Agriculture Reference
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validity range of the models. Nevertheless, it has been demonstrated that
relatively simple change detection approaches, which depend on the avail-
ability of a number of consecutive SAR acquisitions, may be successful in
tracking soil moisture conditions within a watershed.
A number of approaches use the single-channel SAR data provided by
ERS-1/2 (Crevier et al., 1996; Rotunno Filho et al., 1996; Moran et al.,
2000; Le Hégarat-Mascle et al., 2002). Bare-soil fields are ideal targets for
tracking soil moisture through time, but in an agricultural environment a
field does not remain bare throughout the season. The approach of Le-
Hégarat-Mascle et al. (2002), for a particular watershed, uses a time series
of images from one year to develop an empirical relationship between the
mean radar backscatter and mean soil moisture from a number of target
sites. In subsequent years, through inversion of the empirical relationship,
soil moisture can be directly estimated from the imagery rather than by
time-consuming ground-based measurements. The target sites are fields
of bare soil or fields with very little vegetation, which may change from
image to image in the time series. However, on any one date sufficient sites
are selected such that the mean value is representative of the watershed.
As an alternative to bare soil, Crevier et al. (1996) and Rotunno Filho et
al. (1996) proposed temporal measurements of semipermanent grass fields
to monitor soil moisture status of a watershed. The semipermanent grass
fields, due to their relative stability in surface characteristics both within
and across years, offer a means to normalize the surface roughness and
vegetation amounts. This idea is being further examined by Sokol et al.
(2002).
The technique of Moran et al. (2000), developed in a rangeland system
using ERS-1 data, may be useful in tracking a potential drought. A single
image acquired under dry conditions was used to normalize for surface
roughness and standing brown litter content in all other images in a time
series. The simple subtraction of the dry image backscatter values from
the backscatter values in every other image in the time series increased the
relationship with soil moisture (
r
2
[112
Line
——
0.0
——
Norm
PgEn
[112
0.93) compared to using the values
extracted directly from each image (
r
2
=
0.27). The green leaf area index
(GLAI), the one-sided green leaf area per unit ground, within the rangeland
system was
<
0.35 and thus could be ignored in terms of influencing soil
moisture determination using ERS-1. However, a method was presented to
correct for GLAI using a vegetation component derived from optical data.
Further progress in the use of radar can be expected with the launch
of technically more advanced multipolar, multifrequency radar satellites.
Moran et al. (1998) found, in alfalfa and cotton, that up to a GLAI value
of 4, high-frequency Ku (VV) radar was sensitive to increases in GLAI,
whereas the lower frequency C band (VV) was sensitive to soil moisture.
Above a GLAI value of 4, the Ku band saturated and the sensitivity of
C band to soil moisture decreased due to attenuation of the signal by
the vegetation. Similarly, Prevot et al. (1993) found that the simultaneous
use of X band (VV) which is adapted to biomass estimation and C-band
=
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