Land surface temperature and emissivity estimation based on the two-temperature method: sensitivity analysis using simulated MSG/SEVIRI data

A feasibility study of land surface temperature (LST) and land surface emissivity (LSE) estimation is presented using METEOSAT Second Generation (MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, based on the two-temperature method (TTM). The performance of TTM was assessed by taking...

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Bibliographic Details
Published in:Remote sensing of environment Vol. 91; no. 3; pp. 377 - 389
Main Authors: Peres, Leonardo F., DaCamara, Carlos C.
Format: Journal Article
Language:English
Published: New York, NY Elsevier Inc 01.06.2004
Elsevier Science
Subjects:
Animal, plant and microbial ecology
Applied geophysics
Biological and medical sciences
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Internal geophysics
Land surface emissivity
Land surface temperature
MSG/SEVIRI
Sensitivity analysis
Teledetection and vegetation maps
Land surface emissivity
Sensitivity analysis
Land surface temperature
MSG/SEVIRI
atmosphere
models
sensitivity analysis
Space remote sensing
covariance
assimilation
feasibility studies
channels
circulation
noise
temperature
seasonal variations
errors
ISSN:0034-4257, 1879-0704
Online Access:Get full text
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Summary:A feasibility study of land surface temperature (LST) and land surface emissivity (LSE) estimation is presented using METEOSAT Second Generation (MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, based on the two-temperature method (TTM). The performance of TTM was assessed by taking into consideration the noise in SEVIRI channels IR 10.8 and IR 12.0 as well as the errors in the atmospheric profiles. Imposed errors due to uncertainties on atmospheric information were generated based on the background error covariance matrix used in the assimilation scheme of the European Centre for Medium-Range Weather Forecasts (ECMWF) Global Circulation Model. TTM has provided LST values with averaged error bias [root-mean square error (RMSE)] ranging from 0.0 to 0.5 K (from 0.8 to 2.5 K) for geographical–seasonal model atmospheres stored in MODTRAN4 with water vapor content varying between 0.85 and 4.11 g cm −2. Obtained results suggest that TTM may be used as a complementary method to split-window (SW) algorithms over areas where LSE is not well known a priori.
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ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2004.03.011