Correction of bidirectionnal effects and impact on land cover classifications J-L. CHAMPEAUX METEO-FRANCE S. GARRIGUES METEO-FRANCE C. GOUVEIA ICAT, Universidade de Lisboa, Lisbon, Portugal EST, Instituto Politécnico de Setubal, Portugal P. BICHERON SCOT, Toulouse, France GLC 2000 – “FIRST RESULTS” WORKSHOP JRC – Ispra, March 2002

The usual strategy for directional correction: A 3 STEPS APPROACH COLLECT RELIABLE CLOUD-FREE DATA NORMALISE the DATA  s o ;  v o =NADIR FIT THE BRDF MODEL Roujean et al. (1992) :  MOD (  s,  v,  ) = k0 +k1 f1 (  s,  v,  ) + k2 f2 (  s,  v,  ) Cloud mask For S Products?

COLLECT THE N LAST CLOUD-FREE DATA FIT THE BRDF MODEL THE BIDIRECTIONAL COMPOSITING (BDC) P. MAISONGRANDE, B. DUCHEMIN, CESBIO MAIN IDEA : COLLECT A CONSTANT NUMBER OF DATA TO FIT THE BRDF, REGARDLESS TO THE DATE OF ACQUISITION AVERAGE OF THESE REFLECTANCES USE THE ADJUSTED BRDF TO NORMALISE THE CLEAR REFLECTANCES OF THE LAST 10 DAYS

SCOT CLOUD MASK: A pixel is cloudy if: B0(j) > 0.2 and B0(j) > 6* B0 Clim (month) And B0(j) > B2(j) METEO-France CLOUD MASK: A pixel is cloudy if: B0(j) > 0.11 And SWIR(j) > 0.09 The BDC approach was applied here but S1 products are not optimal : - reduced data number and angular sampling - no possibility to track aerosol loading - cloud mask is not satisfactory

I BDC NDVI (SCOT mask) BDC NDVI (Meteo-France mask) IMPORTANCE OF CLOUD DETECTION

Plot of 10-days NDVI profiles: MVC (red), BDC (green)

10-days NDVI profiles for several methods MVC, BDC, Roujean model, daily data Blue: Roujean model Green:Roujean model Red: MVC Black: BDC

CONCLUSION The cloud mask is crucial for use in kernel-driven BRDF models (corrupted data distort the adjustment of the regression) After bidirectional corrections, NDVI profiles are noisy mainly in cloudy regions (due to the too small number of points used for the regression) At this stage of the study, the landcover classifications made with data corrected from the bidirectionnal effects do not improve the final results