Geoscience Reference
In-Depth Information
reason for the lack of success is that most geophysical inversion approaches assume that (1) there are
multiple conductivity signal readings available for each survey point and (2) that distinct, physical
strata differences exist within the near-surface soil horizon. Neither of these conditions is typically
satisfied in most EC
a
surveys.
A more common technique, which is used in soil science, is to employ some form of determin-
istic conversion model (i.e., an equation that converts EC
a
to a soil property based on knowledge of
other soil properties). One model of this type that has been shown to be useful is the model devel-
oped by Rhoades et al. (1989, 1990; see Equation (4.6) through Equation (4.11)) and extended by
Lesch and Corwin (2003). The model demonstrates that soil EC can be reduced to a nonlinear func-
tion of five soil properties: EC
e
, SP, θ
g
, ρ
b
, and soil temperature. In Rhoades et al. (1990), the model
was used to estimate field soil salinity levels based on EC
a
survey data and measured or inferred
information about the remaining soil physical properties. Corwin and Lesch (2003) and Lesch et al.
(2000) showed that this model can also be used to assess the degree of influence that each of these
soil properties has on the acquired EC
a
-survey data.
4.3.3 d
e t e R M i n a t i of n
of f
t h e
s
o i l
P
R o P e R t i e s
i
n f l u e n c i n g
ec
a
In the past, the fact that EC
a
is a function of several soil properties (i.e., soil salinity, texture, water
content, etc.) has sometimes been overlooked in the application of EC
a
measurements to agriculture.
For instance, precision agriculture studies relating EC
a
to crop yield have met with inconsistent results
due to the fact that a combination of factors influence EC
a
measurements to varying degrees across
units of management, thereby confounding interpretation. In areas of saline soils, salinity dominates
the EC
a
measurements, and interpretations are often straightforward. However, in areas other than
arid zone soils, texture and water content or even OM may be the dominant properties measured by
EC
a
. To use spatial measurements of EC
a
in a soil quality or site-specific crop management context,
it is necessary to understand what factors are most significantly influencing the EC
a
measurements
within the field of study. There are two commonly used approaches for determining the predominant
factors influencing EC
a
measurement: (1) wavelet analysis and (2) simple statistical correlation.
An explanation of the use of wavelet analysis for determining the soil properties influencing
EC
a
measurements is provided by Lark et al. (2003). Even though wavelet analysis is a powerful
tool for determining the dominant complex interrelated factors influencing EC
a
measurement, it
requires soil sample data collected on a regular grid or equal-spaced transect. Grid or equal-spaced
transect sampling schemes are not as practical for determining spatial distributions of soil salinity
or other correlated soil properties from EC
a
measurements as the statistical and graphical approach
developed by Lesch et al. (1995a, 1995b, 2000).
The most practical means of interpreting and understanding the tremendous volume of spatial
data from an EC
a
survey is through statistical analysis and graphic display. For a soil quality assess-
ment, a basic statistical analysis of all physical and chemical data by depth increment provides an
understanding of the vertical profile distribution. A basic statistical analysis consists of the deter-
mination of the mean, minimum, maximum, range, standard deviation, standard error, coefficient
of variation, and skewness for each depth increment (e.g., 0 to 0.3, 0.3 to 0.6, 0.6 to 0.9, and 0.9 to
1.2 m) and by composite depth (e.g., 0 to 1.2 m) over the depth of measurement of EC
a
. In the case
of EC
a
measured with ER, the composite depth over the depth of measurement of EC
a
is based on
the spacing between the electrodes, while in the case of EMI measurements of EC
a
, the composite
depth over the depth of measurement of EC
a
is based on the spacing between the coils and the orien-
tation of the coils (i.e., vertical or horizontal). The calculation of the correlation coefficient between
EC
a
and mean value of each soil property by depth increment and composite depth over multiple
sample sites determines those soil properties that correlate best with EC
a
and those soil properties
that are spatially represented by the EC
a
-directed sampling design. Those properties not corre-
lated with EC
a
are not spatially characterized with the EC
a
-directed sampling design, indicating
