Evaluation of six methods for extracting relative emissivity spectra from thermal infrared images

The performance of sir published methods for extracting relative spectral e missivity information from thermal infrared multispectral data has been eva luated. In the first part of this article, we recall those six methods and show mathematically that they are almost equivalent to each other. Then, us ing simulated data for the TIMS (Thermal Infrared Multispectral Scanner) in strument, we analyze the sensitivity of those methods to different sources of error which may occur in real data such as errors due to 1) method simpl ification, 2) instrumental noise and systematic calibration error, 3) uncer tainties on the estimation of downwelling atmospheric radiance, and 4) unce rtainties of atmospheric parameters in atmospheric corrections. In terms of resulting errors in relative emissivity, the results show that: a) all met hods are very sensitive to the uncertainties of atmosphere. An error of 20% of water vapor in midlatitude summer atmosphere (2.9 cm) may lead to an er ror of 0.03 (rms) for Channel 1 (worst case) of TIMS. b) The effect of the atmospheric reflection term is very important. If this term is neglected in method development, this may lead to an error of 0.03 (mns) for Channel 1 and midlatitude summer atmosphere. This Is the case for the alpha method. c ) Instrumental noise commonly expressed by noise equivalent difference temp erature (NE Delta T) from 0.1 K to 0.3 K results in an error on relative em issivity ranging from 0.002 to 0.005 for all methods. d) Error on relative emissivity due to the instrumental calibration error (systematic error;) is negligible. The study also shows that the relative emissivity derived with deviate atmosphere is linearly related to its actual value derived with co rrect atmospheric parameters. Based on this property, we propose three meth ods to correct for the errors caused by atmospheric corrections render hori zontally invariant atmospheric conditions. A practical analysis with the re al TIMS data acquired for Hapex-Sahel experiment in 1992 supports the resul ts of this simulation. (C) Elsevier Science Inc., 1999.