cnes activities in the framework of gsics

GSICS Executive Committee – WMO Geneva – June 06-08 2011

CNES Activitiesin the Framework of GSICS

Patrice Henry, Denis Blumstein, Denis Jouglet - CNES

Thomas Colin - CS

2GSICS Executive Committee – WMO Geneva – June 06-08 2011

AIRS/IASI Intercalibration — sample of results

■ Intercalibration AIRS/IASISNO events (high latitude only)operational in the IASI TECactivated on a regular basis

3 months

■ Updated to handle IASI L1C Day-2 products (from May 2010)


MetopA/IASI-A MetopB/IASI-B

~39°

common zone

16km

10km

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

IASI-A IASI-B

common view by

IASI-A / IASI-B


Study of Asian and Australian Desert Sites forSensor cross-Calibration in the VPIR Range

■ 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


Simpson Tengger

Algeria 3

Libya 1

Libya 3

General view of the 5 sites


Sites temporal behaviour

Spectral dependance of seasonal effect on spectral range for Tengger

(vegetation ?)

Simpson Tengger

Algeria 3 Libya 1 Libya 3

PARASOL TOA reflectance normalized by the red reflectance


VGT2 calibration versus Parasol

Mean 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 …)

Standard deviation of VGT2/PARASOL calibration results


Multitemporal calibration

PARASOL 2008 calibration versus PARASOL 2007

• Good for Tengger (except blue)

• A few percent discrepancy for Simpson (temporal stability ?)

VGT-2 2008 calibration versus VGT-2 2007


■ 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

Deserts cross Calibration Method Assessment


■ Comparison of same geometry and close geometry calibration

Example of Aqua/MODIS vs MERIS

Acquisition geometry error

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


■ Comparison of spline interpolation and band to band calibration

Example of Landsat/ETM+ vs Terra/MODIS

Reflectance interpolation error

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…)


■ Comparison of cross calibration with different set of reference band

Example of Aqua/MODIS vs MERIS

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…

Reflectance extrapolation error

With 412 nm as reference band


■ 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


■ 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

SADE opening to GSICS and CEOS

cnes activities in the framework of gsics

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