Temperature and emissivity retrieval from remotely sensed images using the "Grey body emissivity" method

Barducci, Alessandro; Pippi, Ivan

doi:10.1109/36.499748

Cited by 90 publications

(44 citation statements)

References 12 publications

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“…the two-temperature method (TTM) (Watson 1992); by assuming that the emissivity has a flat spectrum for specific wavelengths, e.g. the grey-body emissivity (GBE) method (Barducci and Pippi 1996); by assuming that the emissivity spectrum is smooth, e.g. the iterative spectrally smooth temperature emissivity separation (ISSTES) method (Borel 1997); or by applying a priori knowledge about the emissivity distribution range, e.g.…”

Section: Emissivity Spectrum Character-based Methodsmentioning

confidence: 99%

“…(1) GBE method Assuming that the emissivity has a flat spectrum for wavelengths larger than 10 µm, Barducci and Pippi (1996) suggested estimating the emissivity spectrum from remotely sensed data using the flat characteristics of the emissivity spectrum in some wavelength intervals. On the basis of the assumption that…”

Section: Emissivity Spectrum Character-based Methodsmentioning

confidence: 99%

“…Provided that M is less than N, i.e. there are at least two channels having the same values of emissivities in N channels, Equation (11) gives a well-determined solution for e and t (Barducci and Pippi 1996). It should be noted that the application of the GBE method to space measurements requires correcting for atmospheric effects and also requires that at least two channels have the same emissivity but not necessarily the grey body.…”

Section: Emissivity Spectrum Character-based Methodsmentioning

confidence: 99%

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Land surface emissivity retrieval from satellite data

Wang

et al. 2012

International Journal of Remote Sensing

395

200

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As an intrinsic property of natural materials, land surface emissivity (LSE) is an important surface parameter and can be derived from the emitted radiance measured from space. Besides radiometric calibration and cloud detection, two main problems need to be resolved to obtain LSE values from space measurements. These problems are often referred to as land surface temperature (LST) and emissivity separation from radiance at ground level and as atmospheric corrections in the literature. To date, many LSE retrieval methods have been proposed with the same goal but different application conditions, advantages, and limitations. The aim of this article is to review these LSE retrieval methods and to provide technical assistance for estimating LSE from space. This article first gives a description of the theoretical basis of LSE measurements and then reviews the published methods. For clarity, we categorize these methods into (1) (semi-)empirical or theoretical methods, (2) multi-channel temperature emissivity separation (TES) methods, and (3) physically based methods (PBMs). This article also discusses the validation methods, which are of importance in verifying the uncertainty and accuracy of retrieved emissivity. Finally, the prospects for further developments are given.

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Section: Emissivity Spectrum Character-based Methodsmentioning

confidence: 99%

Section: Emissivity Spectrum Character-based Methodsmentioning

confidence: 99%

Section: Emissivity Spectrum Character-based Methodsmentioning

confidence: 99%

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Land surface emissivity retrieval from satellite data

Wang

et al. 2012

International Journal of Remote Sensing

395

200

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“…For the ground surface, the components composing the surface are the main factors determining the ground emissivity [33][34][35]. Many effective methods have been approved to estimate the emissivity for LST retrieval [36,37]. Since the emissivity is variable with the wavelength, the NDVI threshold method (NTM) [38] can be used to estimate the emissivity of different land surfaces in the 10-12 µm range.…”

Section: Determination Of Ground Emissivitymentioning

confidence: 99%

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

et al. 2015

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Abstract:The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for OPEN ACCESSRemote Sens. 2015, 7 4269 the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

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“…Therefore, the assumption of a ground-based plume is acceptable for the night case, but is unrealistic for the diurnal case where a plume based at 3300 m gives better results. In fact, during the night the coupled action of the atmospheric stability near the ground [Stull, 1991;Oke, 1987] (radiosonde data acquired on July 25 give a stable layer starting from the ground and 150 m thick) and the nocturnal downhill katabatic wind [Garratt, 1994;Barry, 1992;Stull, 1991] (Table 5). Thus, knowing the plume thickness with a precision of 50 m seems to be sufficient.…”

Section: Maps Of So2 Columnar Contentmentioning

confidence: 99%

Evaluation of SO₂ emission from Mount Etna using diurnal and nocturnal multispectral IR and visible imaging spectrometer thermal IR remote sensing images and radiative transfer models

Teggi¹,

Bogliolo²,

Buongiorno³

et al. 1999

J. Geophys. Res.

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Abstract. We describe a model to evaluate the volcanic SO2 emission using images acquired by the airborne multispectral infrared and visible imaging spectrometer (MIVIS) in the thermal infrared spectral region. The estimating procedure consists of mapping of the SO2 columnar content and evaluation of the total SO2 flux emitted by the volcano. All the atmospheric radiative effects, including the SO2 plume emission/absorption, are computed by the MODTRAN 3.5 radiative transfer code. We apply the model to two MIVIS images acquired over Mount Etna, located on the east coast of Sicily, Italy, during the "Sicilia-94" remote sensing campaign on July 24 and 25, 1994. The June 25 image was acquired during the night; this is the first attempt to estimate the SO2 content in volcanic plume using nocturnal thermal infrared images. The total flux estimates range from 20.9 kg s '• (1810 t d' •) to 82.2 kg s 4 (7100 t d' ) depending on the plume geometry used in the procedure. These results, except one case (82.2 kg s' ), are in agreement with the estimates derived from correlation spectrometer (COSPEC) measurements collected during the same period. We also evaluate the dependencies of the results on several parameters such as plume geometry, surface emissivity, water vapor content, and wind speed.

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Temperature and emissivity retrieval from remotely sensed images using the "Grey body emissivity" method

Cited by 90 publications

References 12 publications

Land surface emissivity retrieval from satellite data

Land surface emissivity retrieval from satellite data

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

Evaluation of SO₂ emission from Mount Etna using diurnal and nocturnal multispectral IR and visible imaging spectrometer thermal IR remote sensing images and radiative transfer models

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Temperature and emissivity retrieval from remotely sensed images using the "Grey body emissivity" method

Cited by 90 publications

References 12 publications

Land surface emissivity retrieval from satellite data

Land surface emissivity retrieval from satellite data

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

Evaluation of SO2 emission from Mount Etna using diurnal and nocturnal multispectral IR and visible imaging spectrometer thermal IR remote sensing images and radiative transfer models

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Evaluation of SO₂ emission from Mount Etna using diurnal and nocturnal multispectral IR and visible imaging spectrometer thermal IR remote sensing images and radiative transfer models