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April 2014
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
ability traditionally missed at coarser scales. Topography
controls water redistribution on the landscape, which in turn
controls pedogenesis over geologic time and subsequent soil
distribution across a landscape. These scientific concepts
are not new to soil resource inventories. However, data such
as lidar and the other aforementioned tools provide spatial-
ly explicit representations of soils and soil processes in a
quantifiable format. Digital soil mapping processes quantify
and capture soil patterns determined by topography, par-
ent materials and other soil forming factors (Jenny, 1941,
McBratney et al., 2003) and package this information in a
digital format for computer based applications. Integration
of remotely sensed data, delivery technology and conceptual
scientific understanding improves soil resource management.
Critical to successful understanding and application of re-
motely sensed data is an understanding of physical architec-
ture and soil processes that underpin observed and predicted
data. Such a fundamental context empowers interpretation
of remotely sensed data. The interplay of geomorphology,
stratigraphy, pedology, hydrology, and vegetation deter-
mines soil geography and functions (Jenny, 1941; McBratney
et al., 2003; Wysocki et al, 2012). These attributes can be
evaluated in sequence from high to low positions on land-
scapes and expressed as soil systems (Daniels et al., 1999).
Soil systems are groups of widely recurring catenas or soil
sequences and constitute the architecture through which
soil and ecosystem processes operate. Soil systems represent
the missing link that can bridge digital information across
scales. Conceptually and quantitatively they connect point
data to area data, and each soil individual to its neighbors.
Soil systems, complemented by remotely sensed data, allow
for up-scaling of soil and landscape dynamics to provide
seamless, quantitative representations of direction, mag-
nitude or timing of energy or materials movement within
soils (or the extent to which they are retained) (Plate 1).
Proximal Sensing of Soil Properties
Proximal sensing of soil using diffuse reflectance spectroscopy
(DRS) has increased our ability to estimate the spatial extent
and variability of selected soil properties under diverse land
management conditions. These advances have allowed the cre-
ation and expansion of digital spectral libraries and use of com-
plex statistical models for characterizing soils, while reducing
the considerable expense of traditional field-based soil inves-
tigations. Use of spectrometers to measure reflected radiation
from soil samples has been demonstrated to be a lower-cost,
less precise estimation alternative to direct measurements of
chemical and physical properties of soils, where large numbers
of observations are required to characterize an area or where
the cost of field survey and laboratory analysis is high. Dif-
fuse reflectance spectroscopy also allows for the rapid and
nondestructive prediction of a wide spectrum of soil properties
critical to addressing sustainable land management objectives
(Bowers and Hanks, 1965; Baumgardner et al., 1985; Viscarra
Rossel et al., 2010) (Plate 2).
Plate 1
. LIDAR derived Digital Elevation Model (DEM), terrain attributes (Altitude Above Channel Network and
Topographic Wetness Index) derived from DEM and soil depth generated from the relationships between soil land-
scape-terrain attributes.
