MERIS, A-MODIS, SeaWiFS, AATSR and PARASOL over the Salar de Uyuni March 2006 MAVT 2006 Marc Bouvet, ESA/ESTEC

Transcription

1 MERIS, A-MODIS, SeaWiFS, AATSR and PARASOL over the Salar de Uyuni

2 Plan of the presentation 1. Introduction : from absolute vicarious calibration to radiometric intercomparison 2. Intercomparison at TOA level : finding reciprocal and identical doublets a) The Salar de Uyuni b) The satellite data c) Results d) Sensitivity analysis e) Conclusion 3. Further work 4. Conclusions 5. Acknowledgments

3 Introduction : from absolute vicarious calibration to intercomparison Absolute calibration In-situ surface data In-situ atmosphere data RT Simulated Simulated TOA Simulated reflectance TOA reflectance TOA reflectance =? Calibration coefficients Satellite TOA reflectance Satellite TOA reflectance Satellite TOA reflectance

4 Introduction : from absolute vicarious calibration to intercomparison The in-situ data to reach highest accuracy Atmosphere Aerosols: scattering phase function, single scattering albedo and optical thickness for all wavelength along a vertical profile Gaseous absorbers: vertical density profiles, temperature and pressure profiles Gaseous diffusers : pressure for Rayleigh Surface Ground reflectance: spectral and angular measurements with adequate spatial sampling

5 Introduction : from absolute vicarious calibration to intercomparison Q: What can you do when you have neither surface nor atmospheric measurements?

6 ρ Reciprocal and identical geometries One can use both identical and reciprocal geometries and assume symmetry wrt to the principal plane to find doublets directly comparable at TOA level: TOA TOA TOA ( θ θ, φ) = ρ ( θ, θ, φ) & ( ) TOA ρ θ θ, φ = ρ ( θ, θ, φ ) sun, sat sat sun sun, sat sun sat θ sat θ sun φ

7 ρ ( θ θ, φ) = ρ( θ, θ, φ) sun, sat sat sun Reciprocal and identical geometries One can use both identical and reciprocal geometries and assume symmetry wrt to the principal plane to find doublets directly comparable at TOA level: & ρ ( θ θ, φ) = ρ( θ, θ, φ) sun, sat sun sat θ sat θ sun φ

8 Reciprocal and identical geometries To identify doublets: χ = [ (SZA(i)- SZA(j)) 2 + (VZA(i)- VZA(j)) 2 + 1/4x(abs(RAA(i))- abs( RAA(j))) 2 ] Criteria: χ < 10 => on average differences smaller than 5 degrees between SZA, VZA angles and 10 degrees in RAA Reciprocal and identical doublets are kept if from the same day or differing by one day

9 The site : The Salar de Uyuni (Bolivia)

10 The ideal terrestrial target for imaging spectrometers intercomparison in the VIS/NIR is: Looking for a good site A surface with a spectrally white reflectance to minimize the radiometric differences due to Relative Spectral Responses Located at high altitude to reduce the contribution of the atmospheric signal (not white ) Spatially homogeneous to reduce the errors due to geolocation errors on the TOA signal As Lambertian as possible to reduce the angular variations of the TOA signal Large (at least 30 km x 30 km) to reduce environment effects Flat : to avoid shadowing effects affecting the reflective properties

11 The site : The Salar de Uyuni The ideal terrestrial target for imaging spectrometers intercomparison in the VIS/NIR?: A surface with a spectrally white reflectance to minimize the radiometric differences due to RSR Mean ground reflectance spectrum measured with the spectroradiometer LICOR LI-1800 on June 8 and June 9, 1999 by Lamparelli et al [2003]

12 The ideal terrestrial target for imaging spectrometers intercomparison in the VIS/NIR?: The site : The Salar de Uyuni Located at high altitude to reduce the atmospheric signal: 3600 m Rayleigh scattering : P 650 hp => τ Rayleigh reduce by 1/3rd Dry atmosphere : reduce water vapour absorption??? Ozone variability is low at low latitudes => climatology should be OK Aerosol load should also be reduced???

13 The ideal terrestrial target for imaging spectrometers intercomparison in the VIS/NIR?: The site : The Salar de Uyuni Lambertian or with a well characterized BRDF to minimize the difference due to illumination and observation geometry: salt crust should have nearly such behaviour??? ( Question for PARASOL?) Evaporation patterns

14 The site : The Salar de Uyuni The ideal terrestrial target for imaging spectrometers intercomparison in the VIS/NIR?: Spatially homogeneous to reduce the errors due to geolocation errors on the TOA signal => the standard deviation over the whole surface is about 5 % in all wavelengths at TOA (when not partially flooded) Flat : to avoid shadowing effects affecting the reflective properties Flat within few meters

15 The ideal terrestrial target for imaging spectrometers intercomparison in the VIS/NIR?: The site : The Salar de Uyuni Large (at least 30 km x 30 km) to reduce the adjacency effects due to neighbouring surfaces with differing spectral signature 200 km longitude wise and a bit less latitude wise

16 The site : The Salar de Uyuni The Region Of Interest (ROI) where L1B data are systematically extracted and averaged is 20x60 km, i.e., about 800 x 1.2 kmresolution pixels ROI chosen to be distant from occasionally flooded areas (January and February mainly)

17 The satellite data Medium resolution data: MERIS 1.2 km RR A-MODIS 1km aggregated data SeaWiFS 1.2 km LAC AATSR 1 km data PARASOL 6km data Temporal coverage Early 2002 to late 2005 Data extracted: L1B TOA radiances

18 The satellite data: MERIS L1B data from MERCI 2002 : MEGS PC : MEGS PC : MERIS MEGS PC 7.4 (in line at L1B level?) 2005 : MEGS PC 7.4 Pre-processing: Cloud screening Smile correction at irradiance level (no reflectance correction) Thuillier 2003 irradiance spectrum for radiance to reflectance conversion Lat/long correction Calibration: Onboard solar diffusers

19 The satellite data: A-MODIS L1B data from the GES Distributed Active Archive Center 2002 to 2005: reprocessing 1.1 Pre-processing: Cloud screening Calibration: Onboard solar diffusers!!!! Over ocean, the calibration is based on MOBY => results here presented do not apply to OC L2 products. The results hereafter presented apply to land only!!!

20 L1A data the Ocean Color Website 2002 to 2005: reprocessing : GAC data The satellite data: SeaWiFS Pre-processing: Cloud screening L1A -> L1B processing with SeaDAS 4.7 (v4.8 for 2005 data) Thuillier 2003 irradiance spectrum for radiance to reflectance conversion Calibration: Onboard solar diffusers and (moon observation)!!!! Over ocean, at L2 level, the calibration is based on MOBY results here presented do not apply to OC L2 products The results hereafter presented apply to land only!!!

21 L1B data: Processor version to not yet completely integrated The satellite data: AATSR Pre-processing: Cloud screening Drift correction implemented until 30 Nov 2005 Calibration: Onboard solar diffusers Drift computed by D. Smith from test sites.

22 The satellite data: PARASOL L1B data with radiometric (PER V2.00) and geometric (PEG V2.01) calibrations: 2005 Pre-processing: Cloud screening Correction of viewing angles for filter rotation time not implemented (impact on VZA small?) Calibration: NO onboard solar diffusers => vicarious calibration Degradation model

23 1 The satellite data The Relative Spectral Response of MERIS, A-MODIS, SeaWiFS, AATSR (no PARASOL yet) TOA reflectance O Wavelength in nm O3 H20 MODTRAN reflectance spectrum, 3600 meters of altitude, Rs=0.8, US 1976 standard atmosphere, in aerosol free conditions, with a SZA of 50 degrees and a VZA=0

24 865 nm Intercomparison of TOA reflectances MERIS A-MODIS SeaWiFS AATSR PARASOL Rather scattered reflectance time series No SeaWiFS data in 2005 The times series are quite distinct but systematic differences in viewing / illumination geometries could explain this

25 Reciprocal and identical geometries: the viewing geometries Viewing and illumination geometries of the dataset MERIS A-MODIS SeaWiFS Reciprocal & identical Few reciprocal / identical doublets because the viewing and illumination geometries are quite different for all sensors PARASOL geometries should be key in this approach For these doublets, the TOA signal should be directly comparable!!!

26 Intercomparison of TOA reflectances: reciprocal and identical MERIS A-MODIS SeaWiFS AATSR PARASOL There should be enough doublets to enable drift retrieval

27 MERIS A-MODIS Excellent agreement between the two sensors!!! No drifting nor bias between these two onboard calibrated sensors

28 MERIS AATSR Good agreement between the two sensors Yet AATSR or MERIS seem to be drifting slowly AATSR drift could be slightly underestimated 560 nm in 2005? (to be confirmed)

29 AATSR vs. MERIS & MODIS 865 nm MERIS AATSR nm MODIS AATSR 560 nm MERIS AATSR 560 nm MODIS AATSR 560 nm in 2005? (to be confirmed)

30 A- MODIS SeaWiFS SeaWiFS is bellow all other sensors. Strange bump in 2004 could be due to scarcity of data and jumps in surface reflectance in this period!!! No altitude correction in geolocation But these are the land data => calibration gains are different over water

31 A-MODIS PARASOL Drift wrt MODIS? Initial sensor degradation? Only 1 year of data so far

32 AATSR PARASOL AATSR 560 nm and PARASOL following a similar trend

33 MERIS PARASOL 443 NP 490 NP NP 670 P!!! 443 NP should not be used : straylight correction pb

34 MERIS PARASOL 865 P Good agreement between the two sensors Only few points so far. Longer time series are needed

35 SeaWiFS PARASOL 443NP 490 NP Dubious:My mistake??? 565 NP 670 P

36 SeaWiFS PARASOL 865NP Degradation wrt to SeaWiFS observed in all bands but in 490 nm

37 Systematic and random errors: the doublets What are the factor which could explain the systematic differences between doublets reflectances: Relative Spectral Response (RSR) Systematic geolocation errors Calibration!!! What are the factor which could explain the random noise of each sensor time series : Geolocation random errors A too loose χ used for reciprocal and identical doublets identification Instrument instabilities

38 TOA reflectance 1 0 Sensitivity analysis : the doublets and the RSR Simulated TOA spectrum Wavelength in nm In band reflectance Reflectances in each band Band AATSR MERIS MODIS SeaWiFS 412 nm nm nm nm nm nm nm For a spectrally white surface, systematic differences at TOA level are of maximum 0.5 % (except for band 670 nm of AATSR where it goes up to -1 %) Additional surface spectral variations: About % for 10 nm shift (worst case) between two bands wavelength centers at surface reflectance level Lamparelli et al [2003]

39 Sensitivity analysis : geolocation Most sensor geolocation accuracy specification is bellow 2 km. What happen if one move the ROI by 2km North and 2 km West on a MERIS scene? TOA difference in % after shifting ROI MERIS band number The error remains within 0.3 % for all wavelengths in this worst case situation because the ROI is 20 km x 60 km

40 Sensitivity analysis : geolocation TEST geoloc accuracy: jpeg showing the ROI and were systematically produced and looked at => qualitative inspection AATSR Nadir view at 865 nm with ROI in white MERIS, PARASOL, AATSR, A-MODIS : indicate a similar ROI which is consistent in time SeaWiFS : is there a correction for altitude in the geolocation? => TB investigated

41 Sensitivity analysis : geolocation SeaWiFS at 865 nm for two different dates and nearly nadir vs. large viewing angle: Day X : Near nadir Day Y : edge of field of view SeaWiFS : is there a correction for altitude in the geolocation? => TB investigated

42 Systematic differences and random noise: the reciprocal and identical doublets What are the factor which could explain the systematic differences between doublets : RSR < 1 % Systematic geolocation errors : < 1 % (more for SeaWiFS?) Calibration differences should be identifiable as soon as they are larger than approximately 2 %!!! What are the factors which could explain the random noise on each sensor time series : Geolocation random errors The too loose χ used for reciprocal and identical doublets identification Instrument instabilities

43 Systematic differences of calibration are identified by a simple methodology at TOA level Conclusion MERIS = A-MODIS PARASOL: First year degradation observed (more data needed). 490 nm? (my mistake?) AATSR Drift appears to be SLIGHTLY underestimated for this peculiar surface type! => more to be understood. 560 nm? SeaWiFS way bellow all previously mentioned sensors (about 10 %). Altitude correction? Over water it s a different story.

44 Olga (from Brockmann Consult) for helping me getting the MERIS data timely and diligently CNES team for useful info on PARASOL Dave for info on AATSR Thanks to Alessandra and Laurent for providing information and pictures over the Salar de Uyuni

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