Guillem Sòria, José A. Sobrino, Juan C. Jiménez-Muñoz ... fileMireia Romaguera and Malena...

MERIS – (A)ATSR WORKSHOP ESRIN, Frascati, Italy 26-30 September 2005 1 of 26

Guillem Sòria, José A. Sobrino,Juan C. Jiménez-Muñoz, Mónica Gómez, Juan Cuenca,

Mireia Romaguera and Malena Zaragoza

University of Valencia, Spain


IndexIndex

1.1. AATSR LST AATSR LST algorithmsalgorithms proposedproposed

a)a) MethodsMethods ofof atmosphericatmospheric correctioncorrection

b)b) CoefficientsCoefficients calculationcalculation

c)c) SplitSplit--windowwindow andand DualDual--AngleAngle methodmethod

2. Validation over an homogeneous area

3. Study over heterogeneous areas

a) Marrakech Field Experiment 2003

b) Barrax Field Experiment 2004

c) Classification process

4. Conclusions


AATSR AATSR CharacteristicsCharacteristics

AATSR: Advanced Along-Track Scanning Radiometer

Provide two views of the surface, nadir and forward (0 and 55 degrees)

and thus improve atmospheric correction

AATSR has 4 NIR/VIS and 3 TIR channels, with a spatial resolution of

1km x 1km at nadir view and 1.5km x 2km at forward view


iθτ iθτ iθτ

1. AATSR 1. AATSR algorithmsalgorithms proposedproposed

a) a) MethodsMethods ofof atmosphericatmospheric correctioncorrection

SplitSplit--windowwindow methodmethod::The SW method uses observations at two different wavelengths with the

same observation angle.

2ji εε

ε+

=210 )1()( BBBTTATT jiis εε ∆−−+−−+=

ji εεε −=∆DualDual--angleangle methodmethod::

The DA method uses observations at two different observation angles

within the same wavelength interval

210 )1()( BBBTTATT nfnns θεε ∆−−+−−+= fn εεεθ −=∆


iθτ iθτ iθτ


b) b) CoefficientsCoefficients calculationcalculation

The algorithms coefficients were obtained from simulated data to applythem to a large amount of surfaces.

From MODTRAN simulation code:

transmissivity transmissivity θτ i

↑atmiR θ

↓atmiR θupwellingupwelling and downwelling and downwelling

atmospheric atmospheric radianceradiance

waterwater vapor vapor contentcontent W

From laboratory spectral library, a set of emissivity values of:

VegetationVegetation: : GrassGrass, , ConifersConifers, , DecidiousDecidious

BareBare soilssoils, , rocksrocks

WaterWater11µm and 12 µm channels,nadir and forward views.



c) c) SplitSplit--windowwindow andand DualDual--AngleAngle algorithmsalgorithms

Algorithm Expression

SW n, Quad: Ts = T2n + 0.61(T2n-T1n) + 0.31(T2n-T1n)2 + 1.92

SW n, Quad, ε: Ts = T2n + 0.76(T2n-T1n) + 0.30(T2n-T1n)2 + 0.10 + 51.2(1-ε)

SW n, Quad, ε, ∆ε: Ts = T2n + 1.03(T2n-T1n) + 0.26(T2n-T1n)2 – 0.11 + 45.23(1-ε) – 79.95∆ε

SW n (W), ε, ∆ε, W: Ts = T2n + (1.01+ 0.53W)(T2n-T1n) + (0.4-0.85W) + (63.4-7.01W)(1-ε) - (111-17.6W)∆ε

SW n, Quad, ε, ∆ε, W: Ts = T2n + 1.35(T2n-T1n) + 0.22(T2n-T1n)2 – (0.82-0.15W) + (62.6-7.2W)(1-ε) - (144-26.3W) ∆ε

SW n, Quad(W), ε, ∆ε, W: Ts = T2n + (1.97+0.2W)(T2n-T1n) - (0.26-0.08W)(T2n-T1n)2 + (0.02-0.67W) + (64.5-7.35W)(1-ε) - (119-20.4W) ∆ε

DA 11 Quad: Ts = T2n + 1.36(T2n-T2f) + 0.18(T2n-T2f)2 + 1.78

DA 11 Quad, ε: Ts = T2n + 1.56(T2n-T2f) + 0.15(T2n-T2f)2 - 0.34 + 51.9(1-ε2n)

DA 11 Quad, ε, ∆ε: Ts = T2n + 1.57(T2n-T2f) + 0.15(T2n-T2f)2 –0.11 + 51.7(1-ε2n) – 25.8∆εθ

DA 11 W, ε, ∆ε, W: Ts = T2n + (1.62+0.3W)(T2n-T2f) + (0.18-0.52W) + (70.1-7.18W)(1-ε2n) - (35.4-3.67W)∆εθ

DA 11 Quad, ε, ∆ε, W: Ts = T2n + 1.92(T2n-T2f) + 0.12(T2n-T2f)2 – (0.39+0.09W) + (71-7.55W)(1-ε2n) - (35.8-3.88W)∆εθ

DA 11 Quad(W), ε, ∆ε, W: Ts = T2n + (2.67-0.07W)(T2n-T2f) - (0.29-0.09W)(T2n-T2f)2 - (0.31+0.28W) + (72.5-7.9W)(1-ε2n) - (35.8-4.1W)∆εθ



c) c) SensitivitySensitivity analysisanalysis fromfrom error error theorytheory ofof thethe algorithmsalgorithms

Algorithm σmod (K)

σnoise (K)

σε (K)

σWV (K)

σtotal (K)

SW n, Quad: 1.73 0.07 1.73

SW n, Quad, ε: 1.39 0.07 0.18 1.40

SW n, Quad, ε, ∆ε: 1.05 0.09 0.59 1.20

SW n (W), ε, ∆ε, W: 0.59 0.10 0.83 0.45 1.12

SW n, Quad, ε, ∆ε, W: 0.93 0.11 1.06 0.20 1.43

SW n, Quad(W), ε, ∆ε, W: 0.52 0.15 0.89 0.37 1.10

DA 11 Quad: 1.31 0.11 1.32

DA 11 Quad, ε: 0.72 0.12 0.18 0.75

DA 11 Quad, ε, ∆ε: 0.69 0.13 0.26 0.74

DA 11 W, ε, ∆ε, W: 0.47 0.13 0.35 0.36 0.70

DA 11 Quad, ε, ∆ε, W: 0.57 0.15 0.36 0.17 0.71

DA 11 Quad(W), ε, ∆ε, W: 0.38 0.20 0.37 0.24 0.62

σmod: residual atmospheric error.

σnoise: noise error: NE∆T=0.05 K.

σε: emissivity error: ε(ε)= 0.005.

σWV: water vapor column error:

ε(WV)= 0.5 gcm-2.

{ }2222mod WVnoisetotal σσσσσ ε +++=


2. 2. ValidationValidation overover anan homogeneoushomogeneous areaarea

Data Data providedprovided by A. by A. PrataPrata, CSIRO, Australia, CSIRO, Australia

Algorithm σ teoric (K)

σvalidació (K)

Bias (K)

RMSD (K)

SW n, Quad: 1.73 1.51 1.59 2.19 SW n, Quad, ε: 1.40 1.56 1.48 2.15

SW n, Quad, ε, ∆ε: 1.20 1.66 1.28 2.09 SW n (W), ε, ∆ε, W: 1.12 1.93 1.28 2.31

SW n, Quad, ε, ∆ε, W: 1.43 1.81 1.13 2.13 SW n, Quad(W), ε, ∆ε, W: 1.10 2.06 1.17 2.36

DA Quad: 1.32 1.24 0.36 1.29 DA Quad, ε: 0.75 1.29 0.02 1.29

DA Quad, ε, ∆ε: 0.74 1.29 0.02 1.29 DA W, ε, ∆ε, W: 0.70 1.25 -0.21 1.27

DA Quad, ε, ∆ε, W: 0.71 1.39 -0.35 1.43 DA Quad(W), ε, ∆ε, W: 0.62 1.28 -0.39 1.34


3. 3. Study over heterogeneous areas

Barrax, Albacete, Spain

5 march 2003

Marrakech, Morocco

17 july 2004



a) Marrakech a) Marrakech campaigncampaign

Marrakech fieldexperiment took place in an area of thewater-catchment of theTensift river in marchof 2003.

(31º40’ N, 07º35’ W, 600 m elevation)



a) Marrakech a) Marrakech campaigncampaign

Bare Soil field

Mixed SiteVegetation + Bare Soil

Vegetated field



b) b) BarraxBarrax campaigncampaign

( 39º03’ N, 02º06’ W, 700 m elevation)Barrax test site is situated within La Mancha, 20 km far away from the capital town Albacete.

The area around Barrax is characterised by a flat morphology and large, uniform landuse units.



b) b) BarraxBarrax campaigncampaignChris / Proba image acquired during the campaign, near to the AATSR overpass to avoid changes in crop growth.

Alfalfa

Corn

Green grass

Wheat

Bare Soil

Thermal measurements in:


c) c) ClassificationClassification processprocess3. 3. Study over heterogeneous areas

The Chris/Proba pixels can be identifiedaccording to their NDVI values.

NDVI Crops

0Nadir view Bare soil

Cut Wheat

0.3

0.5Green grass

Corn

Alfalfa0.9


ClassificationClassification processprocess

The Chris/Proba pixels can be identifiedaccording to their NDVI values. NDVI Crops

0Forward view ( 55 degrees)Forward view ( 55 degrees) Bare soil

Cut Wheat

0.3

0.5Green grass

Corn

Alfalfa0.9


ClassificationClassification processprocess

Measurement strategy: pixels classified into 3 classes.

Bare Soil

Cut wheat

Alfalfa

NDVI


ClassificationClassification processprocess overover a a ChrisChris/Proba /Proba imageimageSupervised - Maximum Likelihood classification

Bare Soil

Cut wheat

Alfalfa

Grid of AATSR pixels


ClassificationClassification processprocess overover a a ChrisChris/Proba /Proba imageimageSupervised - Maximum Likelihood classification

Proportion of Crops in every AATSR pixel

From in-situ temperatureand emissivity of eachcrop, it is possible toobtain LST and LSE foreach pixel to validateAATSR data.

EffectiveLST andLSE of eachpixel

Class Crop1 Bare Soil2 CutWheat3 Alfalfa

Class %1 67.5%2 20.6%3 11.9%

Class %1 31.4%2 15.7%3 52.8%

Class %1 39.9%2 33.7%3 26.4%

Class %1 37.3%2 7.4%3 55.3%


ValidationValidation ofof LST LST fromfrom algorithmsalgorithms andand insituinsitu datadata

The AATSR algorithms need the following data:radiometric temperature from the AATSR image, value of the water vapor content and emissivity of both spectral bands and view angles.

Data from Barrax campaign Pixel 1 Pixel 2 Pixel 3 Pixel 4

Radiometric Temperature 11µm nadir (K) 303.8 300.8 301.4 300.4

Radiometric Temperature 11µm forward (K) 300.1 301.2 300.1 299.1

Radiometric Temperature 12µm nadir (K) 300.4 297.9 298.5 297.5

W (g/cm2) 2.36 2.36 2.36 2.36

Emissivity nadir (11µm) 0.971 0.980 0.977 0.980

Emissivity nadir (12µm) 0.976 0.985 0.982 0.985

Emissivity forward (11µm) 0.959 0.965 0.963 0.965


ValidationValidation ofof LST LST fromfrom algorithmsalgorithms andand insituinsitu datadataDifference between LST from algorithm and LST measured in situ.

Higher errors are observed in the DA algorithms.

Difference LSTalgorithm – LST insitu Pixel 1 Pixel 2 Pixel 3 Pixel 4

1 -1.7 1.9 -0.8 0.9 2 -1.8 1.3 -1.2 0.3 3 -1.3 1.9 -0.7 0.9 4 -1.6 1.7 -0.8 0.8 5 -1.0 2.1 -0.4 1.1

SW algorithms

6 -1.8 1.7 -0.8 0.7 1 0.0 -3.1 -3.3 -1.4 2 1.5 -5.0 -3.2 -2.4 3 1.5 -5.1 -3.4 -2.6 4 1.3 -6.4 -3.6 -2.7 5 1.9 -5.8 -3.4 -2.6

DA algorithms

6 1.0 -6.4 -3.4 -2.6

1 2 34

56

SW

DA-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

LST

diff

eren

ce

Algorithm

Pixel #1

12

34

56

SW

DA-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

LST

diffe

renc

e

Algorithm

Pixel #2

12

34

56

SW

DA-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

LST

diffe

renc

e

Algorithm

Pixel #3

12

34

56

SW

DA-6.00

-4.00

-2.00

0.00

2.00

4.00

6.00

LST

diffe

renc

e

Algorithm

Pixel #4


ClassificationClassification processprocess overover a Landsat5/TM a Landsat5/TM imageimage

Supervised - Maximum Likelihood classification

Specific classes selected: Bare Soil Mixed Vegetated


ValidationValidation ofof LST LST fromfrom algorithmsalgorithms andand insituinsitu datadata

Marrakech Field Campaign Barrax Field Campaign

Difference LSTalgorithm – LST insitu Bias, K σ, K rmse, K

1 -2.22 1.17 2.51

2 -2.54 1.16 2.79

3 -1.75 1.16 2.10

4 -1.48 1.15 1.88

5 -1.33 1.17 1.77

SW algorithms

6 -1.11 1.16 1.61

1 -1.22 3.58 3.78

2 -1.60 3.80 4.12

3 -1.49 3.82 4.10

4 -1.62 3.79 4.12

5 -1.24 4.26 4.43

DA algorithms

6 -1.79 4.05 4.42

Difference LSTalgorithm – LST insitu Bias, K σ, K rmse, K

1 1.82 3.38 3.84

2 1.38 3.28 3.56

3 1.90 3.30 3.80

4 1.63 3.22 3.61

5 2.16 3.31 3.95

SW algorithms

6 1.54 3.17 3.53

1 0.67 5.22 5.27

2 0.37 5.69 5.70

3 0.26 5.72 5.72

4 -0.26 5.73 5.73

5 0.26 6.04 6.05

DA algorithms

6 -0.38 5.40 5.42

Averaged values from a 4x4 grid of AATSR pixels


RescallingRescalling ofof forwardforward AATSR AATSR pixelspixelsThe higher uncertainties observed in the evaluation of the DA algorithmsare supposed to be a problem of the different footprint associated to the

AATSR nadir and forward pixels.

This effect is currently under study.

Nadir pixels: 1km x 1kmThese pixels are regridded onto a 1x1km grid. A process of pixel make-up is carried out.

Forward pixels: 1.5km x 2km


4. 4. Conclusions

SW and DA algorithms have been proposed

DA are better than SW in the simulation process, confirmed in homogeneous areas

In heterogeneous areas DA are worse than SW algorithms, confirmed in Marrakech and Barrax campaigns

Due to different footprint between nadir and forward images

Additional information is necessary to consider this effect


-3

-2

-1

0

1

2

3

4

5

6

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

NDVI nadir

Tnad

ir - T

forw

ard

(ºC

)

-3

-2

-1

0

1

2

3

4

5

6

7

-0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25

NDVI nadir - NDVI forward

Tnad

ir - T

forw

ard

(ºC

)

Guillem Sòria, José A. Sobrino, Juan C. Jiménez-Muñoz ... fileMireia Romaguera and Malena...

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