Algorithm Theoretical Based Document (ATBD) - CESBIO

SO-TN-ESL-SM-GS-0001
Issue 1.a
Date: 31/08/2006
SMOS level 2 processor
Soil moisture ATBD
Figure 12 : MEAN_WEF: image
Figure 13 : MEAN_WEF: semi log cross-cut
The MEAN_WEF must be normalized to unity integral and applied to land cover in order to compute mean fractions.
The above description corresponds to the MEAN_WEF being defined over a FG geographical grid expressed in km.
The following formulation can be used for its actual computation:
MEAN _ WEF
MEAN _ WEF
MEAN _ WEF
earth
( ρ ) = C + WEF ⎜ ⋅ ⎟ for ρ ∈[ 0, C ]
earth
( ρ )
earth
= C
MWEF 2
MWEF 2
for ρ
( ρ ) = 0 otherwise
earth
A
earth
⎛ ρ
⎜
⎝ C
MWEF1
⎤
∈
⎥
C
⎦
MWEF
π ⎞
⎟ earth
CWEF1
⎠
WEF _ SIZE ⎤
1,
2 ⎥
⎦
MWEF1
As for WEF, the MEAN_WEF will be used as a tabulated form of the above equation and thus defined in TGRD.
The values for C MWEF1 (40 km) and C MWEF2 (0.02) are supplied in UPF.
Both WEF and MEAN_WEF are thus used to compute aggregated fractions from the land cover array over a fine grid
area. It will be seen that the land cover includes two classifications: a complementary one, and a superimposed
supplementary one, which accounts for possible NPE conditions as well as the topographic mask.
• Since the complementary classification covers the whole area, rules are necessary to "blend in" the
supplementary classification. This topic is addressed in the decision tree section.
• In a later step, the WEF will be used to compute reference parameter values for every quantity relevant and for
each view for building the forward models. This is also addressed in the decision tree section.
3.2.3 Decision tree
3.2.3.1 Content of the decision tree section
• The decision tree procedure begins with determining the weighted mean aggregated fractions FM 0 which are to
be considered in the decision tree, as well as those FM which contribute to modelled radiometric contributions.
• A battery of tests is defined, based on a series of thresholds concerning the magnitude of various fractions, and
allows defining the branches for the 1rst stage of the decision tree;
• The fraction (s) and model (s) selected for retrieval or default contributions are selected for each branch;
• From auxiliary data, reference values of either fixed parameters or a priori constraints are obtained for every
relevant fraction of the pixel.
• Options (stage 2 set of branches) are finally chosen for the retrieval, concerning the number and nature of
parameters to be actually retrieved as well as a priori standard deviations.
3.2.3.2 Computing aggregated fractions
Eq 71
.
74