1 Spatial Modelling of the Terrestrial Environment - Georeferencial

Coupled Land Surface and Microwave Emission Models 67
Figure 4.2 Relationship between derived optical depth and Landsat-TM measured NDVI for
15 of the sites intensively monitored during SGP97 (redrawn from Burke et al., 2001a). The
moral right of the authors to be identified as the authors of such figure is asserted.
to estimate the optical depth using equation (8), see, for example, Jackson et al. (1999).
An alternative method is to use vegetation indices derived from visible wavelength satellite
data. These two methods for deriving the optical depth can both potentially introduce
significant errors into the retrieved soil moisture. For example, Burke et al. (2001a) used
the Normalized Difference Vegetation Index (NDVI) to derive optical depths for selected
sites intensively monitored during SGP97. They assumed that, at specific measurement sites,
the only unknown in the relationship between the measured soil moisture and measured
microwave brightness temperature was the optical depth. They then related the optical
depth derived in this way to the local NDVI estimated by Landsat-TM (Figure 4.2). A linear
function was fitted to this relationship, with 78% of the variation in the derived optical depth
explained by the variation in NDVI. Figure 4.2 also shows the 66% confidence intervals
of the fitted relationship which correspond to one standard deviation of the optical depth,
or approximately ±0.08. This empirical relationship was found to be dependent on the
resolution of the microwave sensor used to measure brightness temperature (Burke et al.,
2001a).
The SMOS mission provides an opportunity to retrieve estimates of the optical depth as
part of the retrieval algorithm because it will measure brightness temperatures at multiple
look-angles and both polarizations over similar areas at similar times. Therefore, there is
enough information for the proposed retrieval algorithm to retrieve both the near-surface
soil water content and the optical depth. The proposed retrieval algorithm assumes that the
opacity coefficient of the vegetation (equation 8) does not depend on either the polarization
of the radiation or the look-angle of the sensor. There is, however, some evidence that it
depends on both of these radiometer characteristics (van de Griend and Owe, 1996; Lee
et al., 2002b). Figure 4.3 shows the look-angle and polarization dependence of the mean
opacity coefficient retrieved (using the method described by Lee et al. (2002b)) from the
time series of brightness temperatures given in Figures 4.1c and d, this time series having
been calculated using the arguably more realistic extended Wilheit (1978) model. Values