Agriculture Reference
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profile of known properties, it was shown that a theoretical basis exists
for surface-profile relationships and that the chances of success can be
improved with additional observations at greater soil depths at particular
times of the day (early morning/predawn).
Arya et al. (1983) examined the correlation between surface observa-
tions and the soil moisture profile. They observed that the correlation de-
creases as the depth of the soil profile increases. Better results would be
expected for vegetated fields than for bare soil because vegetation tends to
make the surface soil moisture profile more homogeneous with depth. The
authors also compared the differences in the profile water determined using
this approach and using the measured net surface flux. In this study, the
two approaches were nearly equal, which could indicate that no recharge
or flux across the lower boundary was occurring.
Jackson et al. (1987) combined spatially distributed remotely sensed sur-
face observations of soil moisture over a large area in the Texas High Plains
region, United States, of the Ogallala Aquifer with limited ground profile
observations to produce preplanting profile soil moisture maps. The con-
ventional approach to generating the soil moisture product involved sam-
pling the profile at selected locations and then developing a contour map.
The accuracy of this product depended on the number of points and how
well they represented the local conditions at the field scale. In the remote
sensing approach, a correlation was established between (1) the surface ob-
servation determined using 1.4-GHz passive microwave data and (2) the
profile soil moisture at the observation points. Using this relationship at
each remote sensing data point, an estimate of profile soil moisture was
produced. If repeated on a temporal basis, this technique could provide
spatial information on the flux of the soil water profile.
[100
Line
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-1.8
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Norm
PgEn
[100
So il Moisture-Related Indices
R adiation Aridity Index Reutov and Shutko (1987) established a linkage
be tween microwave brightness temperature and an integrated climate pa-
ra meter called the radiation aridity index ( S ). This is computed as follows:
Annual radiation balance
( Latent heat of vaporization )( Annual precipitation )
S =
[7.5]
The authors cite numerous studies that link this climate variable to runoff,
biological activity, and economic productivity. It is essentially the ratio
between incident energy and the energy used to evaporate moisture from
the soil.
Using extensive records from regions in Russia and surrounding states,
they assembled soil moisture and temperature data as well as the data to
compute S . Brightness temperature values were simulated using the ob-
served soil moisture and temperature. Average brightness temperatures
during growing season were computed. Individual regions were then clas-
sified into one of several landscape types and a range of S and T B for each
 
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