Post on 21-Apr-2020
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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
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0
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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)
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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
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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
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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
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Senescent / dryleaves
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spec
ific
ab
sorp
tio
n c
oef
fici
ent
(cm
g
)
2-1
wavelength (nm)
CELLULOSE+LIGNIN ABSORPTION
PROTEIN ABSORPTION
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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
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wavelength ( m)
refl
ec
tan
ce
green vegetation (alfalfa)
senescent vegetation (barley)
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-20
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zeaxanthin-violaxanthin
abso
rpti
on
dif
fere
nce
wavelength (nm)
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violaxanthinzeaxanthin
wavelength (nm)
abso
rpti
on
co
effi
cien
t
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cano
py r
efle
ctan
ce
wavelength (nm)
0.00
0.01
0.02
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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
.)
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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
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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
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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
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0 .12
0 .14
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0 .20
leaf size values
(m)
POLDERnoon flight
POLDERmorning flight
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specular
POLDER data
Irrigated alfalfa field
Channel 3 (550 nm)
Rare events sometimesobserved in real data
Not always predictedby the models!
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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?
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MERIS Landsat TM
HyMapCHRIS
Planck’s Law
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THERMAL INFRARED
18
Kirchoff’s law
energy balance
~ 0
THERMAL INFRARED
THERMAL INFRARED:
Temperature versusemissivity effects
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Data processing methodsfor optical remote sensing data
2
Pre-processing steps:
- Radiometric calibration
- Noise removal
- Cloud screening
- Geometric correction
- Atmospheric correction
- Data integration
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CLOUD SCREENING
- Very dependent on the available spectral information
- Many different algorithms (from simplethresholds up to sophisticate techniques)
Atmospheric effects
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TOA
BOA
SATELLITE SIGNAL MODELLING
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Measured Top-Of-Atmosphere signalS
entin
el-2
SE
NT
INE
L-2
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OUTPUT OF THEATMOSPHERIC/TOPOGRAPHICCORRECTION FOR QUANTITATIVECOMPARISONS IN MULTITEMPORALSTUDIES
62 spectral bands34 m resolution5 view angles
CHRIS/PROBA DATA
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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
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Sentinel‐2
Sentinel‐3
1nm
atmosphere
surface
Atmospheric effects in the thermal domain
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- Monochanel equation
- “Split-window” equation
- Dual-ange equation
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Exploitation of optical remote sensing data for
land science and applications
3
SPECTRALINDICESAS PROXIES
EMPIRICAL APPROACHES
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Visible Atmospherically Resistant Index
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400 700 1000 1300 1600 1900 2200 2500
soil directcanopy directmult iple scat teringtotal ref lectance
wavelength (nm)
refl
ec
tan
ce
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0,1
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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
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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
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Spectral fitting methods are especially useful because we can use the well-known shape of spectral features.
Requires rather high spectral resolution.
0
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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
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0 .1 0 0
Leaf Wat er
Co nt ent (cm )
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700 900 1100
(nm)700 900 1100
(nm)
LAI = 1 LAI = 40.6
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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
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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
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V2 9 V20 V14B V1
Individu al samples
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- vegetation properties- soil properties- solar radiation- atmospheric status- surface fluxes
spatial variability
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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
}
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SPOT-5 / 2005
MOSAICS
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INTEGRATION IN A GIS ENVIRONMENT
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2003 growingSeasonBarrax
Validation 15/07/2003 LANDSAT LAI
0.0
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LANDSAT retrieved
Gro
un
d m
eas
ure
men
ts
ALFALFA
CORN
PAPAVER
POTATO
SUGARBEET
GARLIC
ONION
CORNALFALFA
MULTITEMPORAL SERIES
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lect
anci
a (R
rs, s
r-1)
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CASI-2
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MERIS
TM
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Re
fle
cta
nc e
(%
)
Wavelength (nm)
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lect
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lect
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lect
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rs, s
r-1)
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lect
anci
a (R
rs, s
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lect
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CASI-2
MERIS
TM
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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