Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data

Borel, Christoph C.

doi:10.1080/01431160802036540

Cited by 94 publications

(81 citation statements)

References 7 publications

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“…Young, Johnson, and Hackwell (2002) proposed an inscene method to correct for atmospheric effects. Subsequently, this method was improved by Borel (2008). Gu et al (2000) developed an autonomous atmospheric compensation method with the same purpose.…”

Section: Methodology Development For Atmospheric Corrections In Hypermentioning

confidence: 99%

“…At SNR = 1200:1, the RMSE (bias) of the surface emissivity is in the range of 0.002−0.004 (−0.0004 to +0.0008), and, at SNR = 240:1, the accuracy of the emissivity is degraded to RMSE = 0.015-0.025 and Bias = −0.009 to +0.015. Borel (2008) pointed out that this type of method suffers from a shift of the spectral position and change in the full width at half maximum (FWHM). He demonstrated that a small spectral position shift (1/20th of the wave centre spacing) can produce large retrieval errors.…”

Section: (3) Isstes Methodsmentioning

confidence: 99%

“…This method also requires atmospheric compensation. However, as the hyperspectral IR data can provide enough information to extract the atmospheric information, Borel (2008) coupled the in-scene atmospheric correction (ISAC) algorithm proposed by Young, Johnson, and Hackwell (2002) with the spectral smoothness technique to generate LST and LSEs from airborne hyperspectral data without knowing the atmospheric state. (4) Emissivity bounds method (EBM)…”

Section: (3) Isstes Methodsmentioning

confidence: 99%

“…(1) Requires accurate atmospheric corrections in TIR channels (2) Accuracy of emissivity and temperature depends largely on the assigned maximum emissivity value Gillespie (1985) and Realmuto (1990) Grey-body emissivity (GBE) method There exists a flat region in the emissivity spectrum (1) No other assumptions on the shape of emissivity spectrum except for a flat region in the emissivity spectrum (1) Requires accurate atmospheric corrections in TIR channels (2) Requires at least two channels having the same emissivity Barducci and Pippi (1996) (Continuned) Gillespie et al (1996Gillespie et al ( , 1998 Borel (1997Borel ( , 1998Borel ( , 2008 Ma et al (2000Ma et al ( , 2002 and Li et al (2007) emissivity comparisons are very challenging due to the heterogeneity of the land surface and the difficulty in the definition of the emissivities themselves (Petitcolin and Vermote 2002). For the spatial variability in LSE, the emissivities measured in the laboratory are not representative of the effective emissivity on a pixel scale.…”

Section: Validation Of Satellite-derived Lsesmentioning

confidence: 99%

See 3 more Smart Citations

Land surface emissivity retrieval from satellite data

Wang

et al. 2012

International Journal of Remote Sensing

379

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: Methodology Development For Atmospheric Corrections In Hypermentioning

confidence: 99%

Section: (3) Isstes Methodsmentioning

confidence: 99%

Section: (3) Isstes Methodsmentioning

confidence: 99%

Section: Validation Of Satellite-derived Lsesmentioning

confidence: 99%

See 2 more Smart Citations

Land surface emissivity retrieval from satellite data

Wang

et al. 2012

International Journal of Remote Sensing

379

200

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“…Then, one can easily find that there are still + 1 unknowns ( LSEs and 1 LST for channels) in the RTE with only equations; therefore, a Temperature and Emissivity Separation (TES) solution, such as the ISSTES algorithm in this paper, is also necessary for determining the LST and LSEs [31][32][33].…”

Section: Basic Theory and Schemementioning

confidence: 99%

Land Surface Temperature and Emissivity Separation from Cross-Track Infrared Sounder Data with Atmospheric Reanalysis Data and ISSTES Algorithm

Zhang

Jiang

et al. 2017

Advances in Meteorology

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The Cross-track Infrared Sounder (CrIS) is one of the most advanced hyperspectral instruments and has been used for various atmospheric applications such as atmospheric retrievals and weather forecast modeling. However, because of the specific design purpose of CrIS, little attention has been paid to retrieving land surface parameters from CrIS data. To take full advantage of the rich spectral information in CrIS data to improve the land surface retrievals, particularly the acquisition of a continuous Land Surface Emissivity (LSE) spectrum, this paper attempts to simultaneously retrieve a continuous LSE spectrum and the Land Surface Temperature (LST) from CrIS data with the atmospheric reanalysis data and the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm. The results show that the accuracy of the retrieved LSEs and LST is comparable with the current land products. The overall differences of the LST and LSE retrievals are approximately 1.3 K and 1.48%, respectively. However, the LSEs in our study can be provided as a continuum spectrum instead of the single-channel values in traditional products. The retrieved LST and LSEs now can be better used to further analyze the surface properties or improve the retrieval of atmospheric parameters.

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Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product

Cheng

Liang

2014

JGR Atmospheres

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A constant land surface longwave emissivity value, or very simple parameterization, has been adopted by current land surface models because of a current lack of reliable observations. Of all the various Earth surface types, bare soil has the highest variations in broadband emissivity (BBE). We propose here a new algorithm to estimate BBE in the 8–13.5 µm spectral range based on the Moderate Resolution Imaging Spectrometer (MODIS) albedo product for bare soil. This algorithm takes advantage of both Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) longwave emissivity and MODIS shortwave albedo products, as well as the established linear relationship between ASTER BBE and seven MODIS spectral albedos for bare soil. In order to mitigate step discontinuities in the global land surface BBE product, a transition zone was established and the BBE estimation method was also provided. Three linear formulae were derived for bare soil and transition zones, respectively. Given the accuracy of 0.01 for MODIS spectral albedo, the absolute accuracy of BBE retrieval is better than 0.017. The validation results obtained from the three field trials conducted in China and one field trial in western/southwestern U.S. indicated that the average difference between the estimated BBE and the measured BBE was 0.016. We have introduced a new strategy to generate global land surface BBE using MODIS data. This strategy was used to generate global 8 day 1 km land surface BBE products from 2000 through 2010.

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Error analysis for a temperature and emissivity retrieval algorithm for hyperspectral imaging data

Cited by 94 publications

References 7 publications

Land surface emissivity retrieval from satellite data

Land surface emissivity retrieval from satellite data

Land Surface Temperature and Emissivity Separation from Cross-Track Infrared Sounder Data with Atmospheric Reanalysis Data and ISSTES Algorithm

Estimating the broadband longwave emissivity of global bare soil from the MODIS shortwave albedo product

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