Advanced Optical theory Modelling and Data...

Advanced Optical theoryModelling and Data

ProcessingJose Moreno

3 September 2007, Lecture D1La4

3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 2

ADVANCED OPTICAL THEORYPart I: Signal modelling and data pre-processing

Signal modelling at the satellite level

Radiometric corrections: calibration and noise removal

Atmospheric correction

Topographic normalisation

BRDF corrections

Working with data series: spatial and spectral consistency


Signal Modellingat the Satellite Level


•The atmosphere modifies theradiation measured by opticalsensors:• Aerosols and gases presentoptical activity at VIS/NIR/SWIR.• Reflectance increases / decreases depending on thewavelength.• Image loses contrast.

Removing the atmospheric influence from remote sensing data is necessary before data exploitation


MULTIPLE CONTRIBUTIONS TO THE SIGNAL

6S formulation


Planck’s Law


0

10

20

30

40

50

60

70

0 2000 4000 6000 8000 10000 12000 14000

Wavelength (nm)

Sola

r Rad

ianc

e (µ

W/c

m2 /n

m/s

r)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0AtmosphereSolar 1.0 ReflectanceEarth 300 K, 1.0 Emisivity

Earth Radiance (µW

/cm2/nm

/sr)Available Signal


800

1000

1200

1400

1600

1800

2000

2200

0.4 0.5 0.6 0.7 0.8 0.9 1

6S


0100200300400500600700800900

10001100120013001400150016001700180019002000210022002300240025002600

0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00

wavelength ( m)

Sola

r Irr

adia

nce

(W m

m

)-1

-2μ

μ


Thuillier model (2003)Based on solar spectral irradiance measurements from:

(a) SOLar SPECtrum (SOLSPEC) spectrometerflew with the ATmospheric Laboratory for Applicationsand Science (ATLAS) missions

(b) SOlar SPectrum (SOSP) spectrometerflew on the EUropean Retrieval CArrier (EURECA) missions

and data from Upper Atmosphere Research Satellite (UARS)


800

1000

1200

1400

1600

1800

2000

2200

400 500 600 700 800 900 1000

TUILLIER 2003


0.0

0.5

1.0

1.5

2.0

2.5

0

500

1000

1500

2000

2500

400 500 600 700 800 900 1000

SMARTS_ChKur

SMARTS_newKur

SMARTS_Gueymard

SMARTS_oldKur

SMARTS_ThKur

SMARTS_Wehrli_WRC85

SMARTS_CebChKur

mW/m2/nmm

W/m

2/nm


500

600

700

800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900

0.60 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68 0.69 0.70 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.80

wavelength ( m)

Sola

r Irr

adia

nce

(W m

m

)-1

-2μ

μ


ABSOLUTE SOLAR IRRADIANCE CHANGES

δL δT δRL

= 4 + 2T R

Temperature variations

Radius variations


COUPLING OF AEROSOLS AND WATER VAPOUR VERTICAL STRUCTURE

Vertical aerosol amount andtype variability:- continental bottom layer- maritime upper layer

+ diurnal boundary layerevolution

aerosols vertical structure water vapour vertical structure

High spatial and temporalvariability:- atmospheric circulation- topography

+ turbulent structure


spectral scattering albedorural aerosol model

(for 0, 70, 80 and 99% humidity)

spectral scattering albedomaritime aerosol model

(for 0, 70, 80 and 99% humidity)

SPECTRAL VARIABILITY IN AEROSOLS EFFECTS

coupled to angular variability (different phase functions)


Aerosol type phase functionλ = 550 nm

ANGULAR VARIABILITY IN AEROSOLS EFFECTS

coupled to vertical structure

aerosols can change the shape ofthe TOA-observed surface BRDF


MODELING OF ADJACENCY EFFECTS IN THE DEFINITION OF SPATIALLY AVERAGED 'ENVIRONMENT' REFLECTANCES


Dealing withatmosphericadjacency effectsin an accurate wayrequires quitecomplicatednumericalcomputations !!!


Effective atmospheric Point Spread Function (PSF)


Radiometric corrections:calibration andnoise removal


Pre-processing steps:- Radiometric calibration

- Noise removal

- Cloud screening

- Geometric correction

- Atmospheric correction

- Database management


• Pre-launch radiometric calibration to traceable standard (accepted reference)

• Post launch calibration campaigns to maintain/monitoring in flight calibration (vicarious)

• On-board calibration (both radiometric and spectral)

RADIOMETRIC CALIBRATION


Pre-launch calibrations


AVHRRs’ sensors’ response over time


Univ. Valencia

M. Cutter

Radiometric calibrationProba/CHRIS data


ORIGINAL IMAGE CORRECTED IMAGE


NOISE REMOVAL: Example for CHRIS data


Spectralradiometriccalibration


Atmospheric correction


Measured Top-Of-Atmosphere signal


• TOA radiance modeled assuming Lambertian reflectance for the target:

• Analytically invertible to retrieve ρs.

• Removal of adjacency effects

Surface reflectance retrieval


Non-Lambertian areas with topographic structure:- no analytic inversion under approximations- decoupling 'effective' reflectances and 'effective‘ geometric termsrequired for environment

- multistep numerical procedure required for inversion- multiple reflection terms only significant for high reflectance surroundings

Flat Lambertian areas:

INVERSION OF SURFACE REFLECTANCE


• Cloud screening based on static thresholds over TOA reflectance and spectral slope.

• 2 sets of thresholds:– “Restrictive” set: detects pixels with minimum probability of

clouds AOT retrieval– “Relaxed” set: detects pixels with a high probability of

clouds CWV and reflectance retrieval

Cloud masking

Relaxed Restrictive


CLOUD SCREENING

- Very dependent on the availablespectral information

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


• Retrieval of AOT for each cell• Filling-in empty cells.• Conversion from VIS to AOT at

550nm

AOT retrieval


Very largevariability in aerosolscontent


14 July 2003

14 July 2004 17 July 2004

SPARC Campaigns, Barrax (Spain)


Surface reflectance retrievalAtmospheric correction: Removal of the atmospheric effectsfrom the measured at-sensor radiance, leading to thederivation of surface reflectance images.

AerosolsWater vapor


Topographic normalisation


Effects introduced by topography: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 illuminationconditions:

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


µs DEM

µil


DEM L0 Edir·µs Edif

Cosine correction

Hay’s model


TOTAL APPARENT REFLECTANCE


SIMPLE SEMI-EMPIRICAL FORMULATIONS OF SURFACEBIDIRECTIONAL REFLECTANCE MODELS USED FOR

ATMOSPHERIC/TOPOGRAPHIC NORMALIZATION


Other simple empirical models:


BRDF corrections


nine types of reflectance measurements


OUTPUT OF THEATMOSPHERIC/TOPOGRAPHICCORRECTION FOR QUANTITATIVECOMPARISONS IN MULTITEMPORALSTUDIES


BRDF normalization with aclass specific Ambrals model(U. Beisl, 2001)


PROSPECT+SAIL simulation Actual CHRIS/PROBA data

Alfalfa field


Bach et al., 2006


Hot spot effect

POLDER HyMAP


Working with data series:spatial and spectral

consistency


corn

wheat

Temporalevolution of

surface reflectance


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

und

mea

sure

men

ts ALFALFACORNPAPAVERPOTATOSUGARBEETGARLICONION

CORNALFALFA


SPATIAL CONSISTENCY

- Spatial resolution of individual images in the series

- Geocoding accuracy- Resampling methods

- Changes in view angle implychanges in geometry (resolution)

- Composites of multiple satellite dataare especially critical


Temporalseriesmade with a combinationof images frommultiple satellitesmust take care ofdifferent spectralsampling


“similar”spectralbandsare notalwaysfullycompatible


MODELLING ASPECTS- bare soil bi-directional reflectance modelling - leaf reflectance/transmittance modelling - canopy bi-directional reflectance modelling - atmospheric effects in surface reflectance (diffuse radiation) - at sensor radiance modelling (surface+atmosphere)

model inversion techniques classification techniques

- energy/water surface-atmosphere exchange processes - role of vegetation dynamics in the terrestrial carbon cycle

data assimilation techniques spectral unmixing

Advanced Optical theory Modelling and Data...

Documents

Transcript of Advanced Optical theory Modelling and Data...