digital imaging and remote sensing laboratory spectral signatures

Digital Imaging and Remote Sensing Laboratory

Spectral SignaturesSpectral Signatures

Spectral Sources 2Digital Imaging and Remote Sensing Laboratory

Hyperspectral Imagery: MISIHyperspectral Imagery: MISI

Spectral Sources 3Digital Imaging and Remote Sensing Laboratory

Radiation PropagationRadiation Propagation

Energy Paths

Spectral Sources 4Digital Imaging and Remote Sensing Laboratory

Radiation PropagationRadiation Propagation

The spectral radiance reaching an aerial or satellite

sensor in the UV through LWIR region can be

expressed in simplified form as:

uedeTusdsS

FEDCBA

LrLLLrLrE

L

LLLLLLL

22221 cos

Spectral Sources 5Digital Imaging and Remote Sensing Laboratory

In the reflective region (0.4-3 m) this can be

approximated as:

and in the LWIR and the MWIR at night an

approximate expression is:

CBA LLLL

fED LLLL

Radiation Propagation

Spectral Sources 6Digital Imaging and Remote Sensing Laboratory

The effective radiance (L) reaching a sensor for a

given channel can be expressed as:

where: () is the peak normalized spectral response

of the sensor i.e.

R̂

dRLL )(ˆ0

max

)(ˆR

RR

Radiation Propagation

Spectral Sources 7Digital Imaging and Remote Sensing Laboratory

The effective in band radiance is more commonly used

in imaging spectroscopy and is expressed as:

dRdRLLL eff )(/)(

Radiation Propagation

Spectral Sources 8Digital Imaging and Remote Sensing Laboratory

Radiation PropagationRadiation Propagation

L

)(R

Spectral Sources 9Digital Imaging and Remote Sensing Laboratory

Characteristics of Spectral Data

Spectral Sources 10Digital Imaging and Remote Sensing Laboratory

Characteristics of Spectral dataCharacteristics of Spectral data

•solids

•liquids

•gasses

Grass

asphalt roofing

Brick

1.0

Spectral Sources 11Digital Imaging and Remote Sensing Laboratory

Characteristics of Spectral dataCharacteristics of Spectral data

solidsliquidsgasses

Irondequoit Bay

Lake Ontario

Genesee River

0.50

Spectral Sources 12Digital Imaging and Remote Sensing Laboratory

Characteristics of Characteristics of Spectral dataSpectral data

gasses

WAVENUMBER [cm-1] WAVENUMBER [cm-1]

WAVENUMBER [cm-1]

Spectral SourcesDigital Imaging and Remote Sensing Laboratory

Often in MWIR and LWIR but particularly when

studying gases we use wave numbers as a means

of expressing spectral values.

The wave number is expressed as:

i.e. how many wavelengths fit in 1 cm

wave numberwave number

[cm] 1

v

Spectral Sources 14Digital Imaging and Remote Sensing Laboratory

wave numberwave number

So 3 m is

for 10 m

16 3333

10010

1

cm

mcm

mm

m3

v

14 1000

10

1

cm

mcm

m10

v

Spectral Sources 15Digital Imaging and Remote Sensing Laboratory

Absorption spectra of various Absorption spectra of various atmospheric constituentsatmospheric constituents

H2O

O3

CO

Spectral Sources 16Digital Imaging and Remote Sensing Laboratory

Absorption spectra of various Absorption spectra of various atmospheric constituentsatmospheric constituents

CO2

CH4

N2O

Spectral Sources 17Digital Imaging and Remote Sensing Laboratory

Absorption spectra of various Absorption spectra of various atmospheric constituentsatmospheric constituents

OverallAtmospheric Transmission

O2

Spectral Sources 18Digital Imaging and Remote Sensing Laboratory

Characteristics of Spectral data:Characteristics of Spectral data:Sources of Absorption SpectraSources of Absorption Spectra

• electron transition

• rotation and vibration

• harmonics

Spectral Sources 19Digital Imaging and Remote Sensing Laboratory

SignaturesSignatures

Below 1 m

In minerals, the absorption features are largely influenced by transition metals, particularly iron which is very common. Charge transfer bands that result from electron exchange between neighboring metal ions create strong absorption features in the UV. The wings of these bands account for the general increase in reflectance with wavelength in the visible for most minerals

Spectral Sources 20Digital Imaging and Remote Sensing Laboratory

Signatures (cont’d)Signatures (cont’d)

(from Pieters & Englert,1993)

Spectral Sources 21Digital Imaging and Remote Sensing Laboratory

Signatures (cont’d)Signatures (cont’d)

• Combination bending and stretching overtones of the fundamental OH vibration at 2.74 m cause features

between 2.1 and 2.4 m. Overtones for H2O and CO3 also

occur in this region.

• As we move through SWIR and into the MWIR, the spectra are rich with overtones and fundamentals of vibrational and rotational transitions. However, the thermal signature begins to mask absorption features and must be dealt with before emissive/absorptive spectra can be clearly observed.

Spectral Sources 22Digital Imaging and Remote Sensing Laboratory

Spectroscopy of MaterialsSpectroscopy of Materials

• Observable spectra in the VIS-SWIR may be due to:

– electron transitions in molecules and crystals

– vibration transitions in molecules and crystals

– electronic transition between atoms

• Electronic transitions are generally in the VIS-NIR

• Vibrational transitions are usually further into the IR with overtones and combinations in the NIR and SWIR.

Spectral Sources 23Digital Imaging and Remote Sensing Laboratory

Spectroscopy of Materials (cont’d)Spectroscopy of Materials (cont’d)

Fundamental vibrational modes of simplemolecules and molecular ions.

(from Pieters & Englert,1993)

Spectral Sources 24Digital Imaging and Remote Sensing Laboratory

Spectroscopy of Materials (cont’d)Spectroscopy of Materials (cont’d)

Overtones occur at approximately linear

combinations of the fundamental frequencies, e.g.,

1 + 1, or 1 + 2

Since these are not perfectly free harmonic

oscillations the overtones are usually shifted to

slightly longer wavelengths than simple addition

would predict.

Spectral Sources 25Digital Imaging and Remote Sensing Laboratory

Concentration of material tends to be proportional to absorption but confusion factors can arise caused by, for example, stronger returns from fine particulates dispersed over the matrix. Particularly when the materials are optically interacting any spectral combination may be highly non-linear (e.g., an intimate mixture).

Spectroscopy of Materials (cont’d)Spectroscopy of Materials (cont’d)

Spectral Sources 26Digital Imaging and Remote Sensing Laboratory

Characteristics of Spectral data:Characteristics of Spectral data: Changes in absorption features with Changes in absorption features with

state changesstate changes

Vegetation &Snow Spectra

Examples of a calculated water vapor transmittance spectrum and

measured reflectance spectra of vegetation and snow.

Spectral Sources 27Digital Imaging and Remote Sensing Laboratory

Example reflection spectraExample reflection spectra32% reflector through different atmospheres

DC DC

refl

ecta

nce

refl

ecta

nce

ob

serv

edra

dia

nce

ob

serv

edra

dia

nce

Spectral Sources 28Digital Imaging and Remote Sensing Laboratory

Example reflection spectraExample reflection spectra32% reflectance through different atmospheres

refl

ecta

nce

refl

ecta

nce

ob

serv

edra

dia

nce

ob

serv

edra

dia

nce

DC DC

Spectral Sources 29Digital Imaging and Remote Sensing Laboratory

Scattering TheoryScattering Theory

The shape of the absorption feature when expressed as reflectance vs. energy or apparent absorption is approximately Gaussian.

The continuum must be removed by dividing the reflectance spectrum by an estimate of the continuum or subtracting an estimate of the log of the continuum from the log (lnr) of the reflectance spectrum.

Spectral Sources 30Digital Imaging and Remote Sensing Laboratory

Scattering Theory Scattering Theory (cont’d)(cont’d)

The spectra of pure montmorillonite (top) and mixtures of

montmorillonite plus carbon black (0.5 wt % carbon black,

middle; 2.0 wt % carbon black, bottom)

(from Clark & Rousch1984)

Spectral Sources 31Digital Imaging and Remote Sensing Laboratory

Scattering Theory Scattering Theory (cont’d)(cont’d)

The absorption spectra can then be characterized

by fitting a Gaussian to the specific absorption

feature.

Can estimate source of other absorption features,

curve fit and divide them out or simply curve fit

(often straight line) locally and divide to estimate

the absorption feature.

Spectral Sources 32Digital Imaging and Remote Sensing Laboratory

Band depth defined as:

D = __________

where is reflectance of continuum at band center

and is reflectance at band center

Scattering Theory Scattering Theory (cont’d)(cont’d)

D - DC B

D C

D C

D B

Spectral Sources 33Digital Imaging and Remote Sensing Laboratory

Scattering Scattering TheoryTheory

Spectral Sources 34Digital Imaging and Remote Sensing Laboratory

Characteristics of Spectral data:Sample Spectra

Spectral Sources 35Digital Imaging and Remote Sensing Laboratory

Spectroscopy of MineralsSpectroscopy of Minerals

Figure 4a.

WavelengthWavelength

Spectral Sources 36Digital Imaging and Remote Sensing Laboratory

Spectroscopy of Minerals (cont’d)Spectroscopy of Minerals (cont’d)

Spectral Sources 37Digital Imaging and Remote Sensing Laboratory

Spectroscopy of Minerals (cont’d)Spectroscopy of Minerals (cont’d)

Spectral Sources 38Digital Imaging and Remote Sensing Laboratory

Spectroscopy of Minerals (cont’d)Spectroscopy of Minerals (cont’d)

Spectral Sources 39Digital Imaging and Remote Sensing Laboratory

Spectroscopy of Minerals (cont’d)Spectroscopy of Minerals (cont’d)

Spectral Sources 40Digital Imaging and Remote Sensing Laboratory

Spectroscopy of Minerals (cont’d)Spectroscopy of Minerals (cont’d)

Spectral Sources 41Digital Imaging and Remote Sensing Laboratory

Spectroscopy of Minerals (cont’d)Spectroscopy of Minerals (cont’d)

Spectral Sources 42Digital Imaging and Remote Sensing Laboratory

Spectroscopy of Minerals (cont’d)Spectroscopy of Minerals (cont’d)

Figure 5a. The reflectance spectra of talc as a function of spectral resolution in 1.4 micro-meter region.

Spectral Sources 43Digital Imaging and Remote Sensing Laboratory

Hyperspectral NotesHyperspectral Notes

Some sample spectra of organic compounds are

shown in Figure 3.17 and absorption lines

associated with transitions listed in Table 3.2.

Spectral Sources 44Digital Imaging and Remote Sensing Laboratory

HyperspectralHyperspectral

Fig 3.17. Spectra of organic compounds

sample spectra of organic compounds and absorption lines associated with transitions listed in next table.

Spectral Sources 45Digital Imaging and Remote Sensing Laboratory

Hyperspectral Notes (cont’d)Hyperspectral Notes (cont’d)

Table 3.2. NIR absorptions due to vibrational transitions of organic molecules

Spectral Sources 46Digital Imaging and Remote Sensing Laboratory

Hyperspectral Notes (cont’d)Hyperspectral Notes (cont’d)

Effect of particle size:

Reflection can be thought of as a combination of surface (specular) reflection and volume (diffuse or scattered) reflection.

In the diffuse case, some of the flux penetrates medium and is partially absorbed before being scattered back to the surface.

Spectral Sources 47Digital Imaging and Remote Sensing Laboratory

Hyperspectral Notes (cont’d)Hyperspectral Notes (cont’d)

In general, for a highly reflecting (weakly

absorbing) material: increasing grain size will

decrease the reflectance (increase transmissive

interactions and absorption line strength).

Spectral Sources 48Digital Imaging and Remote Sensing Laboratory

Hyperspectral Notes (cont’d)Hyperspectral Notes (cont’d)

Fig. 3.18 Variation in reflectance and absorption

band depth with variations in particle size of

clacite (Iceland spar), a high albedo mineral.

Spectral Sources 49Digital Imaging and Remote Sensing Laboratory

Hyperspectral Notes (cont’d)Hyperspectral Notes (cont’d)

In strongly absorbing materials, surface reflection may dominate. Depth of penetration is very shallow (little diffuse reflection) reflectivity decreases with decreasing particle (more absorbing centers available).

Absorption band strength is deepest when particle size is approximately equal to the optical depth (which is, of course, wavelength dependent).

Spectral Sources 50Digital Imaging and Remote Sensing Laboratory

Hyperspectral Notes (cont’d)Hyperspectral Notes (cont’d)

Fig. 3.19. Variation in reflection properties with particle size for a strongly absorbing material

(pyrite, FeS2).

Spectral Sources 51Digital Imaging and Remote Sensing Laboratory

• gaseous absorption and emission spectra

• sample spectra

• ASTER Spectral Library http://speclib.jpl.nasa.gov

Example Emission Spectra

0

0.05

0.1

0.15

0.2

3.15 3.2 3.25 3.3 3.35 3.4 3.45

Wavelength (m)

Ab

sorb

ance

Methyl Chloride Absorbance Curve

Spectral Sources 52Digital Imaging and Remote Sensing Laboratory

Hyperspectral Notes (cont’d)Hyperspectral Notes (cont’d)

In mixtures, small, highly absorbing particles may

disproportionately dominate composite spectra

(intimate non linear mixing occurs).