Guillem Sòria, José A. Sobrino, Juan C. Jiménez-Muñoz, Mónica Gómez, Juan Cuenca, Mireia Romaguera and Malena Zaragoza

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1 Guillem Sòria, José A. Sobrino, Juan C. Jiménez-Muñoz, Mónica Gómez, Juan Cuenca, Mireia Romaguera and Malena Zaragoza University of Valencia, Spain MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

2 Index 1. AATSR LST algorithms proposed a) Methods of atmospheric correction b) Coefficients calculation c) Split-window and Dual-Angle method 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 MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

3 AATSR Characteristics 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 MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

4 τ iθ 1. AATSR algorithms proposed a) Methods of atmospheric correction Split-window method: The SW method uses observations at two different wavelengths with the T s same observation angle. = T i + Dual-angle angle method: A( T T ) B B εb i j 0 + (1 ε ) 1 2 ε = ε i + ε j 2 ε = ε i ε j The DA method uses observations at two different observation angles T s within the same wavelength interval = T + A( T T ) B ε B ε B ε θ = ε n ε f n n f 0 + (1 n) 1 θ 2 MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

5 τ iθ 1. AATSR algorithms proposed b) Coefficients calculation The algorithms coefficients were obtained from simulated data to apply them to a large amount of surfaces. transmissivity τ i θ From MODTRAN simulation code: upwelling and downwelling atmospheric radiance water vapor content atm Ri θ W atm Ri θ From laboratory spectral library, a set of emissivity values of: 11µm and 12 µm channels, nadir and forward views. Vegetation: Grass, Conifers, Decidious Bare soils, rocks Water MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

6 1. AATSR algorithms proposed c) Split-window and Dual-Angle algorithms Algorithm Expression SW n, Quad: T s = T 2n (T 2n -T 1n ) (T 2n -T 1n ) SW n, Quad, ε: T s = T 2n (T 2n -T 1n ) (T 2n -T 1n ) (1-ε) SW n, Quad, ε, ε: T s = T 2n (T 2n -T 1n ) (T 2n -T 1n ) (1-ε) ε SW n (W), ε, ε, W: T s = T 2n + ( W)(T 2n -T 1n ) + ( W) + ( W)(1-ε) - ( W) ε SW n, Quad, ε, ε, W: T s = T 2n (T 2n -T 1n ) (T 2n -T 1n ) 2 ( W) + ( W)(1-ε) - ( W) ε SW n, Quad(W), ε, ε, W: T s = T 2n + ( W)(T 2n -T 1n ) - ( W)(T 2n -T 1n ) 2 + ( W) + ( W)(1-ε) - ( W) ε DA 11 Quad: T s = T 2n (T 2n -T 2f ) (T 2n -T 2f ) DA 11 Quad, ε: T s = T 2n (T 2n -T 2f ) (T 2n -T 2f ) (1-ε 2n ) DA 11 Quad, ε, ε: T s = T 2n (T 2n -T 2f ) (T 2n -T 2f ) (1-ε 2n ) 25.8 εθ DA 11 W, ε, ε, W: T s = T 2n + ( W)(T 2n -T 2f ) + ( W) + ( W)(1-ε 2n ) - ( W) εθ DA 11 Quad, ε, ε, W: T s = T 2n (T 2n -T 2f ) (T 2n -T 2f ) 2 ( W) + ( W)(1-ε 2n ) - ( W) εθ DA 11 Quad(W), ε, ε, W: T s = T 2n + ( W)(T 2n -T 2f ) - ( W)(T 2n -T 2f ) 2 - ( W) + ( W)(1-ε 2n ) - ( W) εθ MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

7 1. AATSR algorithms proposed c) Sensitivity analysis from error theory of the algorithms Algorithm σ mod (K) σ noise (K) σ ε (K) σ WV (K) σ total (K) SW n, Quad: SW n, Quad, ε: SW n, Quad, ε, ε: SW n (W), ε, ε, W: SW n, Quad, ε, ε, W: SW n, Quad(W), ε, ε, W: DA 11 Quad: DA 11 Quad, ε: σ mod : residual atmospheric error. σ noise : noise error: NE T=0.05 K. σ ε : emissivity error: ε(ε)= σ WV : water vapor column error: ε(wv)= 0.5 gcm -2. DA 11 Quad, ε, ε: DA 11 W, ε, ε, W: DA 11 Quad, ε, ε, W: total { σ 2 + σ 2 + σ 2 σ 2 } σ = ε + mod noise WV DA 11 Quad(W), ε, ε, W: MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

8 2. Validation over an homogeneous area Data provided by A. Prata,, CSIRO, Australia Algorithm σ teoric (K) σ validació (K) Bias (K) RMSD (K) SW n, Quad: SW n, Quad, ε: SW n, Quad, ε, ε: SW n (W), ε, ε, W: SW n, Quad, ε, ε, W: SW n, Quad(W), ε, ε, W: DA Quad: DA Quad, ε: DA Quad, ε, ε: DA W, ε, ε, W: DA Quad, ε, ε, W: DA Quad(W), ε, ε, W: MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

9 3. Study over heterogeneous areas Marrakech, Morocco Barrax, Albacete, Spain 5 march july 2004 MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

10 3. Study over heterogeneous areas a) Marrakech campaign Marrakech field experiment took place in an area of the water-catchment of the Tensift river in march of (31º40 N, 07º35 W, 600 m elevation) MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

11 3. Study over heterogeneous areas a) Marrakech campaign Bare Soil field Mixed Site Vegetation + Bare Soil Vegetated field MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

12 3. Study over heterogeneous areas b) Barrax campaign ( 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. MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

13 3. Study over heterogeneous areas b) Barrax campaign Chris / Proba image acquired during the campaign, near to the AATSR overpass to avoid changes in crop growth. Thermal measurements in: Alfalfa Corn Green grass Wheat Bare Soil MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

14 3. Study over heterogeneous areas c) Classification process The Chris/Proba pixels can be identified according to their NDVI values. Nadir view NDVI 0 Crops Bare soil 0.3 Cut Wheat 0.5 Green grass Corn 0.9 Alfalfa MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

15 Classification process The Chris/Proba pixels can be identified according to their NDVI values. NDVI Crops Forward view ( 55 degrees) 0 Bare soil 0.3 Cut Wheat 0.5 Green grass Corn 0.9 Alfalfa MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

16 Classification process Measurement strategy: pixels classified into 3 classes. Bare Soil Cut wheat Alfalfa NDVI MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

17 Classification process over a Chris/Proba image Supervised - Maximum Likelihood classification Grid of AATSR pixels Bare Soil Cut wheat Alfalfa MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

18 Classification process over a Chris/Proba image Supervised - Maximum Likelihood classification Proportion of Crops in every AATSR pixel Class % % % % Class Crop 1 Bare Soil 2 CutWheat 3 Alfalfa Class % % % % Class % % % % Class % % 2 7.4% % Effective LST and LSE of each pixel From in-situ temperature and emissivity of each crop, it is possible to obtain LST and LSE for each pixel to validate AATSR data. MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

19 Validation of LST from algorithms and insitu data TheAATSR algorithmsneedthefollowingdata: 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) Radiometric Temperature 11µm forward (K) Radiometric Temperature 12µm nadir (K) W (g/cm2) Emissivity nadir (11µm) Emissivity nadir (12µm) Emissivity forward (11µm) MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

20 Validation of LST from algorithms and insitu data Difference between LST from algorithm and LST measured in situ. Higher errors are observed in the DA algorithms. LST difference Pixel #1 Difference LSTalgorithm LST insitu 4.00 Pixel 1 Pixel 2 Pixel 3 Pixel LST difference Pixel #2 LST difference SW algorithms 2 3 Algorithm DA algorithms 4 Pixel # DA SW 4 Algorithm Pixel # LST difference SW DA Algorithm DA 3 SW Algorithm SW DA MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

21 Classification process over a Landsat5/TM image Supervised - Maximum Likelihood classification Specific classes selected: Bare Soil Mixed Vegetated MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

22 Validation of LST from algorithms and insitu data Marrakech Field Campaign Barrax Field Campaign Difference LSTalgorithm LST insitu Bias, K σ, K rmse, K Difference LSTalgorithm LST insitu Bias, K σ, K rmse, K SW algorithms SW algorithms DA algorithms DA algorithms Averaged values from a 4x4 grid of AATSR pixels MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

23 Rescalling of forward AATSR pixels The higher uncertainties observed in the evaluation of the DA algorithms are 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 1km Forward pixels: 1.5km x 2km These pixels are regridded onto a 1x1km grid. A process of pixel make-up is carried out. MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

24 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 MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

25 MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26

26 Tnadir - Tforward (ºC) Tnadir - Tforward (ºC) NDVI nadir -3 NDVI nadir - NDVI forward MERIS (A)ATSR WORKSHOP ESRIN, Frascati, Italy September of 26