digital imaging and remote sensing laboratory spectral signatures
TRANSCRIPT
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
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Spectroscopy of MineralsSpectroscopy of Minerals
Figure 4a.
WavelengthWavelength
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Spectroscopy of Minerals (cont’d)Spectroscopy of Minerals (cont’d)
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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).