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