R. Santer and B. Berthelot Final meeting, ESRIN, Frascati, April 21, 2009 Calibration Test Sites Selection and Characterisation WP260 – Error analysis: radiometric Calibration Spectral calibration

Objectives Start from WP220: methods To evaluate the performances of the calibration With sites associate to methods Compare specifications and performances for recommendations

Sensors versus methods versus sites Class 3 (TM) -> radiometry->LES(La Crau,WS) Class 2 (MERIS)->radiometry->SES (Moby, AAOT) Class 2 (class 3) -> inter sensor->LNES Class 2 (class 3) -> inter temporal->LNES Class 2 -> inter sensor->WNES Class 2 -> inter band-> sunglint

Error analysis on what? TOA radiance L1 –Perfect instrument (no stray light, no smile, excellent S/N ratio) –From laboratory to sun (radiance based method) TOA reflectance –Vicarious methods reefer to reflectance

Inputs to the error analysis Nominal conditions (WP240) Nominal satellite signal (WP220) Error bars (random) –Current values for accuracy Error bars (systematic) –On input assumptions (aerosol model,…) Bias (systematic) –On physical simplifications (BRDF,..)

6S for computation A reference code With polarization With many functionalities –Sensor altitude –BRDF –Adjacency effect

Output of the error analysis Error bars on the random terms Sensitivity analysis on the systematic error Study of the bias

LES and TM, nominal Winter and summer Gaseous correction Aerosol (AOT) Surface (nadir): 2 percent error

La Crau and White Sands, surface

La Crau and White Sands (total)

BRDF LCWS

Adjacency effects

Conclusion for LES Random: good to do more but we need a sensor degradation model Aerosol model needs to be better characterized (more than with the AOT) Remove assumption of signal formulation –BRDF –Adjacency effect

Better aerosol IOP Single scattering albedo for bright (WS) Phase function for La Crau Recommendation: –Sky radiance measurement

Better BRDF In situ frequent measurements BRDF from space sensor (MISR) Adjacency effect correction Software for correction

WES and MERIS, nominal Winter and summer Gaseous correction Aerosol (AOT) Surface: 5 percent error by consistency

AAOT

MOBY

Conclusion of WES Need AOT on site (MOBY?, BOUSSOLE?) Need more for aerosols in the NIR: sky radiance Stable and homogeneous water body Contradiction between stable platforms and stable waters: AERONET close to case 1 water buey WES=Water Equipped Ship –Limitation for aerosol characterization

Recommendation for WES Water and atmosphere Adjacency effect correction when needed BRDF characterisation of the water body

The so called Rayleigh calibration Looks like a reflectance based Water reflectance from climatology Aerosol type from climatology AOT from NIR (CNES approach)

The radiance based calibration At surface: equivalent to reflectance based if radiometer calibrated by reference to the sun. Aircraft measurement are one option

WS aircraft The higher the better Need Rayleigh correction above Need Aerosol above at 3 km

Sensor inter calibration over LNES Link to the calibration accuracy of the reference sensor Specific protocol to apply Differences in time and view condition result in marginal error if BRDF model available Statistical analysis based on error estimate

Multi temporal calibration over LNES BRDF surface model Aerosol variability

Inter sensor calibration over WNES Control the aerosol difference between the two sensors Mainly in the NIR

Inter band calibration, sunglint Predict the signal the best you can: –AOT –Sky radiance

Spectral calibration, Fraunhoffer For MERIS 0.1 nm For Sciamachy, one order better

Spectral: inter sensor MERIS around 0.1 nm

Summary radiometry

Summary inter sensors