In: Wagner W„ Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
corresponding surfaces which are combined, as linear mixture, 
with the standard land model. 
Infoterra France has elaborated the methodology above, 
developed the processing line, and ran it in off-line mode. 
Today, the existing MERIS FR (300m resolution) products 
covers some river basin across Europe (Rhine, Seine- 
Normandie, Guadalquivir, Adour-Garonne, Nemunas, Moselle- 
Sarre, Motala-Strom, Sventoji, Strymonas-Struma) for years of 
major interest for the final users (Figure 4). The production in 
NRT covering the whole Europe should start in the next weeks. 
These products are being validated by EOLAB, jointly with the 
products derived from SPOT/VEGETATION data (§4.2). 
4.4 Soil Water Index (SWI) derived from ASCAT/Metop 
sensor data 
The Soil Water Index is defined as the soil moisture content (in 
percent) in the soil profile. The retrieval algorithm uses an 
infiltration model describing the relation between Surface Soil 
Moisture (SSM) and profile soil moisture as a function of time. 
The algorithm is based on a two-layer water balance model 
(Wagner et al., 1999) to estimate profile soil moisture from 
SSM retrieved from scatterometer data. The remotely sensed 
topsoil represents the first layer and the second layer extends 
downwards from the bottom of the surface layer. In this model, 
the water content of the reservoir, whose depth is related to a 
characteristic time length (T), is described in terms of an index, 
which is controlled only by the past soil moisture conditions in 
the surface layer. A 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 SSM product as input to generate daily global 
SWI images, calculated for five different T values (1, 5, 10, 15, 
20, 40, 60, 100) together with the respective quality flags. 
Figure 5. Global SWI derived from ASCAT/Metop data (25km 
resolution) for T=10 on 20 th July 2007. 
The retrieval algorithm is defined by Vienna University of 
Technology, the processing line is developed by CNES which 
has also generated the SWI products over the period from 1 st 
June 2007 to the present (Figure 5). In few weeks, the 
processing line will run in near real time at the Institute of 
Meteorology of Portugal. The SWI products are being validated 
by Météo-France and ECMWF using in-situ observations, and 
operational, analyzed products from models running at global 
and regional scale. 
A second version of the product is planned in the project life, 
including a more accurate detection of the freeze and thaw 
conditions of the surface. Then, the ERS/Scat data archive will 
be re-processed in order to get a long time series of SWI fully 
consistent with the current ASCAT products. 
4.5 The Surface Albedo derived from the 
SPOT/VEGETATION sensor data 
The albedo is the fraction of the incoming solar radiation 
reflected by the land surface, integrated over the whole viewing 
directions. The BioPar albedo products include the directional 
albedo calculated for the local solar noon, and the hemispheric 
albedo, integrated over the whole illumination directions for 3 
broad bands: visible [0.4, 0.7pm], near-infrared [0.7, 4pm], and 
the whole solar spectrum [0.3, 4pm], The coefficients resulting 
from the inversion of a 3-kernels linear bidirectional reflectance 
model on the atmospherically-corrected SPOT/VEGETATION 
reflectances (Baret, et al. 2007) acquired during a period of 30 
days are then combined with the pre-computed values of the 
directional kernels integrated over angular domains to estimate 
albedos. Finally, the broadband albedos are derived by linear 
relationships of spectral quantities. 
This algorithm, and the processing line, have been previously 
set-up in the framework of the FP5/CYCLOPES project, by 
CNRM, and Medias-France, respectively. CNES has adapted 
the existing chain to the geoland2/BioPar specifications, and 
generated two years of global, 10-days products. Before the end 
of the year, the processing line will run in NRT at VITO. These 
SPOT/VEGETATION albedo products are being validated by 
EOLAB according to the protocol defined by the Land Product 
Validation (LPV) group of CEOS. An inter-comparison with 
the other BioPar albedo product (§4.6), derived by merging 
geostationary and polar sensors data, will be performed. 
4.6 Other BioPar products 
The radiation variables of the BioPar portfolio (Downwelling 
Shortwave Surface Flux (DSSF), Downwelling Longwave 
Surface Flux (DSLF), Land Surface Temperature (LST), and 
albedo) will be generated by the fusion of geostationary and 
polar sensor data. The DSSF represents the short-wave fraction 
of the solar irradiance (0.3-4 pm) reaching the soil background. 
The DSLF is defined as the irradiance reaching the surface in 
the thermal infrared part of the spectrum (4-100 pm). The LST 
is the radiative skin temperature of land surface. The albedo 
variables are the same as those retrieved using 
SPOT/VEGETATION data (§4.5). The Institute of Meteorology 
of Portugal is in charge of the algorithm definition, the 
processing lines development, and the production in near real 
time of the 4 products. These radiation variables correspond to 
an extension of those currently produced on an operational 
basis by the Satellite Application Facility on Land Surface 
Analysis (Trigo et al., 2010). While the latter are restricted to 
EUMETSAT sensors, Geoland-2 products make use of non- 
European geostationary satellites to increase area coverage. The 
demonstration DSLF, and LST products shall be ready in the 
coming weeks, and the demonstration DSSF and albedo 
products shall be available by the end of the year. 
The small water bodies product results from the fusion of two 
existing algorithms: the first one (Gond et al., 2004) was 
developed in the framework of VGT4AFRICA by the Joint 
Research Centre and is suited to arid and semi arid condition; 
the second one was developed for Global Watch project and was 
further developed in the context of Desert Locust prevention 
FAO product (Pekel, 2009). Both methods rely on different 
thresholds for the NDVI, the Normalized Difference Water