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CNES Activities in the Framework of GSICS. Patrice Henry, Denis Blumstein, Denis Jouglet - CNES Thomas Colin - CS. Intercalibration AIRS/IASI SNO events (high latitude only) operational in the IASI TEC activated on a regular basis 3 months
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CNES Activitiesin the Framework of GSICS Patrice Henry, Denis Blumstein, Denis Jouglet - CNESThomas Colin - CS
Intercalibration AIRS/IASI • SNO events (high latitude only) • operational in the IASI TEC • activated on a regular basis • 3 months • Updated to handle IASI L1C Day-2 products (from May 2010) AIRS/IASI Intercalibration — sample of results
MetopA/IASI-A IASI-A IASI-B MetopB/IASI-B ~39° 16km common zone 10km common view by IASI-A / IASI-B IASI-A / IASI-B Intercalibration — Cal/Val preparation • Metop-A / B are on the same trajectory (180 deg apart) • Overlap between the swath of the 2 IASI instruments • Observation by 2 IASI of a same region on ground possible • 50 min between overflight of a same point • At all latitudes • Use of common zone where Sat Viewing Angle are “equal” • We limit ourself to 4 IASI pixels width • Satellite Viewing Angle between 0 deg (high latitude) and 39 deg (equator) • Limitation to uniform and stable (in time) geophysical situation
Sites selected in 2009 by B.J. Sohn using MODIS data • Simpson desert (Australia) – 50x50 km2, centered at 26.075S, 137.175E • Tengger desert (China) – 17x17 km2, centered at 38.125N, 103.0E • CNES studies • Extraction of POLDER/PARASOL and SPOT5/VGT2 images over a 2 year period (2007-08) • Data processing (cloud screening…) and insertion in the SADE data base • Sites analysis using ‘standard’ CNES tools • Spatial, spectral, temporal and directional behaviour • PARASOL and VGT2 cross calibration • Results comparison with 3 African desert sites : Algeria 3, Libya 1 and Libya 3 • For the 2 sites : less suitable characteristics for calibration than African sites • Tengger • Very small site and not so homogeneous • Calibration standard deviation much higher than for other sites • No winter calibration opportunity (potential snow coverage) and poor results for sensors cross calibration • Simpson • Lightly less homogeneous than African sites • Poor temporal stability : bad results for multidate calibration • No SADE extension with other PARASOL and VGT2 data but MERIS data will be added Study of Asian and Australian Desert Sites forSensor cross-Calibration in the VPIR Range
Sites temporal behaviour Simpson Tengger Algeria 3 Libya 1 Libya 3 PARASOL TOA reflectance normalized by the red reflectance Spectral dependance of seasonal effect on spectral range for Tengger(vegetation ?)
VGT2 calibration versus Parasol Mean VGT2/PARASOL calibration results Standard deviation of VGT2/PARASOL calibration results • Good consistency for the red range • Simpson : 3% higher in the blue, 3% lower in the NIR • Tengger : 6% higher in the blue (very high s…)
Multitemporal calibration PARASOL 2008 calibration versus PARASOL 2007 VGT-2 2008 calibration versus VGT-2 2007 • Good for Tengger (except blue) • A few percent discrepancy for Simpson (temporal stability ?)
Deserts cross Calibration Method Assessment • Study performed to provide inputs for deserts calibration error budget • TOA reflectance of different sensors (MODIS, MERIS, PARASOL, VGT, ETM+) simulated using Hyperion hyperspectral TOA data • Aqua/MODIS vs MERIS • MERIS vs Aqua/MODIS • ETM+ vs Terra/MODIS • VGT2 vs Parasol/POLDER • Parasol/POLDER vs Aqua/MODIS • Parasol/POLDER vs MERIS • Different cross calibration method tested : • Same geometry (data pairs simulated with the same Hyperion data) • Close geometry (data pairs from close geometry Hyperion pairs) • Closest spectral band (direct band to band comparison to spline interpolation) • Omitted spectral bands to assess interpolation and extrapolation effect
Acquisition geometry error • Comparison of same geometry and close geometry calibration Example of Aqua/MODIS vs MERIS Same geometry Close geometry • Very important increase of standard deviation (x2 to x10) but small effect on the mean value (0.5% max.) But viewing geometry is always the same (Hyperion geometry). Discrepancies are only due to : sun angles, atmospheric correction, annual variation of the site
Reflectance interpolation error • Comparison of spline interpolation and band to band calibration Example of Landsat/ETM+ vs Terra/MODIS Spline interpolation Band to band • Increase of cross calibration unaccuracy • Increase of site to site discrepancy Band to band calibration shall be limited to very similar bands (VGT2/VGT1, Aqua/MODIS vs Terra/MODIS…)
Reflectance extrapolation error • Comparison of cross calibration with different set of reference band Example of Aqua/MODIS vs MERIS With 412 nm as reference band Without 412 nm as reference band • Very important error due to extrapolation (> 20%) Site reflectance profiles do not allow any extrapolation neither in the blue or in the SWIR…
Interpolation (extrapolation !) error : main contributor of the error budget • Adequate choice for the reference sensor • Good knowledge of the site reflectance • Good knowledge of the directional effects over the sites • Statistics can take afford for atmospheric correction errors • Necessity for a great amount of data • Risk a small bias due to uncertainty on aerosol content • Good accuracy for multitemporal calibration • Sensors cross calibration only possible for ‘close’ spectral bands a more complete error budget has been undertaken Main Conclusions of the Study
SADE opening to GSICS and CEOS • Few feedbacks from beta-users : only one (very positive…) • SADE access through CNES scientific mission website • http://smsc.cnes.fr/CALIBRATION/ • Password mandatory • No procedure yet available for password delivery (contact Denis Blumstein or Patrice Henry) • A complete reprocessing of SADE exported files is foreseen for Nov. 2011 • Data extension up to mid 2011 • New sensors : • Terra/Modis • Landsat 7 • Theos • New MERIS reprocessing • VGT1 updated calibration