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the relationship between the observed backscatter coefficient in [dB] and the actual soil moisture condition is linear
a long historic time series captures the driest (lowest backscatter, "air dryness") and wettest (highest backscatter, "saturation") possible soil conditions
The retrieved soil moisture values from the TU-Wien model are commonly expressed as degree of saturation of the soil surface layer.
Estimating the historically driest and wettest soil moisture condition by taking the 2.5% lower/upper backscatter values
the dry reference $\sigma^0_{dry}$ has to be shifted back (extrapolated) to the reference incidence angle of 40 degrees by means of the corresponding slope and curvature functions
this is not necessary for the wet reference, since it is assumed to be 40° (i.e., equal to the wet crossover angle)
Comparison of the observed normalised backscatter measurements $\sigma(40°,t)$ to the dry ($\sigma^0_{dry}$) and wet ($\sigma^0_{wet}$) reference:
$sm(t)\;=\;\frac{\sigma^0(40,t)\;-\;\sigma^0_{dry}(40,t)}{\sigma^0_{dry}(40,t)\;-\;\sigma^0_{wet}(40,t)}$
Now we've got a soil moisture value!
L1b...Level 1b
DGG...discrete global grid
The soil moisture retrieval method is a data-based approach:
No external/auxiliary datasets are used for the retrieval
No soil texture, soil type, land cover, biomass, evapotranspiration, brightness temperature…
But raw backscattering signatures in different incidence (viewing) angles