In: Wagner W„ Szekely, B. (eds.): ISPRS ТС VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
2.4 Outputs 
2.4.1 Processing lines 
The version 1 of vegetation variable processing line has been 
delivered to VITO in June 2010. After integration in the 
operational environment, expected before the end of the year, 
VITO will produce in NRT the vegetation variable and albedo 
product, and reprocess off-line the VGT archive to produce the 
time series of vegetation products. 
ONES is currently developing the climatology processing line 
which will process this archive to provide, in 2011, the yearly 
climatology products (see tables 1 & 2). 
The SWI processing line has been delivered to the Institute of 
Meteorology (IM) from Portugal in June 2010. After 
integration in the operational environment, expected before the 
end of the year, IM will produce in NRT the soil moisture 
product. 
2.4.2 Demonstration products 
Together with the processing lines, CNES delivers to the 
validation team and end users a test data set corresponding to 
the products that will be delivered by the operational 
processing line. All these data sets are available on the SDI 
web portal, with a Product User Manual document (PUM) 
describing their technical characteristics. 
In 2009 CNES has generated 4 years of Cyclopes V0 
vegetation products (2004 to 2007) which have been used by 
INRA to calculate the best neural network weights for the VI 
vegetation processing line. These products are available on the 
Postel web server. 
The vegetation variables test data set includes 10-day LAI, 
fAPAR, fCover & NDVI products from February 2003 to 
January 2005, issued from VGT2-P data provided by VITO. 
The first test data sets have been delivered mid-April 2010 and 
analysed by INRA (Fr), who detected a large difference with 
former products (V0 Cyclopes, MERIS products). This 
difference came from the fact that the neural network had been 
calibrated on Cyclopes V0 L3A data, while these new neural 
network weight were applied on VI L3A data. 
During May 2010 the neural network has been calibrated on 
VI L3A data and L3B test data have been re-produced with the 
new neural network weights. 
The new vegetation test data sets have been delivered to the 
users beginning of June 2010. 
The albedo test data set includes 10-day albedo products from 
February 2003 to January 2005, issued from VGT2-P data 
provided by VITO. 
The first delivery of SWI test data sets to TU Wien and Meteo- 
France took place end of March 2010, with a time length value 
of T = 10, 20, 40, 60 & 100. The analysis shown that above T 
= 20 the SWI values where the same, and a code mistake has 
been corrected. 
More, after analysis, it has been decided to extend the product 
range to lower values of T= 1, 5 & 15, in order to have a better 
analysis depth by avoiding the figure stagnation due to the T 
exponential integration (see §3.3 SWI product characteristics). 
The second version of test data sets have been delivered end of 
April 2010 and validated mid-May 2010. In order to have a 
service continuity with the end users until the product 
availability at IM (Pt), CNES produces monthly the SWI 
products and keep available the current archive, from June 
2007 up to the present. 
3. THE DEMONSTRATION PRODUCTS 
3.1 LAI/fAPAR/FCover/NDVI from SPOT/VGT 
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 (See [3]), 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. 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. 
The product has the following characteristics (see the PUM 
document on SDI for more details) : variables values with error 
and quality flags, spatial resolution 1 km, temporal resolution 
10 days, geometric accuracy < 300 m, thematic target accuracy 
5% to 15%, global coverage by square tiles of 10°. 
LAI FILLED - 2004/01/15 
-10 -5 0 5 10 15 20 25 
longitude