thermal bands. ATCOR2 uses the theoretical approach by Singh (1988). The calibration coefficients for the low gain band 6 (20 October 2000) were as follows: offset~0, gain~0.00668 (mW cm {2 sr {1 mm {1 ).…”

Section: Image Processingmentioning

confidence: 99%

Landsat 7 night imaging of the Nissyros Volcano, Greece

Ganas

Λάγιος

2003

International Journal of Remote Sensing

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A night-time image of Nissyros from the ETMz (Enhanced Thematic Mapperz) scanner onboard the Landsat 7 satellite was acquired in October 2000. The image was processed to compute surface temperature on the volcano while contemporaneous field measurements on the island's surface were collected. We confirm that the thermal sensor of Landsat 7 can map (a) the 'orographic effect' on land surface temperatures (temperature falling with increasing elevations) and (b) the crater surface temperature within an accuracy of 0.4-2 ‡ C. In addition, the low-temperature fumarolic activity of the volcano could not be detected on the mid-infrared bands (5 and 7). However, there are some high-frequency temporal variations of surface temperature inside the main crater that cannot be mapped because of the revisit capability of the sensor (16 days).

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Section: Image Processingmentioning

confidence: 99%

Landsat 7 night imaging of the Nissyros Volcano, Greece

Ganas

Λάγιος

2003

International Journal of Remote Sensing

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“…There are also some works based on the empirical formulas of band-averaged radiance (Schott and Volchok 1985, Singh 1988, Wukelic et al 1989, Goetz et al 1995, Sospedra et al 1998, Liang 2003. These methods have very simple explicit formulas and high efficiency, but limited accuracy.…”

Section: Empirical Formula Methodsmentioning

confidence: 98%

“…It is therefore desirable to develop an algorithm that can invert brightness temperature with high accuracy and exploit the advantages of the hardware to improve performance. There are already some approaches for brightness temperature inversion (Schott and Volchok 1985, Singh 1988, Wukelic et al 1989, Goetz et al 1995, Sospedra et al 1998, Alley and Nilsen 1999, Badenas and Onrubia 2003, Liang 2003, which can be divided into three categories: iteration methods, Look-Up- Table (LUT) methods, and empirical formula methods. These methods have differences in accuracy, performance and flexibility.…”

Section: Introductionmentioning

confidence: 99%

A high‐performance approach for brightness temperature inversion

Hao

Hauss

et al. 2007

International Journal of Remote Sensing

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Brightness temperature inversion is one of the most essential tasks in satellite remote sensing data processing. Accurate calculation of brightness temperature is crucial for many remote sensing applications, such as land surface temperature retrieval, sea surface temperature retrieval, and active fire detection. Due to the huge amount of remote sensing data, performance is also very important for operational use. Major current approaches for brightness temperature inversion are iteration methods, Look-Up- Table (LUT) methods, and empirical formula methods. Some of these algorithms can invert brightness temperature efficiently with limited accuracy, while others can invert brightness temperature at high accuracy but have lower performance. It is desirable to develop an algorithm that can make the balance of accuracy and performance more flexible and can invert brightness temperature efficiently even at high accuracy. In this paper, we analyse the advantages and limitations of the current algorithms for brightness temperature inversion, and propose a new approach based on the high-order approximation of the relationship between band-averaged radiance and brightness temperature. Our approach has the advantages of high accuracy and flexibility. It can be vectorized easily to exploit the advanced features of current computing platforms and improve performance in operational data processing. For validation and analysis, we have applied this method to brightness temperature inversion of MODIS thermal infrared (TIR) measurements and compared with other algorithms for accuracy and performance. The results demonstrate that our approach can be used for operational data processing more flexibly and efficiently.

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“…Roof surface temperature, T surface can then be calculated by numerical solution of Planck's law over the wavelength band pass of interest weighted for the detector response function, normalised to unity, after Singh [58] as suggested by Snyder and Schott [20] . This has been simplified to the approximation in equation 4 where a and b are parameters determined from a least squares fit between ln(L T ) and 1/T surface calculated for a number of points in the temperature range [20,58] .…”

Section: Calculation Of Roof Surface Temperaturementioning

confidence: 99%

Toward quantitative aerial thermal infrared thermography for energy conservation in the built environment

2005

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The UK Home Energy Conservation Act puts a duty on local authorities to develop strategies to improve energy efficiency in all public and private sector housing in order to tackle fuel poverty and reduce carbon dioxide emissions. The City of Nottingham, UK turned to aerial Thermal InfraRed Thermography (TIRT) to try and identify households where energy savings can be made. In this paper, existing literature is reviewed to explain the limitations of aerial TIRT for energy conservation in the built environment and define the techniques required to overcome them. This includes the range of suitable meteorological conditions at the time of the survey, the use of ground truth data, the need to account for all radiation paths and losses when calculating roof surface temperature and the assumptions that must be made when calculating insulation levels. Atmospheric calibration, roof surface emissivity and sky view factor must also be determined by some means and approaches to these problems are reviewed from the wider literature. Error analysis and benchmarking are important if the technique is to be validated and these are discussed with reference to the literature. A methodology for determining the thickness of loft insulation for residential buildings in the city of Nottingham, UK using aerial TIRT data within a GIS software environment is proposed.

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Brightness temperature algorithms for landsat thematic mapper data

Cited by 19 publications

References 1 publication

Landsat 7 night imaging of the Nissyros Volcano, Greece

Landsat 7 night imaging of the Nissyros Volcano, Greece

A high‐performance approach for brightness temperature inversion

Toward quantitative aerial thermal infrared thermography for energy conservation in the built environment

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