1 Spatial Modelling of the Terrestrial Environment - Georeferencial

70 Spatial Modelling of the Terrestrial Environment
soil moisture (0–5 cm) and deeper soil moisture (5–120 cm) predicted using MICRO-
SWEAT derived over one year for a typical crop growing in sandy soil using hourly driving
data from the SGP97 study area. There is a significant, but noisy, relationship that is
structured around several separate linear correlations, which are themselves related to the
water content in the soil profile immediately after the most recent precipitation event. (Note:
at the time of precipitation the modelled relationship between surface and deep soil moisture
depends strongly on the amount of precipitation and modelled runoff, but once precipitation
has ceased, the modelled relationship between the surface and deep soil moisture changes
mainly as a result of the surface soil drying.) Figure 4.4b shows the good agreement between
the MICRO-SWEAT modelled values of the ratio of near-surface to deep-soil moisture and
the values calculated using a simple derived relationship that acknowledges the effect of
recent precipitation.
Other methods of extending estimates of surface soil moisture deep in the soil profile
generally involve assimilation techniques, such as direct insertion of the surface soil moisture
(Walker et al., 2001) or use of the extended Kalman filter (Walker et al., 2001; Walker
and Houser, 2001; Hoeben and Troch, 2000) or variational assimilation (Reichle et al.,
2001). Walker et al. (2001) compared the direct insertion and Kalman filter assimilation
methods using synthetic data and demonstrated that using the Kalman filtering is superior,
with correction of the soil moisture profile being achieved in 12 hours as compared to
8 days or more with direct insertion. Variational data assimilation is computationally more
effective than using the Kalman filter, but variational assimilation requires an adjoint model
that is numerically well behaved and there are currently no adjoint models available for the
commonly used land surface models.
Assimilation procedures either introduce retrieved soil moisture into the land surface
model (Walker et al., 2001; Montaldo et al., 2001; Hoeben and Troch, 2000; Wigneron
et al., 1999; Li and Islam; 2002, Calvet and Noilhan, 2000), or the measured microwave
brightness temperatures is itself assimilated through the use of a coupled land surface
and microwave emission model (Crosson et al., 2002; Galantowicz et al., 1999). They
generally build on information already present in a land surface model and, in general,
result in improved profile soil moisture estimates by the land surface model, regardless of
the assimilation methods used. It has been demonstrated (Walker and Houser, 2001) that
through the assimilation of near-surface soil moisture observations, errors in forecast soil
moisture profiles that result from poor initialization may be removed, and the resulting
predictions of runoff and evapotranspiration by a hydrological model improved.
4.3.3 Subpixel Heterogeneity
In the near future, any passive microwave satellite mission is likely to measure with resolution
between approximately 30 and 60 km. At this resolution, the land surface is strongly
heterogeneous and the impact of this heterogeneity on the accuracy of the retrieved soil
moisture is a significant issue.
In the case of a heterogeneous bare soil, both Burke and Simmonds (2002) and
Galantowicz et al. (2000) demonstrated that errors in the retrieved soil moisture are negligible.
For a pixel with heterogeneous vegetation, Drusch et al. (1999), Liou et al. (1998),
Crow et al. (2001), Crow and Wood (2002), and Burke and Simmonds (2002) suggested
that errors in the retrieved soil moisture are generally less than 0.03 cm 3 cm −3 . This is a