In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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computational adaptation of the original SWI algorithm has 
been made based on a recursive formulation proposed by 
Albergel (2008). In this method, a gain factor is introduced that 
relates the past SWI measurements to the current 
measurements. The SWI processing algorithm uses ASCAT- 
25km Level 2 Soil Surface Moisture product as input to 
generate daily global SWI images, calculated for 8 different T 
values (1, 5, 10, 15, 20, 40, 60, 100) together with the 
respective quality flags. 
The product has the following characteristics (see the PUM 
document on SDI for more details) : index values quality flags, 
spatial resolution 25 km, temporal resolution 1 day, geometric 
accuracy 4 km, thematic target accuracy 10%, global coverage. 
January 2009 Time length = 10 
July 2009 Time length = 10 
July 2009 Time length = 40 
July 2009 Time length = 100 
Figure 6 : Soil Water Index data sets, © CNES/TU Wien. 
4. CONCLUSION 
These first deliveries of BioPar test data show that vegetation 
and humidity variables products are know qualified and 
available for end users analysis. The product lines shall be 
operational for NRT processing in the production centres 
(VITO & IM) before the end of the year. All the products are 
available on the SDI web portal. 
Development will continue on the same scheme and new 
versions are foreseen : 
• Version 2 of vegetation variable processing line will 
improve the overall performance of the product line 
by applying directly the neural network to the VGT-P 
top of atmosphere reflectance. Products shall be 
available end of 2011. 
• SWI & F/T Version 2 will improve the detection of 
ffeeze/thaw surface conditions and shall be available 
beginning of 2012. 
CNES participate also to the SATChMo Core Monitoring 
Service (see figure 1) and will deliver end of 2011 a medium 
resolution land cover change map from MODIS data, on 
Europe and Africa. The development activities, shared with 
Université Catholique de Louvain in Belgium and the Institute 
of Geodesy and Cartography in Poland, will follow the same 
engineering cycle that BioPar. 
5. REFERENCES 
[1] Lacaze R. & al., Geoland2 - Towards an operational GMES 
Land Monitoring Core Service; First Results of the 
Biogeophysical Parameter Core Mapping Service, ISPRS 2010 
[2] Baret, F., Hagolle, O. et ah, 2007. LAI, fAPAR and fCover 
CYCLOPES global products derived from VEGETATION. 
Part 1: Principles of the algorithm. Rem. Sens. Environ., 110: 
275-286. 
[3] Weiss, M., Baret, F., Garrigues, S., Lacaze, R. and 
Bicheron, P., 2007. LAI, fAPAR and fCover CYCLOPES 
global products derived from VEGETATION, part 2: 
Validation and comparison with MODIS Collection 4 products. 
Rem. Sens. Environ, 110: 317-331. 
[4] Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, 
N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., Martin, 
E., 2008. From near-surface to root-zone soil moisture using an 
exponential filter: an assessment of the method based on in- 
situ observations and model simulations. Hydrol. Earth Syst. 
Sei. 12: 1323-1337. 
[5] Wagner, W., Lemoine, G., Rott, H., 1999. A Method for 
Estimating Soil Moisture from ERS Scatterometer and Soil 
Data. Rem. Sens. Environ. 70: 191-207. 
6. WEBSITES 
Geoland 2 : http://www.land.eu 
SDI portal : http://www.geoland2.eu/ 
POSTEL : Land surface thematic centre, 
http ://postel. mediasfrance.org/ 
CNES : http://www.cnes.fr 
VEGA Technologies : http://www.vegatechnologies.fr