Geoscience Reference
In-Depth Information
transport modeling in the vadose zone, SSCM, assessing management-induced changes, and map-
ping and inventorying soil properties.
When geospatial measurements of EC
a
are spatially correlated with geo-referenced yield data,
their combined use provides an excellent tool for identifying edaphic and anthropogenic factors
that influence yield, which can be used to delineate SSMUs (Corwin and Lesch, 2005a; Corwin
et al., 2003a). The delineation of productivity zones from geospatial measurements of EC
a
provides
another approach to SSCM (Jaynes et al., 2005; Kitchen et al., 2005). Even so, an understanding of
the soil-related factors influencing yield or the identification of productivity zones does not provide
the whole picture for SSCM because crop systems are affected by a complex interaction of edaphic,
biological, meteorological, anthropogenic, and topographic factors. Moreover, the precise manner
in which these factors influence the dynamic process of plant growth and reproduction is not always
well understood. Geo-referenced EC
a
will only help to provide a spatial understanding of edaphic
and anthropogenic influences. To be able to manage within-field variation in yield, it is necessary
to have an understanding within a spatial context of the relationship of all dominant factors causing
the variation.
Current applications of geophysical techniques in agriculture have made it evident that the tem-
poral and spatial complexity of soil-plant systems at field and larger spatial extents will require a
combined use of multiple geophysical sensors to obtain the full spectrum of spatial data necessary
to identify and characterize the factors influencing yield. Of these, the use of hyperspectral imagery,
EMI, real-time kinematic GPS, and GPR probably have the greatest potential from a cost-benefit
perspective for providing the greatest information impact. The fruition of EC
a
in SSCM will likely
come from future plant indicator approaches where combinations of geo-referenced data are used
(Corwin and Lesch, 2003). These geo-referenced data will likely include airborne multi- and hyper-
spectral imagery, EMI, GPR, and real-time kinetic GPS. Plant and soil sampling with model- or
design-based sampling strategies will be based on the combined data inputs. Manipulation, orga-
nization, and display of these inputs and outputs will be performed with a geographic information
system, image analysis, and spatial statistical analysis.
Remotely sensed imagery and EMI measurements of EC
a
provide complementary information.
Remotely sensed imagery is generally best suited for spatially characterizing dynamic properties
associated directly with plant vegetative development, and EC
a
measurements are best suited for
spatially characterizing static soil properties such as texture, water table depth, and steady-state
salinity. Remotely sensed imagery is particularly well suited for obtaining spatial crop information
during the maturation of a crop. Furthermore, hyperspectral imagery may hold the key for iden-
tifying the spatial effects of nonedaphic factors (e.g., disease, climate, humankind, etc.) on crops.
Geospatial measurements of EC
a
are most reliable for measuring static soil properties that may
influence crop yield because of the associated soil sampling required for ground truth to establish
what soil property or properties are influencing EC
a
at a given point of measurement. Soil sampling
and analysis is time and labor intensive, making the measurement of dynamic soil properties using
EC
a
generally untenable. Ground truth for remotely sensed imagery is also necessary, but (1) wide-
coverage real-time remote images are generally easier to obtain than spatially comparable real-time
EC
a
data unless EC
a
is measured from an airborne platform and (2) calibrations are often faster
because soil sampling for EC
a
can involve several depth increments and numerous soil properties.
Conventional mobilized ground-based EC
a
platforms cannot begin to compete with satellite or air-
borne imagery from the perspective of extent of coverage of real-time data. Nonetheless, ground-
based EC
a
surveys at field scales have their place because they allow greater control and potentially
increased spatial resolution.
There is no question that geospatial measurements of EC
a
have found a niche in agricultural
research and will likely continue to serve a significant role in the future. However, additional spatial
information is needed to fill gaps in the database necessary for SSCM, including (1) the need for
integrated spatial data of topographical, meteorological, biological, anthropogenic, and edaphic
