Active Microwave Systems for Monitoring Drought Stress - Monitoring and Predicting Agricultural Drought: A Global Study - page 111

where σ 0 veg is the backscatter attributable to vegetation, σ 0 soil is the direct
backscatter from the underlying soil surface, a t is the one-way attenuation
factor, and σ 0 int represents multiple scattering in soil and vegetation.
In many models the interaction term σ 0 int is neglected because multiple
scattering by vegetation and soil is generally smaller than the direct contri-
butions from the canopy and the soil. One such model is the Cloud Model
proposed by Attema and Ulaby (1978), which assumes that the vegetation
volume can be represented by a cloud of water droplets that are uniformly
distributed throughout the volume. Although the Cloud Model does not
take structural effects into account, it has been used with some success to
empirically relate ground measurements of vegetation water content, vege-
tation height, and other crop parameters to backscatter measurements ac-
quired with ground-based, airborne, or spaceborne radar sensors (Bouman,
1991; Taconet et al., 1994; Xu et al., 1996).

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Monitoring Drought Stress

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PgEn

H igh-Resolution SAR Imaging

For assessing drought conditions at a local scale, such as a municipal or
watershed level, high-resolution imagery from visible-infrared scanners
and SAR systems can be used. The preceding discussion illustrated the
potential of SARs for mapping drought-relevant parameters including soil
moisture or plant stress. The latter may be manifest in reduced canopy
moisture and ultimately in plant growth.
The use of radar for directly inferring drought stress conditions of crops
has been limited. In a ground-based scatterometer study, radar backscat-
ter differed between stressed and nonstressed crops, but backscatter was
dependent on crop type and crop density (Brisco and Brown, 1990). The
separability of the stressed crops was attributed to their stunted growth
and lower biomass. Steven et al. (2000) concluded that Radarsat SAR data
can be used to detect canopy dehydration under conditions of moisture
stress.
The greatest potential for SAR data for drought monitoring is through
the estimation of soil moisture. Dobson and Ulaby (1998) provided an
overview of common modeling approaches on how to derive soil mois-
ture from SAR data. At the scale of SARs (tens of meters), spatial patterns
of surface roughness and both vegetation type and density leave a strong
imprint on SAR imagery. Therefore it is not possible to infer soil mois-
ture patterns by simple visual analysis of SAR images. More sophisticated
retrieval approaches are required that account for surface roughness and,
where vegetation is present, for the effect of vegetation on the SAR sig-
nal. Unfortunately, as previously discussed, models often fail to accurately
describe backscatter from bare soil surfaces due to the complex and multi-
scale structure of agricultural and other natural soil surfaces and the limited

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