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surface temperature, surface albedo, burnt areas, soil moisture, 
products, provided at various spatial resolution depending on 
the sensor data used as input (Table 3). 
Most of the products will be delivered in NRT in the sense of 
few days (less than one week), as requested by the final users 
who need to know the surface conditions within a few days of 
delay in order to react appropriately in case of anomaly, and to 
anticipate and manage the potential resulting problems. 
Detecting anomalies by comparing the current observation with 
a reference requires having consistent long time series. BioPar 
will provide such time series for some Essential Climate 
Variables (LAI, FAPAR, soil moisture) taking advantage of 
existing EO data archive, and developing sensor-independent 
methodologies. 
4.2 LAI, FCover, FAPAR, NDVT derived from 
SPOT/VEGETATION sensor data 
The leaf area index (LAI) is defined as half the total foliage area 
per unit of ground surface. The FCover is the fraction of ground 
unit covered by green vegetation. The FAPAR is defined as the 
fraction of photosynthetically active radiation absorbed by 
green vegetation for photosynthesis activity. The instantaneous 
FAPAR value at 10:00 solar time is used as a very good 
approximation to the daily integrated value under clear sky 
conditions. The Normalized Difference Vegetation Index 
(NDVI) corresponding to the SPOT-5/VEGETATION-2 sensor 
characteristics for its Red (B2) and NIR (B3) bands, is also 
provided. 
The algorithm is based on already existing LAI, FAPAR, and 
FCover products to capitalize on the efforts accomplished and 
get a larger consensus from the user community. Following the 
published literature on products validation (Weiss, et al. 2007; 
Garrigues, et al. 2008), the best performing products were 
selected and combined to take advantage of their specific 
performances while limiting the situations where products show 
deficiencies. The selected products are re-projected onto the 
VEGETATION plate-carree 1/112° grid, smoothed through 
time and interpolated at the 10 days frequency. Then the 
products are combined, and eventually scaled, to provide the 
fused product that is expected to give globally the ‘best’ 
performances. The fused products are generated for few years 
over the BELMANIP2 set of sites that is supposed to represent 
the possible range of surface types and conditions over the 
Earth (Baret, et al. 2006). Neural networks are then calibrated 
over this set of sites to relate the fused products to the 
corresponding atmospherically-corrected and directionally- 
normalized top of canopy SPOT/VEGETATION reflectances 
(Baret, et al. 2007). 
Figure 3: LAI derived from SPOT/VEGETATION, July 2002 
Such methodology has been defined by INRA, and the 
processing line has been adapted by CNES, based upon an 
existing chain developed previously by Medias-France in the 
framework of the FP5/CYCLOPES project. CNES has also 
generated two years of global, 10-daily products (example on 
Figure 3). Before the end of the year, the processing line will 
run in NRT at VITO. These vegetation products are being 
validated by EOLAB according to the protocol defined by the 
Land Product Validation (LPV) group of CEOS (Morissette et 
al., 2006). 
The methodology will be adapted to the historical AVHRR 
surface reflectances made available by the LTDR project. The 
archive from 1981 will be processed to get a long time series 
(about 30 years) of vegetation variables fully consistent with the 
SPOT/VEGETATION products. 
4.3 The set of biophysical variables derived from the FR 
MERIS sensor data. 
The MERIS Full Resolution (FR) biophysical products, and the 
High Resolution (HR) biophysical products, contain a set of 
variables including estimates of the green, brown & soil cover 
fractions, the LAI, the FAPAR, the chlorophyll content, a 
canopy shadow factor, and the water & snow cover fractions. 
Figure 4. FAPAR over the Adour-Garonne river basin for June 
2008 (left), and the Guadalquivir river basin for June 2006 
(right) derived from MERIS FR data. The values vary from 0 
(white) to 1 (red). 
The baseline vegetation model developed for processing the 
MERIS data uses the SAIL/PROSPECT model as core 
component (Verhoef, 1984; Jacquemoud et Baret, 1990). This 
model was upgraded and completed in order to include the 
contribution of brown vegetation, the modelling of “rough” 
canopies, the computation of vegetation cover fractions in 
reference directional conditions, and the computation of 
FAPAR from the SAIL model. Then, to restore the 
heterogeneous nature of the MERIS pixels, a further modelling 
step is applied consisting in having a composite canopy model 
made of two components: 1) a main canopy component made of 
predominantly green vegetation, that may have all range of 
conditions from crops to forest/shrub canopies; 2) a second 
canopy component made of low brown vegetation, primarily 
designed to model either senescent crops/grasslands or bare soil 
conditions. Another important component of the developed 
model is the soil modelling, the soil reflectance being an input 
of the SAIL model. In this approach, the variation of soil 
brightness in relation with soil roughness and humidity is 
handled by performing an initial learning of the soil mean 
spectral signature in the MERIS bands at a regional level. Then, 
this signature is exploited through a physical model of soil 
variability, accounting for the soil shading and surface humidity 
effects. This scene model is then coupled with a model of the 
atmospheric transmission from Earth surface level to the sensor 
(Verhoef and Bach, 2003). The detection of water and snow 
covers is done through additional modelling of the