Digital Imaging and Remote Sensing Laboratory

Thermal Infrared Spectral AnalysisThermal Infrared Spectral Analysis

Thermal absorption/emissivity structure (3-20 µm)

can be indicative of the structure and make up of

materials (particularly minerals).

Figure 5.1 shows variation in laboratory spectra

with structure and, 5.2 shows variation between

rock types

Digital Imaging and Remote Sensing Laboratory

Thermal Infrared Spectral Analysis (cont’d)Thermal Infrared Spectral Analysis (cont’d)

Fig. 5.1 Thermal infrared transmission spectra of silicate

minerals showing the correlation between band location

(vibrational energy) and mineral structure.

Digital Imaging and Remote Sensing Laboratory

Thermal Infrared Spectral Analysis (cont’d)Thermal Infrared Spectral Analysis (cont’d)

Fig. 5.2. Emission spectra of various rock types showing resistrahlen minima (Vickers

and Lyon, 1967)

Digital Imaging and Remote Sensing Laboratory

Thermal Infrared Spectral Analysis (cont’d)Thermal Infrared Spectral Analysis (cont’d)

The problem in sampling in this spectral region (3-

20 µm) is that the signal is heavily influenced by

thermal effects.

The spectral radiance signature can be expressed

)()())(1()()()( 22 udT LLLL

Digital Imaging and Remote Sensing Laboratory

Thermal Infrared Spectral Analysis (cont’d)Thermal Infrared Spectral Analysis (cont’d)

The atmospheric variables can be solved for using

LOWTRAN if detailed radiosonde data are known.

An alternative (cf. Hook 1992) is to adjust the inputs to

LOWTRAN in an interactive fashion and predict the

temperatures for targets with known spectral

emissivities. When the predicted temperatures are the

same in all bands, the atmosphere is assumed to be

correct.

Digital Imaging and Remote Sensing Laboratory

Thermal Infrared Spectral Analysis (cont’d)Thermal Infrared Spectral Analysis (cont’d)

This method has been successfully applied to the

thermal infrared multispectral scanner (TIMS). A six-

band line scanner developed and operated by JPL.

The relative emissivities can be estimated by first

solving for T in one channel where emissivity is

assumed constant over the image (i.e. in continuum)

and then solving for the emissivity in the other

channels.

Digital Imaging and Remote Sensing Laboratory

Thermal Infrared Spectral Analysis Thermal Infrared Spectral Analysis (cont’d)(cont’d)

A second technique that is useful when spectral

data over a wide range are available is to fit the

surface-leaving radiance to a Planck curve and

select the lowest temperature that doesn’t force an

emissivity greater than 1.

Digital Imaging and Remote Sensing Laboratory

Thermal Infrared Spectral Analysis Thermal Infrared Spectral Analysis (cont’d)(cont’d)

Experimental results: Figure 5.16 shows a comparison of six

point emissivity spectra from TIMS compared to laboratory

spectra (not clear which technique is used).

Digital Imaging and Remote Sensing Laboratory

Thermal Infrared Spectral Analysis (cont’d)Thermal Infrared Spectral Analysis (cont’d)

Fig. 5.16 (a) Spectra from TIMS data, Mauna Loa basalts.

(b) Spectra from laboratory, Mauna Loa basalts.