Status of CNES Cal/Val Activities

Transcription

1 GSICS Executive Panel Meeting 16-17th May 2014, Guangzhou, China Status of CNES Cal/Val Activities Patrice Henry CNES 1

2 Summary Overview of CNES Cal/Val Activities Summary Re-Calibration Activities POLDER(s)/PARASOL reprocessing VGT1&2 reprocessing (SPOT World Heritage) Support to MERIS Towards a lunar calibration improvement Use of Pleiades lunar acquisitions Monitoring of IASI/AIRS/CRIS inter-calibration 2

3 CNES Cal/Val activities Calibration monitoring of CNES missions in operation phase Pléiades 1A&1B, IASI A&B, Saphir & Scarab (Megha-Tropiques), IIR/Calipso Reprocessing and sustainable archiving of the past CNES missions POLDER/PARASOL Intensive recalibration program and complete reprocessing in 2014 (level 1 to 3) VGT Enhanced calibration program and VGT1&2 reprocessing in 2014/2015 SPOT(s) SPOT World Heritage project 3 Participation in the calibration of partners missions MERIS Calibration check using the various CNES tools SPOT 6&7 Calibration of the Astrium new SPOT satellites Sentinel2 Calibration and performances assessment for the in-flight commissioning phase Sentinel3 Participation in the OLCI and SLSTR in orbit calibration monitoring

4 4 Overview of PARASOL Recalibration (achieved reprocessing in progress)

5 Applying Operational Calibration Methods Desert Sites (PICS) o20 stable sites Africa/Arabia o1 sensor as ref. = cross-calibration o1 date as ref. = multitemporal ogeometrical matching (same geometry) + spectral interpolation oall reflective bands to 2200nm [Henry et al., IEEE, 2013; Lachérade et al., IEEE, 2013] Rayleigh Scattering o6 main oceanic sites oselection of very non-turbid situations orayleigh contribution about 90% omarine contribution, aerosol background ovis bands to 670nm [Hagolle et al., IEEE, 1999; Fougnie et al., OO, 2002] Clouds (DCC) oselection of white DCC brightness, high altitude, homogeneity o1 band as reference = interband ocot estimated using reference band ospectral correction assuming a CPT ovis-nir bands to 865nm Sunglint [Lafrance et al., IEEE, 2002; Fougnie et al., IEEE, 2009] o6 main oceanic sites oselection of very non-turbid situations o1 band as reference = interband osunglint estimated using reference band omarine contribution, aerosol background oall reflective bands to 2200nm [Hagolle et al., IEEE, 2004] Antarctica 5

6 Multi-Methods Synergic Approach Combining the capability of each method Rayleigh Scattering Desert sites Snow (Dome C) Clouds (DCC) Sunglint Method Absolute cal. Interband cal. Field-of-view cal. Temporal monitoring Cross-calibration Polarisation Characterization 6

7 Temporal Evolution: Model Adjustement B490 = 0.16 D=0.018 B565 = 0.11 B670 = B865 = B1020 = A( t) = A( to) [ 1 B (1 exp( D t)) ] B765 =

8 PARASOL in the Field of View Calibration Calibration for the in field-of-view evolution DCC calibration suppose the reference band is stable (765nm) Desert (reference = POLDER1) suggest it is not the case Rayleigh (absolute reference) confirm that for 75% of the coverage sufficient to generalize Confirmed also for most of other bands Band 490nm Calibration result versus pixel on the CCD matrix The black hole from band 765nm Interband over DCC (ref=765) Intercalibration over desert (ref=pol1) Absolute calibration over Rayleigh 8 Black hole confirmed by Rayleigh Instrument-765 Bright banner not confirmed method artefact

9 Blue and green bands Relative calibration estimation 9

10 Red and PIR bands Relative calibration estimation 10

11 Validation DES : RAY : DCC : Nov nm band Scale: [+/-20%] Average along columns Average along lines Average along columns Average along lines D E S R A Y D C C 11 L1 before L1 corrected

12 Impact on level 2 products Aerosol Optical Depth over ocean» Validation against aeronet 12

13 13 Outlines of VGT Recalibration (work in process)

14 Intensive Averaging of VGT Data 2 sets of lines (averaging raw products) PIR Band 14

15 Pixel to Pixel Relative Calibration Hanning filtering (to remove LF) PIR Band 15

16 Validation of the Estimated Coefficients PIR Band 16

17 Relative calibration coefficients comparison In orbit(2011) Pre flight (2002) 17 PIR (B3) BAND

18 Low Freq. Relative Calibration Evolution As seen by the on-board lamp: R lamp (t)/r lamp (2002) Blue Band 18 Detector #

19 Low Freq. Relative Calibration Evolution As seen by Rayleigh Scatering Calibration Blue Band 19

20 Low Freq. Relative Calibration Evolution Rayleigh Scatering Calibration: Average 10 boxes Blue Band 20

21 Low Freq. Relative Calibration Evolution As seen by calibration using DCC (relative cal. vs red band) Blue Band 21

22 Low Freq. Relative Calibration Evolution Calibration using DCC: Average 10 boxes Blue Band 22

23 23 SPOT Archive Reprocessing

24 SPOT WORLD HERITAGE CNES s SPOT World Heritage programme to provide free SPOT satellite archive imagery for non-commercial uses Officially announced at the plenary session of the Group on Earth Observations (GEO) in Geneva, on January 17, 2014 First goal: to process 400,000 multispectral images in 4 years A first batch of 100,000 images will be made available at the end of this year Recalibration of SPOT1 to 5 (multitemporal consistency) Processing: Level 1C orthorectified TOA reflectance Complete coverage of France plus other areas to be defined 24

25 EXAMPLE OF SWH PRODUCTS Level 3A algorithm validation over the Versailles area (Feb. 2013) using SPOT4 Take 5 images 25

26 26 Validation of MERIS Calibration

27 Radiometric stability or Trending Using the cross-calibration method over desert sites (according Lachérade et al., 2013)» Perfect long-term stability» Seasonal variations are due to periodical changes in the geometrical sampling Cross-calibration with PARASOL - Time series (Bands are shifted by 0,05 steps for clarity) 27

28 Trending - interband Interband stability over DCC Matchups are normalized to a reference band 620nm (DES) and 680nm (DCC) reference VIS reference D DCC look similar E to a white D diffuser plate S C E at the top of the atmosphere! C R T reference NIR 28

29 29 Improvement of Lunar Calibration with PLEIADES

30 The PLEIADES moon acquisitions 166 images acquired by PLEIADES-1A since its launch (12/2011) 970 images acquired by PLEIADES-1B since its launch (12/2012) Phase ±40 New Experiments 30 Lunar cycles

31 Stability monitoring PHR-1A PHR-1B 31 Selection of the lunar acquisitions within a phase of 40 (±1 ) Normalized by the ROLO albedo (version 311g + corrected by Apollo Samples) The lunar calibration method shows similar answers with very low dispersion for the two instruments.

32 The PLEIADES radiometric calibration Calibration Results (Moon, Desert, Antarctica) 32 -> Good agreement between the calibration methods -> Retrieve the observed biais between MERIS and MODIS (Lachérade et al., IEEE, 2013)

33 The PLEIADES-1B lunar acquisitions vs phase angle -> Signature of the results with the phase (up to 6%) -> Important difference (up to 14%) in absolute with ROLO 33

34 Impact of the orientation of the Moon Acquisition of an image of the Moon every minute with a rotation of the yaw angle of 45 between each acquisition - Variation of the phase up to 1.5 between the first and last image 13 experiments realised on different phase angles of the Moon 34

35 Impact of the orientation of the Moon Results obtained for the red band of PHR-1B Effect from 0.2% to 0.6% 35 The behaviour of the results (shape and level) is quite similar whatever the phase: the variation of the solid angle is well taken into account Extreme values (min and max) are obtained near the full Moon conditions

36 Conclusion for lunar calibration The PLEIADES dataset is based on - High spatial resolution images (one Moon: more than 4 millions of pixel!) - High frequency revisit (lunar cycle acquisitions within 1 image every 1h40) -> gives a phase coverage from -115 to High agility level rotation of the satellite to acquire the Moon every minute -> This dataset allows us to quantify very precisely the uncertainties linked to this dataset and to the method (including ROLO) 36

37 37 IASI/AIRS/CRIS inter-calibration monitoring

38 Methodology for IASI / AIRS, IASI / CRIS Similar scenes: SNOs (Simultaneous Nadir Overpasses) Tolerance in simultaneity : 20 min ~30 scenes every 3 days for IASI / AIRS (12000 in 5 years) Always at high latitudes AIRS IASI IASI & AIRS pixel central positions Spatial match: Regional averaging of the soundings pixels over a 300km*300km area around the orbit crossing point Spectral match: Construction of 33 broad pseudo-bands Each PB = intelligent averaging of ~100 elementary channels to get the similarity of the PB spectral functions The AIRS missing channels and varying spectral resolution are considered when calculating the IASI coefficients NB: the convolution of IASI by the CRIS or AIRS SRFs has been performed but is still under exploitation For each pseudo-band, T = ( L L ) IASI AIRS L T σ ( σ,280k) 38

39 IASI-A / CRIS & IASI-B / CRIS inter-comparison Biases and standard deviations (no filtering) Biases < ~0.2K Very well cross calibrated Same shape, highest bias in B1, stronger for IASI-B. Spectral slope? Similar datasets: 39

40 IASI-A / AIRS & IASI-B / AIRS inter-comparison Biases and standard deviations (no filtering) Biases < ~0.2K Very well cross calibrated Same shape, highest bias in B1, stronger for IASI-B. Atmospheric shape? Similar datasets: 40

41 Temporal Evolution of IASI/AIRS and IASI/CRIS B1 B2 B3 IASI-A / CRIS IASI-B / CRIS IASI-A / AIRS IASI-B / AIRS 41 All couples are very stable with time

42 IASI-A / IASI-B through AIRS and CRIS Combination of IASI / AIRS and IASI / CRIS for IASI-B / IASI-A IASI-B / CRIS IASI-A / CRIS IASI-B / AIRS IASI-A / AIRS All biases agree: ~0.1K Confirms the very good cross calibration Always an effect on B1, IASI origin? Small differences in B1: dataset selection, e.g. colder? Direct IASI-B / IASI-A 42

43 Status of IASI inter-calibration The tool for inter-comparison is operational for the 5 couples of sensors: IASI-A / IASI-B, IASI-A / AIRS, IASI-B / AIRS, IASI-A / CRIS, IASI-B / CRIS Major results: very accurate cross-calibration! IASI-B very close to IASI-A (bias < ~0.1K) continuity of the IASI mission IASI / AIRS / CRIS: Bias between 0K and 0.2K, < radiometric absolute specification of 0.5K Cal/val results are confirmed with a larger and more diverse dataset All are very stable with time Largest bias in IASI B1, stronger in IASI-B: non-linearity in IASI? The observed bias is still high for climatic time series On-going work: Go further in the interpretation of the shape of the bias curves: Specific sensitivity studies for B1 are scheduled Update of the linearity tables Get the bias curves for each IASI pixel Exploit the IASI / AIRS and IASI / CRIS at high spectral resolution Uncertainty complete budget Go further in an absolute radiometric and spectral calibration 43

44 44 Thank you for your attention!