Optical Theory Basics - 2 Atmospheric corrections and...

Optical TheoryBasics - 2

Atmospheric correctionsand parameter retrieval

Jose Moreno

3 September 2007, Lecture D1Lb2

3 September 2007 D1Lb2 Atmospheric corrections and parameter retrievals Jose Moreno 2

OPTICAL THEORY-FUNDAMENTALS (2)

Radiation laws: definitions and nomenclatureSources of radiation in natural environment in the optical domainInteraction of radiation with matter in the optical domainIllumination and observation geometriesRadiative transfer in the optical domainGeneral solutions for the radiation transfer in the coupled earth-surface and through the atmosphereDerivation of surface reflectance from measured satellite radiancesSpectral information: signatures of natural objectsSpatial information: uniformity, textures and scalesTemporal information: land surface dynamics at multiple scalesInformation retrieval: from spectral indices to model inversionOverview of applications


Derivation of surfacereflectance from measured

satellite radiance


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


CLOUD SCREENING

- Very dependent on the availablespectral information

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


SATELLITE SIGNAL MODELLING


•The atmosphere modifies theradiation measured by opticalsensors:• Aerosols and gases presentoptical activity at VIS/NIR/SWIR.• Reflectanceincreases/decreases dependingon the wavelength.• Image loses contrast.

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


• 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


Atmospheric composition

Permanent Constituents Variable constituents

Constituent % byvolume

Constituent % by volume

Nitrogen (N2) 78.084 Water Vapor (H2O) 0.04Oxygen (O2) 20.948 Ozone (O3) 12 x 10-4

Argon (Ar) 0.934 Sulfur dioxide (SO2)b 0.001 x 10-4

Carbon dioxide (CO2) 0.033 Nitrogen dioxide (NO2) 0.001 x 10-4

Neon (Ne) 18.18 x 10-4 Ammonia (NH3) 0.001 x 10-4

Helium (He) 5.24 x 10-4 Nitric oxide (NO) 0.0005 x 10-4

Krypton (Kr) 1.14 x 10-4 Hydrogen sulfide (H2S) 0.00005 x 10-4

Xenon (Xe) 0.089 x 10-4 Nitric acid vapor traceHydrogen (H2) 0.5 x 10-4

Methane (CH4) 1.5 x 10-4

Nitrous Oxide (N2O) 0.27 x 10-4

Carbon Monoxide (CO) 0.19 x 10-4


110010009008007006005004000 .2

0 .4

0 .6

0 .8

1 .0

1 .2

1 .4

1 .6

dir ectglobal

wavelength (nm)

sola

r rad

iati

on(W

m

nm

)-1

-2

Relative Humidity (%)

0

5000

10000

15000

20000

25000

30000

35000

0 20 40 60 80 100RH (%)

Heig

ht (g

pm)

Temperature

0

5000

10000

15000

20000

25000

30000

35000

-80,0 -60,0 -40,0 -20,0 0,0 20,0 40,0Temperature (¼C)

Heig

ht (g

pm)

110010009008007006005004000 . 0

0 . 2

0 . 4

0 . 6

0 . 8

1 . 0

1 . 2

wavelength (nm)

Tota

l Opt

ical

Dep

th

Ozone

0

5000

10000

15000

20000

25000

30000

35000

0,00 2,00 4,00 6,00 8,00 10,00 12,00 14,00 16,00

Ozone (mPa)

Hei

ght (

gpm

)

In-situ radiosoundingsIn-situ spectral irradianceATMOSPHERIC INFORMATION


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

AerosolsWater vapor


TOA BOA


+ =

µs DEMµil

Cosine correction

Hay’s model

Topographic effects


BRDF effects


Spectral information:signatures of natural objects


+90

0

-90400 2500wavelength (nm)

view zenith angle

wavelength (nm)

Hyperspectral BRDFSolar Principal Plane

C =0.0405 cmwaterview

zen

ith a

ngle

0.51

0.00

400

2500

-90

+90


SOIL REFLECTANCE


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)

refle

ctan

cegreen vegetation (alfalfa)senescent vegetation (barley)

μ


refle

ctan

ce

wavelength (nm)

0

5

10

15

20

25

30

35

40

45

50

55

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

bare soilorganic litter (humic acid)


MERIS Landsat TM

HyMapCHRIS


Spatial information:uniformity, textures

and scales


SPATIAL SCALES TO BE RESOLVED 10 km toglobal

<1 - 300meters

Local site Global sampling


VIS/NIR/SWIR Colour Composite Thermal data

Multiresolution data

1.25 m

3.75 m

12.0 m


Spatialinformationin theimages

- Textures

- Higher orderstatistics


countingindividual trees by usingQuickbirdvery highresolutionimagery


0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,30

0

0,32

5

0,35

0

0,37

5

0,40

0

0,42

5

0,45

0

0,47

5

0,50

0

0,52

5

0,55

0

0,57

5

0,60

0

0,62

5

0,65

0

0,67

5

0,70

0

0,72

5

0,75

0

0,77

5

0,80

0

0,82

5

0,85

0

0,87

5

0,90

0

0,92

5

0,95

0

0,97

5

1,00

0

1,02

5

1,05

0

1,07

5

1,10

0

PAN

BLUE

GREEN

RED

NIR

QuickBird


Temporal information:land surface dynamics

at multiple scales


> 3years

3 - 10days

Requires very large time series

TIME SCALES TO BE RESOLVED


Time series: Coupling canopyfunctioning and radiative transfermodels for remote sensing data assimilation


MERIS - 14 July 2003 Landsat - 15 July 2003

MERIS and Landsat both provide time series at different spatial and temporal scales


Stable calibration,adequate cloud screeningand precise atmosphericcorrections are needed fortime series analisis


Information retrieval:from spectral indices

to model inversion


MERIS Terrestrial Chlorophyll Index (MTCI)


Retrievals from hyperspectral data:

Canopy watery = 3,1431x + 857,

R2 = 0,5797

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

-100 0 100 200 300 400 500 600 700 800 900

Index

Corn bar1_12Subarbeet_bar1_12Barley bar1_12Wheat_bar1_12Alfalfa bar_1_12Corn bar2_12Sugarbeet bar2_12Barley bar2_12wheat bar2_12Alfalfa bar2_12Sugarbeet bar1_9Alfalfa bar1_9Sugarbeet bar2_9Alfalfa bar2_9Sugarbeet bar1_15Alfalfa bar1_15Subarbeet bar2_15Alfalfa bar2_15

y = 0,0359x + 0,347R2 = 0,3789

0

5

10

15

20

25

30

35

40

45

-100 0 100 200 300 400 500 600 700 800 900

Index

Corn bar1_12Sugarbeet bar1_1Barley bar1_12Wheat bar1_12Alfalfa bar1_12Corn bar2_12Sugarbeet bar2_1Barley bar2_12Wheat bar2_12Alfalfa bar2_12Sugarbeet bar1_9Alfalfa bar1_9Sugarbeet bar2_9Alfalfa bar2_9Sugarbeet bar1_1Alfalfa bar1_15Sugarbeet bar2_1Alfalfa bar2_15

Leaf chlorophyllLeaf chlorophyll


Verhoef and Bach, 2007


• turbid medium• separation green / senescent parts: LAIT = LAIG + LAIS


Choice of the merit function

Incorporation of the uncertainties in the inverse process:

[ ] [ ] [ ] [ ]ptpCRMmes

t

CRMmes VVCVVVRRWVRR −−+−−= −− 112 )()(χ

The maximum likelihood on the variables minimize:

Residuals

a prioriCovariance Matrix

Residuals

a prioriCovariance Matrix

Radiometric Part Variable Part2)(∑ ⎥⎦⎤

⎢⎣⎡ −M

iiR

iCRM

imes VRRσ

2

∑ ⎥⎦⎤

⎢⎣⎡ −N

jiV

ipi VVσif diagonal matrixes


Neuralnetworkmethods


Spectral-angular synergy:

application of synergy

0

5

10

15

20

25

30

35

40

45

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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

ctan

cere

flect

ance

ALFALFA

Li ne 1 , n oo nLi ne 2 , n oo nLi ne 1 , mo rn in gLi ne 2 , mo rn in gLi ne 1 , a f t e rn oo nLi ne 2 , a f t e rn oo n

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

Li ne 1 , n oo nLi ne 2 , n oo nLi ne 1 , mo rn in gLi ne 2 , mo rn in gLi ne 1 , a f t e rn oo nLi ne 2 , a f t e rn oo n

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

Homogeneous vegetation:Spectral information dominant

Heterogeneous vegetation:Angular information dominant


Overview of applications


- 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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ercy

cle

carb

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Optical Theory Basics - 2 Atmospheric corrections and...

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