1

23/07/2013 1

Optical/Thermal:Principles & Applications

Jose F. Moreno

University of Valencia, Spain

Jose.Moreno@uv.es

Lecture D1T2 – 1 July 2013

OPTICAL PRINCIPLES AND APPLICATIONS

Information content of optical data: retrievable information

Forward modelling of surface reflectance: soil, leaf and canopy models

Pre-processing aspects

Information retrieval techniques, validation and scaling issues

Data usage as inputs to models and applications

Perspectives

2

Understanding theactual information content

of the optical data:forward modelling

of the signal

1

3

OPTICAL SYSTEMS:- Panchromatic:

- very high spatial resolution (broadband)

- Multispectral: - “colour” imaging

- Hyperspectral:- chemical composition

- Multi-angular:- structure

Many systems availableas a function of spatial, temporal and spectral resolutions

4

0

10

20

30

40

50

60

70

0 2000 4000 6000 8000 10000 12000 14000

Wavelength (nm)

Sola

r R

adia

nce

(µW

/cm

2 /nm

/sr)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Atmosphere

Solar 1.0 Reflectance

Earth 300 K, 1.0 Emisivity

Earth R

adiance (µW

/cm2/nm

/sr)Available Signal

Signatures of natural targets:

- Spectral signatures

- Angular signatures

- Spatial signatures

- Temporal signatures

- Other signatures (i.e., fluorescence, polarization, etc.)

What we measure is always radiance, either reflected and / or emitted by the land surface, which variations depend on the optical properties of land targets(and illumination conditions)

5

1 0-2

1 0-1

1 00

1 01

1 02

1 03

1 04

1 05

1 07

1 06

(m)

1 00

1 01

1 0- 1

Re

al (

n)

75342

1 TM 6TM

PLC

WATER SCATTERING

1 0-2

1 0-1

1 00

1 01

1 02

1 03

1 04

1 05

1 07

1 06

1 01

1 00

1 0-1

1 0-2

1 0- 3

1 0-4

1 0-5

1 0-6

1 0-7

1 0-8

Ima

g (

n)

(m)

TM 6

75

1

WATER ABSORPTION

PL

C

2

3

4

TM

Optical properties of elementary constituents determinethe spectral reflectance of land elements

6

SCATTERING BY VEGETATION MATERIAL

High modelling variability !

Are all pigmentsseparable in the signal ?

Key issues:- existing model parameterisations do not account for the observed variability

- high variability set limits to the possible decomposition of effects due to different pigments

7

Senescent / dryleaves

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600

spec

ific

ab

sorp

tio

n c

oef

fici

ent

(cm

g

)

2-1

wavelength (nm)

CELLULOSE+LIGNIN ABSORPTION

PROTEIN ABSORPTION

8

Non photosyntheticelements:spectral variability

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.60

5

10

15

20

25

30

35

40

45

50

55

60

65

wavelength ( m)

refl

ec

tan

ce

green vegetation (alfalfa)

senescent vegetation (barley)

9

-60

-40

-20

0

20

40

60

80

100

120

140

400 450 500 550 600 650

zeaxanthin-violaxanthin

abso

rpti

on

dif

fere

nce

wavelength (nm)

0

20

40

60

80

100

120

140

160

180

200

220

240

260

280

400 450 500 550 600 650

violaxanthinzeaxanthin

wavelength (nm)

abso

rpti

on

co

effi

cien

t

10

cano

py r

efle

ctan

ce

wavelength (nm)

0.00

0.01

0.02

0.03

0.04

0.05

0.06

600 650 700 750 800

wavelength (nm)

ab

sorp

tion

(a

.u.) light

absorption

abso

rpti

on

(a.

u.)

S2

S1

S0

internal energy conversions

ener

gy le

vels

Chlorophyll fluorescence as a new tool for remote sensing of vegetation activity.

abso

rpti

on flu

orescen

ce

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

fluo

resce

nce

(a.u

.)

fluorescence emission

lower energy

fluo

rescence (a.u

.)

11

Energy budget at the leaf level

Chlorophyll fluorescence as an indicator of usage of the absorbed lightby vegetation

De-excitation path ways

forward

backward

Degree of polarization:POLARIZATION OF TOA RADIANCES- Mostly due to atmosphere- Surface also introduces polarization effects

12

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.2

0 .4

0 .6

0 .8

1 .0

1 .2

1 .4

1 .6

2 .0

1 .8

Sun = 675 nm

CHANGES IN CANOPY HEIGHT canopy height values

(m)

view angle

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

refl

ec

tan

ce

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Sun

CHANGES IN LEAF SIZE

= 675 nm

refl

ec

tan

ce

view angle

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

0.02

0 .06

0 .04

0 .08

0 .10

0 .12

0 .14

0 .16

0 .18

0 .20

leaf size values

(m)

POLDERnoon flight

POLDERmorning flight

13

specular

POLDER data

Irrigated alfalfa field

Channel 3 (550 nm)

Rare events sometimesobserved in real data

Not always predictedby the models!

14

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600

0

5

10

15

20

25

30

35

40

45

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600

SUGAR BEET

wavelength (nm)

wavelength (nm)

refle

cta

nce

refle

ctan

ce

ALFALFA

Line 1 , noon

Line 2 , noonLine 1 , morning

Line 2 , morning

Line 1 , af t ernoonLine 2 , af te rnoon

SV6 - C03 UTM-X: 577783 UTM-Y: 4324794 LAI = 1.71 fCover = 0.61

Line 1 , noon

Line 2 , noon

Line 1 , morning

Line 2 , morning

Line 1 , af t ernoon

Line 2 , af te rnoon

V16 - C01 UTM-X: 577550 UTM-Y: 4324069 LAI = 1.84 fCover = 0.95 Spectral versus

angular information

A rather Lambertian surface

A highly anysotropic surface

Spatialinformationin theimages

- Textures

- Higher orderstatistics

15

TEMPORAL SIGNATURESTime series and data assimilation

will be common strategies with future GMES Sentinel data

How well the spectral reflectance signal is understood?

16

MERIS Landsat TM

HyMapCHRIS

Planck’s Law

17

THERMAL INFRARED

18

Kirchoff’s law

energy balance

~ 0

THERMAL INFRARED

THERMAL INFRARED:

Temperature versusemissivity effects

19

Data processing methodsfor optical remote sensing data

2

Pre-processing steps:

- Radiometric calibration

- Noise removal

- Cloud screening

- Geometric correction

- Atmospheric correction

- Data integration

20

CLOUD SCREENING

- Very dependent on the available spectral information

- Many different algorithms (from simplethresholds up to sophisticate techniques)

Atmospheric effects

21

TOA

BOA

SATELLITE SIGNAL MODELLING

22

Measured Top-Of-Atmosphere signalS

entin

el-2

SE

NT

INE

L-2

23

OUTPUT OF THEATMOSPHERIC/TOPOGRAPHICCORRECTION FOR QUANTITATIVECOMPARISONS IN MULTITEMPORALSTUDIES

62 spectral bands34 m resolution5 view angles

CHRIS/PROBA DATA

24

A - Vertical geometric distorsion (horizontal displacement due to relief)

B - Variation of atmospheric (optical) properties with height

C - Relative changes in slope and orientation of surface introduce variations in illumination conditions:

Direct irradiance:- illuminated areas- self-shadowed areas- cast-shadowed areas

Diffuse irradiance:- directional distribution- modeling of sky view factors

Surface reflectance model:- non-Lambertian effects- modeling of direct/diffuse components

D - Adjacency effects (additional contributions)

E - Additional multiple reflections

Effects introduced by topography:

simulation & inversionsoftware tools

25

Sentinel‐2

Sentinel‐3

1nm

atmosphere

surface

Atmospheric effects in the thermal domain

26

- Monochanel equation

- “Split-window” equation

- Dual-ange equation

27

Exploitation of optical remote sensing data for

land science and applications

3

SPECTRALINDICESAS PROXIES

EMPIRICAL APPROACHES

28

Visible Atmospherically Resistant Index

29

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

400 700 1000 1300 1600 1900 2200 2500

soil directcanopy directmult iple scat teringtotal ref lectance

wavelength (nm)

refl

ec

tan

ce

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

400 700 1000 1300 1600 1900 2200 2500

lea f r e f le ct a nce

le af t r ansmi t t a nce

lea f absor pt a nce

so il r ef le ct a nce

wavelength (nm)

Signal composed by multiple contributions(soil+vegetation)

Multiple scattering effectsplay a major role

SURFACE MODEL PARAMETERISATION:(a) Leaf inputs:

- Leaf effective thickness - Leaf water content- Total leaf chlorophyll (a+b) - Specific leaf weight- Ratio Ca/Cb - Leaf cellulose content- Fraction of Ca in LHCP - Leaf lignin content- Leaf carotenes content

(b) Canopy inputs:- LAI- fCover- Clumping parameter (H/D)

(c) Soil inputs:- Soil wetness parameter

30

MODEL INVERSION STRATEGIESThe problem of model inversion can be considered from different perspectives:

(a) Root finding of a given function

(b) Solving non-linear set of equations

(c) Function minimisation

(d) Non-linear least-squares modeling of data

Root finding and solving non-linear set of equations would require that the function is “exact”, and for this reason function minimisation is normally preferred.

p

t

pmodmes

t

modmes VVCVVVRRWVRR 112 )()(

a priori Covariance Matrix

Residuals

a priori Covariance Matrix

Observations Model variables

Merit function:Incorporation of the uncertainties in the inversion process

Residuals ResidualsResiduals

Use of constrained minimization procedures that guarantee the minimal variation of model variables to produce the same output, and a robust initialization procedure of such variables (consistency even if model has global bias).

31

THE SHAPE OF THE MERIT FUNCTION

Absolute minimum at first guess (most probable value)

(location of minimum is variable)

X0

Xmin X max

maximum range of possible values

Ymin

Yref

Ymax

maximum range of probable values

Xminref Xmax

ref

f (X)

non valid solutions

valid solution

absolute minimum

relative minimum

Neural networkmethods

Training becomes the critical issue

32

Spectral fitting methods are especially useful because we can use the well-known shape of spectral features.

Requires rather high spectral resolution.

0

0 .1

0 .2

0 .3

0 .4

0 .5

0 .6

3 0 0 5 0 0 7 0 0 9 0 0 1 1 0 0 1 3 0 0 1 5 0 0 1 7 0 0 1 9 0 0 2 1 0 0 2 3 0 0 2 5 0 0

L A I= 4

(n m)

0 .0 0 1

0 .0 0 2

0 .0 0 4

0 .0 0 6

0 .0 1 0

0 .0 1 5

0 .0 2 0

0 .0 4 0

0 .0 6 0

0 .0 8 0

0 .1 0 0

Leaf Wat er

Co nt ent (cm )

33

700 900 1100

(nm)700 900 1100

(nm)

LAI = 1 LAI = 40.6

0.5

0.4

0.3

0.2

0.1

0.0

0.6

0.5

0.4

0.3

0.2

0.1

0.0

1 .1 01 .0 51 .0 00 .9 50 .9 00 .8 5

atmospheric water vapour

surface liquid water

tran

smit

tan

ce

& r

efle

cta

nce

(r

elat

ive

un

its

)

wavelength ( m)

Decoupling of atmospheric effectswhen retrieving leaf / canopyinformation

34

spectral channels

multi-resolution spatial classification

homogeneity test

convergence test

N N+1

heterogeneous pixels:

mixture model inversion

homogeneous pixels:

reflectance model inversion

Nth iteration parameters

a i

N{ }i = 1 , ... , P

FIR

ST

ST

EP

unmixing procedure

end-members parametric

characterisation

end-members definition (soil, vegetation, shade...)

preliminary values from empirical relationships

N=0

heterogeneous pixel masking

SE

CO

ND

ST

EP

TH

IRD

ST

EP

additional outputs

generation of a complete output map for each variable

(merge homogeneous + heterogeneous pixel outputs)

A general multi-step procedure

- Explicit separation of almostpure pixels from spectral mixtures

- Use of several retrieval techniquesfor each step

- Produce different adequate outputsfor each retrieval procedure

Such methods are used in practice for

real images

MULTI-STEP PROCEDURES

MERIS FR

CHRIS/PROBA

SCALINGASPECTS

Comparison among different sensor products

35

Intensive field campaigns

Ground validation data:Mean values

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

V2 9 V20 V14B V1

Individu al samples

0

0 ,5

1

1 ,5

2

2 ,5

3

3 ,5

4

4 ,5

5

- vegetation properties- soil properties- solar radiation- atmospheric status- surface fluxes

spatial variability

36

Usage of the derived information:- Tendency: from proxies to quantitative information- Multi-resolution spatial inputs and time series

- First approach: Land cover mapping, classification and tables of biophysical variables assigned to each class

- Second approach: Retrievals of biophysical variables as direct inputs to models

- Third approach: direct assimilation ofradiances/reflectances into models

- Mapping Applications - cartography - thematic mapping - Monitoring Applications - ecosystems dynamics - natural hazards (fires, floods, desertification) - Research about Land Surface Processes - heat and mass exchange at Land/Atmosphere interface - photosynthesis and net primary production - hydrologic processes - Land/Atmosphere exchange of biochemicals

REMOTE SENSING OF LAND SURFACE PROCESSES

}

37

SPOT-5 / 2005

MOSAICS

38

INTEGRATION IN A GIS ENVIRONMENT

39

2003 growingSeasonBarrax

Validation 15/07/2003 LANDSAT LAI

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0

LANDSAT retrieved

Gro

un

d m

eas

ure

men

ts

ALFALFA

CORN

PAPAVER

POTATO

SUGARBEET

GARLIC

ONION

CORNALFALFA

MULTITEMPORAL SERIES

0

0.001

0.002

0.003

0.004

0.005

400 500 600 700 800 900

Longitud de onda (nm)

Ref

lect

anci

a (R

rs, s

r-1)

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

0

0.001

0.002

0.003

0.004

400 450 500 550 600 650 700 750 800 850 900

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

CASI-2

MERIS

TM

0

0.001

0.002

0.003

0.004

0.005

400 500 600 700 800 900

Longitud de onda (nm)

Ref

lect

anci

a (R

rs, s

r-1)

0

0.001

0.002

0.003

0.004

0.005

400 500 600 700 800 900

Longitud de onda (nm)

Ref

lect

anci

a (R

rs, s

r-1)

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

0

0.001

0.002

0.003

0.004

400 450 500 550 600 650 700 750 800 850 900

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

CASI-2

MERIS

TM

9

6

3

Re

fle

cta

nc e

(%

)

Wavelength (nm)

12

0

0.001

0.002

0.003

0.004

0.005

400 500 600 700 800 900

Longitud de onda (nm)

Ref

lect

anci

a (R

rs, s

r-1)

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

0

0.001

0.002

0.003

0.004

400 450 500 550 600 650 700 750 800 850 900

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

CASI-2

MERIS

TM

0

0.001

0.002

0.003

0.004

0.005

400 500 600 700 800 900

Longitud de onda (nm)

Ref

lect

anci

a (R

rs, s

r-1)

0

0.001

0.002

0.003

0.004

0.005

400 500 600 700 800 900

Longitud de onda (nm)

Ref

lect

anci

a (R

rs, s

r-1)

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

0

0.001

0.002

0.003

0.004

400 450 500 550 600 650 700 750 800 850 900

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

CASI-2

MERIS

TM

0

0.001

0.002

0.003

0.004

0.005

400 500 600 700 800 900

Longitud de onda (nm)

Ref

lect

anci

a (R

rs, s

r-1)

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

0

0.001

0.002

0.003

0.004

400 450 500 550 600 650 700 750 800 850 900

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

CASI-2

MERIS

TM

0

0.001

0.002

0.003

0.004

0.005

400 500 600 700 800 900

Longitud de onda (nm)

Ref

lect

anci

a (R

rs, s

r-1)

0

0.001

0.002

0.003

0.004

0.005

400 500 600 700 800 900

Longitud de onda (nm)

Ref

lect

anci

a (R

rs, s

r-1)

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

0

0.001

0.002

0.003

0.004

400 450 500 550 600 650 700 750 800 850 900

0

0.001

0.002

0.003

0.004

400 500 600 700 800 900

CASI-2

MERIS

TM

9

6

3

Re

fle

cta

nc e

(%

)

Wavelength (nm)

12

Optical signature of water targets

40

Visible bands

CHRIS/PROBA IMAGERY OVER ALBUFERA DE VALENCIA LAKE

PC March 1st 2007Chl-a March 1st 2007

0 20 60 100 140 180 220 >250 mg m-30 20 60 100 140 180 220 >250 mg m-3

CHRIS / PROBA

41

Hot spots from 21 to 26 August 2007

42

New capabilities for fires monitoring with upcoming sensors

– Bi-temporal and multitemporal detectors

Change detection applications

43

Las Vegas urban development

Athens, PROBA image

44

THERMAL INFRAREDAPPLICATIONS

wat

er c

ycle

carb

on

cyc

le

45

RESOLVED SPATIAL SCALES

global<1 - 300meters Local site Global sampling

severalyears

fewdays

Requires very large time series

RESOLVED TIME SCALES RESOLVED

46

from local measurements to global models

NEW GENERATION OF SENSORS

- Well calibrated (more suitable for multitemporal studies)

- Increased spatial resolution (0.5 m PAN now available)

- Increased spatial coverage (global mapping in highspatial resolution (as ESA GMES/Sentinel-2)

- New type of information (i.e., vegetation fluorescence)

- Time series: gap filling using multi-sensor data, bettertemporal resolution with high spatial resolution

- Integration of multi-resolution data with diverse spectral information in common temporal databases

47

Sen

tin

el-2

Sen

tin

el-3

SE

NT

INE

L-3

SE

NT

INE

L-2

PERSPECTIVES IN DATA EXPLOITATION

- Adequate exploitation of the different data sources: multi-source (multi- resolution data integration).

- Focus on systematic data assimilation approaches exploiting the time-series concept and synergy among simultaneously available satellite systems

- Consistent incorporation in the modelling approaches of processes covering time scales from weeks to decades and exploiting spatially distributed inputs

- Accounting for spatial variability and temporal dynamics as the main contributions

48

Calibration & Validation

Vegetation monitoring

Agriculture

Forestry

Water quality

Climate change

Damage Assessment

Cartography

Physical models

Instrument design

Image processing and analysis

Automatic classification

Analysis of time series

Biophysical parameter estimation

Data fusion

EO Applications Methods

GMES / Sentinels

The near future