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Introduction to Imaging Spectroscopy
part 1
Remote Sensing (GRS-20306)
Outline
Part 1
Definition
History
Why spectroscopy works!
Measurement methods
● Non-imaging
● Imaging
Applications
Part 2
Analytical Methods
● SAM
● SUM
Exercise
● Cuprite
Definition
Spectroscopy is everywhere
● Exobiology: in search for extraterrestrial life
● Designing eye-friendly filters for new generation Xenon
discharge lamp based headlights
● Rare earth elements doped Euro bills to prevent falsification
● Weaving of silver strings into carpets to increase total reflectivity in office space (to save illumination power)
● Unravel the composition of planets, moons, asteroids, and comets (as done on Mars, Mercury, Jupiter, Moon, Virtanen, etc.)
● Interaction measurement of polymeric surfaces with the environment
● Ballistic analysis in forensic medicine
Definition
Spectroscopy is the study of light as a function of wavelength that has been emitted, reflected or scattered from a solid, liquid, or gas.
● The quantity measured is usually reflectance (expressed in %)
Spectroradiometry is the technology for measuring the power of optical radiations in narrow, contiguous wavelength intervals
● The quantities measured are usually spectral radiance
Definition
In literature, the terms imaging spectroscopy, imaging spectrometry, hyperspectral (e.g., Lillesand,
Kiefer, Chipman), superspectral, and ultraspectral imaging are often used interchangeably. Even though semantic differences might exist, a common definition is:
● Imaging spectrometry is the simultaneous acquisition of
spatially co-registered images, in many, spectrally
contiguous bands, measured in calibrated radiance units,
from a remotely operated platform.
● Imaging spectroscopy is the simultaneous acquisition of
spatially co-registered images, in many, spectrally
contiguous bands, measured as reflectance, from a
remotely operated platform. Schaepman, M.E., Green, R.O., Ungar, S., Boardman, J., Plaza, A.J., Gao, B.-C., Ustin, S., Miller, J., Jacquemoud, S., Ben-Dor, E., Clark, R., Davis, C., Dozier, J., Goodenough, D., Roberts, D., & Goetz, A.F.H. (2006 (accepted)) The Future of Imaging Spectroscopy – Prospective Technologies and Applications. In IGARSS, pp. 5. IEEE, Denver (USA).
Imaging Spectroscopy:
The Data Cube Principle
Definition
Applying this definition results in quantitative and qualitative
characterization of both the surface and the atmosphere, using
geometrically coherent spectral measurements.
This result can then be used for the
● unambiguous direct and indirect identification of surface materials,
water properties, and atmospheric trace gases,
● the measurement of their relative concentrations,
● subsequently the assignment of the proportional contribution of
mixed pixel signals (e.g., spectral un-mixing),
● the derivation of their spatial distribution (e.g., mapping), and
● finally their evolution over time (multi-temporal analysis).
Definition
Spectroradiometric measurements are one of the least reliable
of all physical measurements.
Henry Kostkowski, Reliable Spectroradiometry, 1997
Three major reasons for large errors in spectroradiometry are:
● The measurement is a multidimensional problem,
● The instability of measuring instruments and the standards
used to calibrate these instruments are frequently 1% or more
during the complete measurement process, and
● The principles and techniques used for eliminating (or
reducing) measurement errors due to this multidimensionality
or instability have not been widely disseminated.
Definition
Optical
System
Background
Transmissions-
medium Photons contributing
to the total signal
Object
esrsr
sr
sr
sr
ta
at
sr
i0 Exitance
in Irradiance
a Absorbed radiance
sr Scattered/reflected radiance
t Transmitted radiance
e Emitted radiance
sr
sr
e
t
t
sr
t
t
i0i0
i0
i1
i1
i2
i2
i2
i2
i2
i2
i3
i3
t
t
a
srsr
sr
e
a
sr
sr
a
sr
sr
asr
sr sr
sr sr
a
a
srsr
sr
a
t
sra
t
sr
sr
sr
sra
sr
sr
sr
a
e
e
e
e
e
sr
sr
sr
sr
a
Source
Contributing sources to a spectroradiometric measurement
History of Spectroscopy
Source: Newton, I.: Opticks: or, a Treatise of the Reflexions, Refractions, Inflexions, and Colours of Light, Book I, Plate IV, Part I, Fig. 18, Sam Smith and Benj. Walford, St. Paul’s Church-yard, 1704 – Burndy Library
Spectral dispersion
Continuous spectrum, interrupted by dark lines
Explanation of Fraunhofer lines
Absorption in gas
Composition of astronomical objects
First imaging spectrometer in space
For complete overview: Schaepman, M.E., 2007. Spectrodirectional remote sensing: From pixels to processes. JAG 9 (2): 204
History of imaging spectroscopy
(1960s) (1970s)
(1980s)
(1990s) (2000s)
(2010s)
Why Spectroscopy Works!
Path from the sun to the sensor
E0 Latm
Edif
Egnd
du
Lg,adj
Lgnd
Lg,dir
Ls
Why Spectroscopy Works!
The influence of the major absorption bands of atmospheric water vapour, carbondioxide and ozone on spectral signatures of vegetation, measured with the AVIRIS sensor; Flevoland test site, July 5th 1991.
wavelength (µm)
1.9 0.4 0.7 1.0 1.3 1.6
20
15
10
5
0
radiance (mW/cm2/µm/sr)
H2O
H2O
H2O
H2O H2O
H2O
O2; H2O
CO2
CO2
CO2
potatoes maize absorption features
Absorption features in spectra
Electronic transitions
● Isolated atoms and ions have discrete energy states. Absorption of photons of a specific wavelength causes a change from one energy state to a higher one.
Vibration processes
● The bonds in a molecule or crystal lattice are like springs with attached weights: the whole system can vibrate.
Electronic transitions
High energy - low wavelength [ Q = h = hc/ ]
Broad features
Between 0.2 - 1.1 microns
Vibration processes
The frequency of vibration depends on the strength of each spring (the bond in a molecule) and their masses (the mass of each element in a molecule)
Vibration processes
Low energy - high wavelength
Narrow features (10-20 nm)
Stretching of molecular bonds
● Water 1.4 +1.9
m
● AlOH 2200 nm
● MgOH, 2300 nm
● CaCO3, 2320-
2350 nm
Energy levels
2
1
0 0
1
24
3
2
1
0
00
HH
H H
Spectrum
2.74
2.66
6.47
Wavelength ( m)
H0
H H
En
erg
y
Normal modes
0.8
0.6
0.4
0.2
0.0
Re
fle
cta
nce
[sca
led
fro
m 0
-1]
24002200200018001600140012001000800600400
Wavelength [nm]
0.8
0.6
0.4
0.2
0.0
Kaolinite Dolomite Hematite
Kaolinite Absorption Feature
Dolomite Absorption Feature
Hematite Absorption Feature
Kaolinite Absorption Feature
Unambiguous Identification of Spectral Diversity
Spectral Data Richness I
0.15
0.10
0.05
0.00
Ls [
W/(
m2 s
r n
m)]
24002200200018001600140012001000800600400
Wavelength [nm]
0.15
0.10
0.05
0.000.15
0.10
0.05
0.000.15
0.10
0.05
0.00
Total Radiance at Sensor (MODTRAN 4)
Imaging Spectrometer (10 nm FWHM)
Landsat 7
SPOT 4
Spectral Data Richness II
Example of vegetation stress
Each time step is 10 mins., total duration 8 hrs
Measurement is reflectance plus reflected transmittance
Undisturbed leaf
Wageningen UR 2003
Laboratory spectrometer
Measures the composition of gases,
liquids or solids (PerkinElmer Lambda
900 (275-3300 nm))
Plant Facility WUR (CGI&PRI): goniometric set-up for spectral, thermal and fluorescence
Field spectroradiometers
Field measurements (MERIS Calibration)
MERIS Cal/Val (June 2002)
● Goniometric Measurements
● Direct solar irradiance
● Total and diffuse solar irradiance
Observations by Data Acquisition Systems
Four categories of sensors
● Exploratory missions
● ESA: SPECTRA (1) and APEX (1/2); NASA: ESSP and AVIRIS
● Technology demonstrators / operational precursor missions
● ESA: CHRIS/PROBA (2) and APEX (1/2); NASA: Hyperion/EO-1
● Systematic measurement missions
● ESA: MERIS/ENVISAT (3); NASA: MODIS/TERRA and on AQUA, GER: ENMAP (2012), IT: PRISMA (2012), NASA: HYSPIRI (2013)
● Operational missions
● ESA: MSG-1 (4); NASA: NOAA AVHRR
Source: http://www.esa.int http://www.apex-esa.org
1 1/2 2 3 4
Water quality of Lake Garda using Hyperion
22nd July 2003
Chl-A: chlorophyll A
TR: tripton
RT-model
Giardino et al., 2007
Mapping invasive species
Underwood, E., Ustin, S., and DiPietro, D. (2003). Mapping nonnative plants using hyperspectral imagery, Remote Sensing of Environment, Vol. 86(2), p. 150-161.
Summary
IS evolved over last 35 years from experimental technique to systematic measurement mission
Technology development essential to safeguard high quality measurements
Shift from qualitative to quantitative products -> development of physicallly based RT models
End Part I