Regression of Surface Spectral Emissivity From Hyperspectral Instruments

Zhou, Lihang; Goldberg, Mitchell D.; Barnet, C.; Cheng, Zhaohui; Sun, Fengying; Wolf, W.; King, Thomas; Liu, Xingpin; Sun, Haibing; Divakarla, Murty

doi:10.1109/tgrs.2007.912712

Cited by 40 publications

(35 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Land surface broadband emissivity (BBE) is a key parameter in the estimation of surface energy budget and is a common input required for a variety of radiative transfer models [1][2][3][4][5][6][7][8]. Because of limited temporal and spectral information on land surface emissivity, a constant BBE assumption or simple parameterization schemes are adopted in land surface models and climate models [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, some researchers have produced a few narrowband emissivity products from which the BBE could be obtained by converting it to BBE via a linear regression function [17,18], including the North American ASTER Land Surface Emissivity Database (NAALSED) composited from the ASTER narrowband emissivity product [19], the University of Wisconsin Global Infrared Land Surface Emissivity Database (UWIREMIS) retrieved by adjusting MODIS narrowband emissivity product (MOD11) with the proposed baseline fit method [20], and those derived from hyperspectral resolution thermal infrared (TIR) data [2,[21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

et al. 2013

View full text Add to dashboard Cite

Abstract:This study compared three broadband emissivity (BBE) datasets from satellite observations. The first is a new global land surface BBE dataset known as the Global Land Surface Satellite (GLASS) BBE. The other two are the North American ASTER Land Surface Emissivity Database (NAALSED) BBE and University of Wisconsin Global Infrared Land Surface Emissivity Database (UWIREMIS) BBE, which were derived from two independent narrowband emissivity products. Firstly, NAALSED BBE was taken as the reference to evaluate the GLASS BBE and UWIREMIS BBE. The GLASS BBE was more close to NAALSED BBE with a bias and root mean square error (RMSE) of −0.001 and 0.007 for the summer season, −0.001 and 0.008 for the winter season, respectively. Then, the spatial distribution and seasonal pattern of global GLASS BBE and UWIREMIS BBE for six dominant land cover types were compared. The BBE difference between vegetated areas and non-vegetated areas can be easily seen from two BBEs. The seasonal variation of GLASS BBE was more reasonable than that of UWIREMIS BBE. Finally, the time series were calculated from GLASS BBE and UWIREMIS BBE using the data from 2003 through 2010. The periodic variations of GLASS BBE were stronger than those of UWIREMIS BBE. The long time series high quality GLASS BBE can be incorporated in land surface models for improving their simulation results.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Also, the emissivity is usually not updated simultaneously along with the profiles in the physical iterative retrieval (Li et al 2000;Zhou et al 2006). To improve LST and atmospheric sounding retrievals from current and future IR sounders, global IR LSE datasets have been derived by numerous researchers (Seemann et al 2008;Pé quignot et al 2008;Zhou et al 2002Zhou et al , 2008. For example, Seemann et al (2008) have improved the LSE with a synthetic regression-based surface emissivity scheme derived from a training database.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al (2007) developed techniques to separate surface skin temperature and surface emissivity by representing the emissivity spectrum as eigenvectors (EV) derived from laboratory spectra in a one-dimensional variational (1DVAR) approach, and global high-spectralresolution LSE spectra from AIRS radiance measurements were derived . Zhou et al (2008) used an efficient synthetic regression approach to derive global LSE from AIRS. This approach is based on clear-sky radiances simulated from the European Centre for Medium-Range Weather Forecasts (ECMWF) forecast and a surface emissivity training dataset, and LSE spectra from the Infrared Atmospheric Sounding Interferometer (IASI) have also been derived ) by the physically statistical approach.…”

Section: Introductionmentioning

confidence: 99%

Surface Emissivity Impact on Temperature and Moisture Soundings from Hyperspectral Infrared Radiance Measurements

Yao¹,

Li²,

Li³

et al. 2011

Journal of Applied Meteorology and Climatology

View full text Add to dashboard Cite

An accurate land surface emissivity (LSE) is critical for the retrieval of atmospheric temperature and moisture profiles along with land surface temperature from hyperspectral infrared (IR) sounder radiances; it is also critical to assimilating IR radiances in numerical weather prediction models over land. To investigate the impact of different LSE datasets on Atmospheric Infrared Sounder (AIRS) sounding retrievals, experiments are conducted by using a one-dimensional variational (1DVAR) retrieval algorithm. Sounding retrievals using constant LSE, the LSE dataset from the Infrared Atmospheric Sounding Interferometer (IASI), and the baseline fit dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) are performed. AIRS observations over northern Africa on 1-7 January and 1-7 July 2007 are used in the experiments. From the limited regional comparisons presented here, it is revealed that the LSE from the IASI obtained the best agreement between the retrieval results and the ECMWF reanalysis, whereas the constant LSE gets the worst results when the emissivities are fixed in the retrieval process. The results also confirm that the simultaneous retrieval of atmospheric profile and surface parameters could reduce the dependence of soundings on the LSE choice and finally improve sounding accuracy when the emissivities are adjusted in the iterative retrieval. In addition, emissivity angle dependence is investigated with AIRS radiance measurements. The retrieved emissivity spectra from AIRS over the ocean reveal weak angle dependence, which is consistent with that from an ocean emissivity model. This result demonstrates the reliability of the 1DVAR simultaneous algorithm for emissivity retrieval from hyperspectral IR radiance measurements.

show abstract

“…Several algorithms have been developed to derive IR emissivity from satellite measurements [5][6][7][8][9][10][11][12] . Recent work 13 based on Zhou et al 5 has been published.…”

Section: Introductionmentioning

confidence: 99%

How well can infrared sounders observe the atmosphere and surface through clouds?

et al. 2010

View full text Add to dashboard Cite

Infrared sounders, such as the Atmospheric Infrared Sounder (AIRS), the Infrared Atmospheric Sounding Interferometer (IASI), and the Cross-track Infrared sounder (CrIS), have a cloud-impenetrable disadvantage in observing the atmosphere and surface under opaque cloudy conditions. However, recent studies indicate that hyperspectral, infrared sounders have the ability to detect cloud effective-optical and microphysical properties and to penetrate optically thin clouds in observing the atmosphere and surface to a certain degree. We have developed a retrieval scheme dealing with atmospheric conditions with cloud presence. This scheme can be used to analyze the retrieval accuracy of atmospheric and surface parameters under clear and cloudy conditions. In this paper, we present the surface emissivity results derived from IASI global measurements under both clear and cloudy conditions. The accuracy of surface emissivity derived under cloudy conditions is statistically estimated in comparison with those derived under clear sky conditions. The retrieval error caused by the clouds is shown as a function of cloud optical depth, which helps us to understand how well infrared sounders can observe the atmosphere and surface through clouds.

show abstract

Regression of Surface Spectral Emissivity From Hyperspectral Instruments

Cited by 40 publications

References 8 publications

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

A Comparative Study of Three Land Surface Broadband Emissivity Datasets from Satellite Data

Surface Emissivity Impact on Temperature and Moisture Soundings from Hyperspectral Infrared Radiance Measurements

How well can infrared sounders observe the atmosphere and surface through clouds?

Contact Info

Product

Resources

About