J. A. Sobrino, G. Sòria, M. Atitar, A. B. Ruescas, J. C...

J. A. Sobrino, G. Sòria, M. Atitar, A. B. Ruescas, J. C. Jiménez-Muñoz, V. Hidalgo, Y. Julien, B. Franch

Global Change Unit, IPL ‐ University of Valencia, Spain

sobrino@uv.es

963543115

2nd MERIS/(A)AATSR User Workshop

22 to 26 September 2008 · ESA/ESRIN Frascati

2nd MERIS/(A)AATSR User Workshop · 22 to 26 September 2008 · ESA/ESRIN Frascati

PRESENTATION SCHEMEPRESENTATION SCHEME

1.1.-- CEFLES2 field campaignCEFLES2 field campaign

Instrumental DescriptionInstrumental Description

Field dataField data

2.2.-- LSTLST

AATSR AATSR

SEVIRISEVIRI

MODISMODIS

3.3.-- COMPARISONCOMPARISON

4.4.-- CONCLUSIONSCONCLUSIONS

The CEFLES2 (CarboEurope, FLEX and Sentinel-2) experimental field campaign wasconceived as a collective multi-objective campaign exploiting synergies between three experiments collocated in the Les Landes region of France during the period April to September 2007, thus encompassing a large fraction of the vegetation cycle of the different land covers and crops.

1. CEFLES2 campaign1. CEFLES2 campaign

The objective within theCEFLES2 campaign was tocollect biophysical paramethersdata related with thephotosinthesis, energy fluxes, etc, in different agriculturaland forest areas.

Measurement activities were constrained within a rectangle from Bordeaux to Toulouse in Southwest France. The majority of sites lie in the Les Landes forested area and the adjacent intermediate zone, in the transition between forested and cultivated areas.

The Global Change Unit measurements were carried out mainly in the agricultural areanear Marmande and the forest area of Le Bray

• Marmande site:

The site comprises large fields of winter wheat, corn and rapeseed at three separate locations on flat land in the Garonne valley bottom.

Continuos measurements werecarried out using radiometersinstalled on masts, simultaneoslywith the sensors overpasses.

MaizeMaize (P222)(P222)

WheatWheat (P250)(P250)

GrassGrass (P823)(P823)2nd MERIS/(A)AATSR User Workshop · 22 to 26 September 2008 · ESA/ESRIN Frascati

• Le Bray Forest site:

In the forest site of Le Bray, thermal measurements were carried out using 3 different broad band radiometers located on top of a tower and pointing at nadir and 55º, every 5 minutes.

• Thermal radiometers:

• Calibration sources:EVEREST 1000EVEREST 1000

EVEREST 4000EVEREST 4000RaytekRaytek STST RaytekRaytek MIDMID OPTRIS OPTRIS MiniSightMiniSight PlusPlus

CIMEL CIMEL CE 312CE 312--1& 21& 2

• Thermal camera:NEC TH9100 proNEC TH9100 pro

1. Instrumental1. Instrumental

Le Bray forest were Pine trees andground in between are visible. Continuous radiometric temperaturemeasurements in two viewing angles(nadir and 50º), with Raytek MID and Everest 4000 radiometers.

Data gap betweendays 133 to 144

1. Field data1. Field data

• Some examples of measured values in Le Bray Forest:

2. LST ALGORITHMS2. LST ALGORITHMSRADIATIVE TRANSFER EQUATION

, , , , ,( ) (1 )sen atm atmi i i s i i i iL B T L Lθ θ θ θ θε ε τ↓ ↑⎡ ⎤= + − +⎣ ⎦

TTii TTgg AT-SENSOR RADIANCE=

SURFACE EMISSION ATENUATTED BY THE

ATMOSPHERE+

GROUND REFLECTED RADIANCE ATENUATTED BY THE

ATMOSPHERE+

ATMOSPHERIC EMISSION

RTE applied to 1 band ⇒ SINGLE-CHANNEL METHODSRTE applied to 2 bands ⇒ TWO-CHANNEL (SW) METHODSRTE applied to 2 observation angles ⇒ DUAL-ANGLE METHODS

Advanced Along-Track Scanning Radiometer

• two view angles: nadir (0º) and forward (55º)

• pixel size: 1 km2 nadir, 1.5 km x 2 km forward

• swath of about 500 km (555 pixels nadir, 371 forward)

• 7 spectral bands: visible, near infrared thermal infrareden

2. 2. LST ALGORITHMSLST ALGORITHMS. AATSR. AATSR

ESA Level2 product of LST is processed from:

“Land Surface Temperature Measurement from Space; AATSR Algorithm Theoretical Basis Document” by A. Prata.

SplitSplit--windowwindow methodmethod::

DualDual--angleangle methodmethod::

2. 2. LST ALGORITHMSLST ALGORITHMS. AATSR. AATSR

The SW method uses observations at two differentwavelengths with the same observation angle

The DA method uses observations at two different observation angles within the same wavelength interval

A set of algorithms were proposed to retrieve LST from AATSR data using Split-window andDual-angle methods. A deep analysis of this algorithms and its validation over homogeneousand heterogeneous sites can be found in the paper:

Soria G. and Sobrino, J.A., (2007) ENVISAT/AATSR derived Land Surface Temperature over a heterogeneous region. Remote Sensing of Environment 111

A split window algorithm has been cosen in order to make the comparison withthe Level2 product

SplitSplit--windowwindow methodmethod::

The SW method uses observations at two different wavelengths with the

same observation angle.

210 )1()( BBBTTATT jiis εε Δ−−+−−+=2

ji εεε

ji εεε −=Δ

DualDual--angleangle methodmethod::

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

The DA method uses observations at two different observation angles

within the same wavelength interval

iθτ iθτ iθτ 2. LST 2. LST AlgorithmsAlgorithms. AATSR. AATSR

A set of algorithms were proposed to retrieve LST from AATSR data usingSplit-window and Dual-angle methods. A deep analysis of this algorithms andits validation over homogeneous and heterogeneous sites can be found in Soria and Sobrino, Remote Sensing of Environment 111, 2007.

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

iθτ iθτ iθτ

From MODTRAN simulation code:

transmissivitytransmissivity 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.

-- CoefficientsCoefficients calculationcalculation

2. LST 2. LST AlgorithmsAlgorithms. AATSR. AATSR

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, Δε: Ts = T2n + 1.03(T2n-T1n) + 0.26(T2n-T1n)2 – 0.11 + 45.23(1-ε) – 79.95Δε

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

SW n, Quad, ε, Δε, 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) Δε

SW n, Quad(W), ε, Δε, 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) Δε

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, Δε: Ts = T2n + 1.57(T2n-T2f) + 0.15(T2n-T2f)2 –0.11 + 51.7(1-ε2n) – 25.8Δεθ

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

DA 11 Quad, ε, Δε, 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)Δεθ

DA 11 Quad(W), ε, Δε, 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)Δεθ

Algorithms with different number of inputs: Brightness Temperature, emissivity and water vapor.

A split window algorithm was chosen in order to make the comparison with the Level2 product

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, Δε: 1.05 0.09 0.59 1.20

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

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

SW n, Quad(W), ε, Δε, 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, Δε: 0.69 0.13 0.26 0.74

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

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

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

σmod: residual atmospheric error.

σnoise: noise error: NEΔT=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 +++=

* * SensitivitySensitivity analysisanalysis fromfrom error error theorytheory ofof thethe algorithmsalgorithms

Spinning Enhanced Visible and Infrared Imager

• Geostationary sensor onboard the MSG-2 platform

• 11 Visible, NIR and TIR spectral bands of 3km pixel

• 1 High resolution pancromatic band of 1km pixel

• 1 global image every 15 min

2. LST Algorithms. SEVIRI2. LST Algorithms. SEVIRI

LST product isdistributed from the

Land SatelliteApplication Facilities

from EUMETSAT

A Split Window algorithm was developed by the GCU to retrieve LST from SEVIRI data.

M. Atitar, J.A. Sobrino, G. Soria, J.P. Wigneron, M. Romaguera, J.C. Jiménez-Muñoz, Y. Julien, A.B. Ruescas. Land surface temperature retrieved from SEVIRI / MSG2 data: algorithm and validation. Eumetsat Meteorological Satellite Conference 2008

Sobrino, J.A. and Romaguera, M. (2004). Land surface temperature retrieval from MSG1-SEVIRI data. Remote Sensing of Environment, 92

This algorithm has been used in thecomparison with theLand SAF product

2. LST 2. LST AlgorithmsAlgorithms. SEVIRI. SEVIRI

Moderate Resolution Imaging Spectroradiometer

• Polar sensor onboard the TERRA / AQUA platforms

• 36 Visible, NIR and TIR spectral bands

• 2 bands of 250m pixel, 5 of 500m pixel and 29 bands

of 1km pixel

MODIS products has been obtained from the

EOS DATA Gateway.

LST product can be retrieved from the

MOD11 product

2. LST 2. LST AlgorithmsAlgorithms. MODIS. MODIS

a SW algorithm was developed to retrieve LST from MODIS data. This algorithm has been used in the comparison with the MODIS EOS Data Gateway product.

More information can be found in:

Sobrino, J.A., El-Kharraz, J. and Li, Z.L. (2003). Surface temperature and water vapour retrieval from MODIS data. International Journal of Remote Sensing. 24

2. LST 2. LST AlgorithmsAlgorithms. MODIS. MODIS

LAND SURFACE EMISSIVITY FROM SATELLITE DATALAND SURFACE EMISSIVITY FROM SATELLITE DATA

2. LST 2. LST AlgorithmsAlgorithms

AATSR-SEVIRI: Split window covariance-variance ratio (SWCVR)

W = 0.26 - 14.253 cosθ lnR54 - 11.649 (cosθ lnR54)2

− −

( )( )

T T T T

4k 4o k ok

4k 4o2

θ : Ángulo de observación satélite.

error: 0.5 (g cm-2)

ESTIMATION OF WATER VAPOUR CONTENT FROM SATELLITE DATAESTIMATION OF WATER VAPOUR CONTENT FROM SATELLITE DATA

Ratio (C=L17/L2)band 17, absorbent (0.902-0.908) μmband 2, transparent 0.8610.861--0.869 0.869 μm

W= 26,3+-54,4 C+ 28,5 C2

MODIS/TERRA-

Antennas

NOAA-AHVRRTERRA/AQUA-MODIS MSG-SEVIRI

IPL-VALENCIA UNIVERSITY (Spain)

3. Comparison Results. Satellite data3. Comparison Results. Satellite data

• Simultaneous images were acquired of AATSR, SEVIRI and MODIS sensors over thestudy site of the CEFLES2 campaign.

•2 AATSR images analized correspond to a day scene of the 26th of July 2007 (10:38 UTC) and a night scene of the 27 of July 2007 (21:25 UTC). The more simultaneousSEVIRI and MODIS images has been acquired and procesed to obtain LST from thealgorithms proposed and also to get the LST products, respectively.

AATSR scene over Le Bray site AATSR RGB image 26th July 2007

AATSR LST image retrieved 26 july 2007 (10:38 UTC) with SW6 algorithm proposed

AATSR LST Level 2 product26 july 2007 (10:38 UTC)

286 290 294 298 302 306 310 314 318 286 290 294 298 302 306 310 314 318

Study area of Le Bray has not been processed in Level2 product, only Brightness temperature in 11μm band is available.

Another area, more homogeneous, has been used for intercomparison

Study Area: 44º 43’ 7” N, 0º 45’ 23” W

Study Area #2: 44º 20’ 26” N, 0º 41’ 16” W

AATSR LST image retrieved 27 july 2007 (21:25 UTC) with SW6 algorithm proposed

AATSR LST Level 2 product27 july 2007 (21:25 UTC)

286 290 294 298 302 306 310 314 318 286 290 294 298 302 306 310 314 318

MODIS LST image retrieved 26 july 2007 (10:38 UTC) with SW algorithm from GCU

MODIS LST product 26 july 2007 (10:38 UTC) from EOS Data Gateway

286 290 294 298 302 306 310 314 318 286 290 294 298 302 306 310 314 318

285 290 295 300 310 320 325 330 335

SEVIRI LST image retrieved withSW algorithm from GCU

SEVIRI LST products from LandSAF

26 july 2007 (10:38 UTC)

27 july 2007 (21:25 UTC)

Field mesurements of LST at nadir and 55ºon Le Bray Tower for 26th and 27th July 2007

3. Comparison Results. Field data3. Comparison Results. Field data

In Situ LST measured at nadir and 55 º

26/07/2007 0:00 26/07/2007 12:00 27/07/2007 0:00 27/07/2007 12:00 28/07/2007 0:00

Mean LST measured atsatellite overpass time (±15 min)

289.7 K290.2 K27 / 07 / 2007

21:25 UTC

303.9 K296.8 K26 / 07 / 2007

10:38 UTC

In Situ LST 55º

In Situ LST nadir

Day /Time

3. Comparison Results. 3. Comparison Results.

286 290 294 298 302 306 310 314 318

309.2302.9303.3

308.5300.1300.6

300.1299.2300.0

300.0298.9300.7

300.0300.9302.2

300.1300.1301.9

301.6302.0301.4

301.2301.3301.5

300.4300.5300.6

299.5299.6299.9

299.7299.9300.1

300.1300.2300.4

Area of 3x3 pixel size (1km pixel)

Mean value of the 3x3 pixel area

Area of 1 pixel size (3km pixel)

AATSR MODIS SEVIRI

302.7 300.5 301.2 299.9 302.5 301.7

LST (K) 27th (2nd area)

289.7-303.5In Situ

288.8298.3299.6EOS

product

289.2299.9301.2SW GCUMODIS

287.9300.7302.7LandSAFproduct

288.4301.7302.5SW GCUSEVIRI

Notprocessed302.9Not

processedLevel2

product

289.7300.5302.7SW GCUAATSR

LST (K) 27th (1st area)

LST (K) 26th (2nd area)

LST (K) 26th (1st area)

ProductSensor

AATSR SW – IN SITU

NotProcessed**0.92.33.0AATSR SW – MODIS EOS data

0.30.50.61.5AATSR SW – MODIS SW

0.21.8-0.20.0AATSR SW – SEVIRI LandSAF

0.41.3-1.20.2AATSR SW – SEVIRI SW

-1.0Not Processed

(2.5)*-2.4

Not Processed(4.9)*

AATSR SW – AATSR Level2

289.8289.7300.5302.7LST AATSR SW (K)

27 2nd area

271st area

26th 2nd area

26th1st area

*Difference with channel 11 brightness temperature

** MODIS (22:05h)

DAY NIGHT

4. Summary and Conclusions4. Summary and Conclusions

A set of Split window algorithms has been developed to retrieve LST from AATSR data

Satellite images from AATSR, SEVIRI and MODIS has been acquired during the CEFLES2 campaign, carried out in the Les Landes region of France. Split window algorithms developed for SEVIRI and MODIS by the Global Change Unit has been applied to the images acquired to retrieve LST

A comparison has been carried out between the SW algorithms proposed and the LST products for AATSR, SEVIRI and MODIS data distributed by ESA, LandSaf and EOS data Gateway, respectively.

The comparison shows:

-LST from AATSR SW algorithm proposed is generally higher-AATSR SW algorithm proposed has a good agreement with in situ data

-AATSR SW algorithm proposed has a good agreement with the SEVIRI SW and LandSAF and MODIS SW images.

-Less agreement has been observed for AATSR ESA product and MODIS EOS.

PROBLEMSSome problems remain in data quality

– Cloud flagging – well known problem for many sensors

– Continuity at biome boundaries– Resolution (0.5x0.5) not adequate for

heterogeneous surfaces (i, land cover, and f fractional vegetation cover) .

What about the dynamic of Land cover?Why not the same that the AATSR spatial

resolution (1 km x 1km) ?

– Limitations of scale of auxiliary data– LST product only nadir view.

TOWARDS SOME IMPROVEMENTS...but less

operational?• To optimise (i and f) the AATSR LST PRODUCT

for each test site (Methodologies for heterogeneous areas)• To estimate W from AATSR data (SWCVR? Others?)• To evaluate emissivity from AATSR data (from NDVI, from dynamic land cover maps?)• THE AATSR NOVELTY IS THE BIANGULAR. WHY

NOT TO EXPLOIT THIS AND To develop dual angle algorithms (better LST retrievals) for homogeneous areas.

(field measurements of emissivity angular variation)

Is time to know the interest of this?

( ) ( ) ( )[ ] ↑↓ +−+= iisisensori LLTBTB τεε 1

Atmospheric effectsBT measured by the sensor

Surface emissivity

LST Product Algorithm (1)

• The atmosphere and surface emissivity are temporally and spatially heterogeneous over land

• The LST product uses global ancillary data (some of which are seasonally dependent) to derive the atmospheric correction

• Brightness Temperatures from multiple channels are used to improve the atmospheric correction (split-window)

( ) ( ) 12,,1211,,, TcbTTbaLST ififn

ifpwif ++−+=• Regression-based split-window algorithm (nadir-only):

• Where:• T11 and T12 are nadir 11 and 12 μm channel brightness

temperatures (BT)• a, b, c – retrieval coefficients from Prata (2002) that

depend on:– Surface/vegetation type (i)– Vegetation fraction (f) – seasonally dependent– Precipitable water (pw) – seasonally dependent– Satellite zenith view angle (n(θ))– (Time of day)

• Coefficients are on 0.5° grids

LST Product Algorithm (2)

Surface/Vegetation Type

• 13 biomes and 1 lake class

• 0.5° resolution grid

• Based on the Dorman & Sellers (1989) classification

Fractional Vegetation

• Values between 0 and 1

• 0.5° resolution grid (monthly intervals)

• Based on monthly vegetation fraction from Dorman and Sellers (1989) and ISLSCP NDVI global composite

Precipitable Water

• Corrects for increased atmospheric absorption with viewing angle (i.e. for true nadir this has no effect)

• 0.5° resolution grid (monthly intervals)

• Based on NASA Water Vapour Climatology (NVAP)

J. A. Sobrino, G. Sòria, M. Atitar, A. B. Ruescas, J. C...

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