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| Titel |
Mustiscaling Analysis applied to field Water Content through Distributed Fiber Optic Temperature sensing measurements |
| VerfasserIn |
Javier Benitez Buelga, Leonor Rodriguez-Sinobas, Raúl Sánchez, María Gil, Ana M. Tarquis |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250100957
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| Publikation (Nr.) |
 EGU/EGU2014-16980.pdf |
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| Zusammenfassung |
| Soils can be seen as the result of spatial variation operating over several scales. This
observation points to “variability” as a key soil attribute that should be studied. Soil
variability has often been considered to be composed of “functional” (explained) variations
plus random fluctuations or noise. However, the distinction between these two components is
scale dependent because increasing the scale of observation almost always reveals structure in
the noise. Geostatistical methods and, more recently, multifractal/wavelet techniques have
been used to characterize scaling and heterogeneity of soil properties among others coming
from complexity science.
Multifractal formalism, first proposed by Mandelbrot (1982), is suitable for variables with
self-similar distribution on a spatial domain (Kravchenko et al., 2002). Multifractal analysis
can provide insight into spatial variability of crop or soil parameters (Vereecken et al., 2007).
This technique has been used to characterize the scaling property of a variable measured
along a transect as a mass distribution of a statistical measure on a spatial domain of the
studied field (Zeleke and Si, 2004). To do this, it divides the transect into a number of
self-similar segments. It identifies the differences among the subsets by using a wide range of
statistical moments.
Wavelets were developed in the 1980s for signal processing, and later introduced to soil
science by Lark and Webster (1999). The wavelet transform decomposes a series; whether
this be a time series (Whitcher, 1998; Percival and Walden, 2000), or as in our case a series of
measurements made along a transect; into components (wavelet coefficients) which describe
local variation in the series at different scale (or frequency) intervals, giving up
only some resolution in space (Lark et al., 2003, 2004). Wavelet coefficients can
be used to estimate scale specific components of variation and correlation. This
allows us to see which scales contribute most to signal variation, or to see at which
scales signals are most correlated. This can give us an insight into the dominant
processes
An alternative to both of the above methods has been described recently. Relative entropy
and increments in relative entropy has been applied in soil images (Bird et al., 2006) and in
soil transect data (Tarquis et al., 2008) to study scale effects localized in scale and provide the
information that is complementary to the information about scale dependencies found
across a range of scales. We will use them in this work to describe the spatial scaling
properties of a set of field water content data measured in an extension of a corn field,
in a plot of 500 m2 and an spatial resolution of 25 cm. These measurements are
based on an optics cable (BruggSteal) buried on a ziz-zag deployment at 30cm
depth.
References
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analysis of soil pore-scale images. J. Hydrol. 322:211–219.
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