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sampling. Design-based sampling primarily consists of the use of unsupervised classification (John-
son et al., 2001), whereas model-based sampling typically relies on optimized spatial response sur-
face sampling (SRSS) design (Corwin and Lesch, 2005b). Design-based sampling also includes
simple random and stratified random sampling. Lesch and colleagues (Lesch, 2005; Lesch et al.,
1995a, 1995b, 2000) developed a model-based SRSS software package (ESAP) that is specifically
designed for use with ground-based soil EC
a
data. The ESAP software package identifies the opti-
mal locations for soil sample sites from the EC
a
survey data. These sites are selected based on spa-
tial statistics to reflect the observed spatial variability in EC
a
survey measurements. Generally, eight
to twelve sites are selected depending on the level of variability of the EC
a
measurements for a site.
The optimal locations of a minimal subset of EC
a
survey sites are identified to obtain soil samples.
Protocols are currently available to maintain reliability, consistency, accuracy, and compatibility of
EC
a
surveys and their interpretation for characterizing spatial variability of soil physical and chemi-
cal properties (Corwin and Lesch, 2005b).
There are two main advantages to the response-surface approach. First, a substantial reduction
in the number of samples required for effectively estimating a calibration function can be achieved
in comparison to more traditional design-based sampling schemes. Second, this approach lends
itself naturally to the analysis of remotely sensed EC
a
data. Many types of ground-, airborne-, and
satellite-based remotely sensed data are often collected specifically because one expects this data
to correlate strongly with some parameter of interest (e.g., crop stress, soil type, soil salinity, etc.),
but the exact parameter estimates (associated with the calibration model) may still need to be deter-
mined via some type of site-specific sampling design. The response-surface approach explicitly
optimizes this site-selection process.
2.3.2 c
h a R a c t e R i z a t i of n
of f
s
of i l
s
P a t i a l
v
a R i a b i l i t y
w i t h
ec
a
The shift in the emphasis of field-related EC
a
research from observed associations to directed-sam-
pling design has gained momentum, resulting in the accepted use of geospatial measurements of
EC
a
as a reliable directed-sampling tool for characterizing spatial variability at field and landscape
extents (Corwin and Lesch, 2003, 2005a, 2005b). At present, no other measurement provides a
greater level of spatial soil information than that of geospatial measurements of EC
a
when used to
direct soil sampling to characterize spatial variability (Corwin and Lesch, 2005a). The character-
ization of spatial variability using EC
a
measurements is based on the hypothesis that spatial EC
a
information can be used to develop a directed soil sampling plan that identifies sites that adequately
reflect the range and variability of soil salinity and other soil properties correlated with EC
a
. This
hypothesis has repeatedly held true for a variety of agricultural applications (Corwin, 2005; Corwin
and Lesch, 2003, 2005a, 2005c, 2005d; Corwin et al., 2003a, 2003b; Johnson et al., 2001; Lesch
et al., 1992, 2005).
The EC
a
measurement is particularly well suited for establishing within-field spatial variability
of soil properties because it is a quick and dependable measurement that integrates within its mea-
surement the influence of several soil properties that contribute to the electrical conductance of the
bulk soil. The EC
a
measurement serves as a means of defining spatial patterns that indicate differ-
ences in electrical conductance due to the combined conductance influences of salinity, θ, texture,
and ρ
b
. Therefore, maps of the variability of EC
a
provide the spatial information to direct the selec-
tion of soil sample sites to characterize the spatial variability of those soil properties correlating,
either for direct or indirect reasons, to EC
a
.
The characterization of the spatial variability of various soil properties with EC
a
is a conse-
quence of the physicochemical nature of the EC
a
measurement. Three pathways of current flow
contribute to the EC
a
of a soil: (1) a liquid phase pathway via dissolved solids contained in the soil
water occupying the large pores, (2) a solid-liquid phase pathway primarily via exchangeable cat-
ions associated with clay minerals, and (3) a solid pathway via soil particles that are in direct and
continuous contact with one another (Rhoades et al., 1989, 1999a). These three pathways of current
