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

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

Signal Modellingat the Satellite Level

3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 4

•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

3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 5

MULTIPLE CONTRIBUTIONS TO THE SIGNAL

6S formulation

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Planck’s Law

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0

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Wavelength (nm)

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ianc

e (µ

W/c

m2 /n

m/s

r)

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2.0AtmosphereSolar 1.0 ReflectanceEarth 300 K, 1.0 Emisivity

Earth Radiance (µW

/cm2/nm

/sr)Available Signal

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800

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0.4 0.5 0.6 0.7 0.8 0.9 1

6S

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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

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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)

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800

1000

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2200

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TUILLIER 2003

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0.0

0.5

1.0

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2.5

0

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SMARTS_ChKur

SMARTS_newKur

SMARTS_Gueymard

SMARTS_oldKur

SMARTS_ThKur

SMARTS_Wehrli_WRC85

SMARTS_CebChKur

mW/m2/nmm

W/m

2/nm

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500

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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

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3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 17

ABSOLUTE SOLAR IRRADIANCE CHANGES

δL δT δRL

= 4 + 2T R

Temperature variations

Radius variations

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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

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3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 20

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)

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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

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

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Dealing withatmosphericadjacency effectsin an accurate wayrequires quitecomplicatednumericalcomputations !!!

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Effective atmospheric Point Spread Function (PSF)

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Radiometric corrections:calibration andnoise removal

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Pre-processing steps:- Radiometric calibration

- Noise removal

- Cloud screening

- Geometric correction

- Atmospheric correction

- Database management

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• 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

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Pre-launch calibrations

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AVHRRs’ sensors’ response over time

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Univ. Valencia

M. Cutter

Radiometric calibrationProba/CHRIS data

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ORIGINAL IMAGE CORRECTED IMAGE

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3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 36

NOISE REMOVAL: Example for CHRIS data

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Spectralradiometriccalibration

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Atmospheric correction

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Measured Top-Of-Atmosphere signal

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• TOA radiance modeled assuming Lambertian reflectance for the target:

• Analytically invertible to retrieve ρs.

• Removal of adjacency effects

Surface reflectance retrieval

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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

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• 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

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CLOUD SCREENING

- Very dependent on the availablespectral information

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

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

550nm

AOT retrieval

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Very largevariability in aerosolscontent

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14 July 2003

14 July 2004 17 July 2004

SPARC Campaigns, Barrax (Spain)

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

AerosolsWater vapor

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3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 49

Topographic normalisation

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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

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µs DEM

µil

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DEM L0 Edir·µs Edif

Cosine correction

Hay’s model

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TOTAL APPARENT REFLECTANCE

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SIMPLE SEMI-EMPIRICAL FORMULATIONS OF SURFACEBIDIRECTIONAL REFLECTANCE MODELS USED FOR

ATMOSPHERIC/TOPOGRAPHIC NORMALIZATION

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3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 56

Other simple empirical models:

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BRDF corrections

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nine types of reflectance measurements

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OUTPUT OF THEATMOSPHERIC/TOPOGRAPHICCORRECTION FOR QUANTITATIVECOMPARISONS IN MULTITEMPORALSTUDIES

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

3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 61

PROSPECT+SAIL simulation Actual CHRIS/PROBA data

Alfalfa field

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Bach et al., 2006

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Hot spot effect

POLDER HyMAP

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Working with data series:spatial and spectral

consistency

3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 65

corn

wheat

Temporalevolution of

surface reflectance

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3 September 2007 D1La4 Advanced Optical – Modelling and Data Processing Jose Moreno 67

2003 growingSeasonBarrax

Validation 15/07/2003 LANDSAT LAI

0.0

1.0

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LANDSAT retrieved

Gro

und

mea

sure

men

ts ALFALFACORNPAPAVERPOTATOSUGARBEETGARLICONION

CORNALFALFA

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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

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Temporalseriesmade with a combinationof images frommultiple satellitesmust take care ofdifferent spectralsampling

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“similar”spectralbandsare notalwaysfullycompatible

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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