Remote Sensing of Environment 80 (2002) 406 – 417
www.elsevier.com/locate/rse
Field-derived spectra of salinized soils and vegetation as
indicators of irrigation-induced soil salinization
R.L. Dehaan, G.R. Taylor*
School of Geology, University of New South Wales, Sydney, NSW 2052, Australia
Received 30 October 2000; received in revised form 11 September 2001; accepted 3 October 2001
Abstract
Salinization is a major cause of soil degradation in the Murray – Darling Basin of Australia. The objective of this research is to evaluate
the utility of field-derived spectra of saline soils and related vegetation for characterizing and mapping the spatial distribution of irrigationinduced soil salinization. A FieldSpec FR hand-held spectrometer was used to measure the spectra of a range of salinized soils and
associated vegetation. Strategies for mapping field-derived spectra using hyperspectral (HyMap) imagery were assessed, and a continuumremoved Spectral-Feature-Fitting (SFF) approach adopted. Field-derived spectra of the vegetation comprising of samphire, sea blite, and
native grass species are also useful indicators of salinization; however, their absence is not necessarily an indicator of healthy soils.
Distribution maps created using the SFF method and a restricted wavelength range of field-derived spectra provide an accurate record of the
distribution of both vegetation and soil indicators of salinization at the time of image acquisition. Salinized soil and vegetation indicator
class maps show a similar spatial distribution to soil salinization as mapped by ground-based geophysical surveys. D 2002 Elsevier Science
Inc. All rights reserved.
1. Introduction
Salinization and soil degradation occur in areas where
saline groundwaters are elevated to where they approach the
ground surface and evaporation exceeds precipitation. In
irrigated areas, where the water table approaches the ground
surface, salt accumulation occurs in areas known as discharge zones. Capillary rise of saline groundwater causes
the direct precipitation of evaporite minerals at the surface
in these zones. Salinization may also occur when salts are
concentrated in soils by the evaporation of freestanding
irrigation water. The destruction of primary clay minerals
by the interaction of salt-bearing water with which they are
in disequilibrium also occurs (Jankowski and Ackworth,
1999, personal communication) The research described in
this contribution was undertaken within irrigated land in the
Murray– Darling Basin (MDB) of southeast Australia, but
salinization associated with irrigation is a worldwide problem. In the MDB, it is estimated to cost Australia at least
US$200 million (1987) annually in lost agricultural produc-
* Corresponding author. Fax: +61-2-9385-5935.
E-mail address: [email protected] (G.R. Taylor).
tion (Ghassemi, Jakeman, & Nix, 1995) with the Kerang
and Shepperton areas responsible for 75% of the total
productivity losses (Chartres, 1987). The major causes are
a combination of poor land management, the removal of
trees for agriculture, and crude irrigation practices. The
effect of these practices has been to raise the water table
to where saline groundwater interacts with the root zone of
trees and crops. This causes changes in vegetation cover and
ultimately loss of vegetation and agricultural productivity.
The key to successfully managing the salinization problem
is the early recognition of salinized soils, the implementation of methods to combat incipient salinization such as
improved irrigation, drainage, and farming practices, and
the monitoring of salinized land on a regular basis.
Conventional techniques for identifying and monitoring
salinized land include the measurement of water table levels
in boreholes, ground-based geophysics, measurements of
soil electrical properties using soil pastes and water extracts,
EC1:5 (Van Der Lelij & Poolman, 1989), and the visual
identification of salinization and degradation. Ground-based
electromagnetic (EM) measurements of soil electrical conductivity are generally accepted as the most effective
method for rapid acquisition and quantification of soil
salinization (Norman, Lyle, Heuperman, & Poulton, 1989).
0034-4257/01/$ – see front matter D 2002 Elsevier Science Inc. All rights reserved.
PII: S 0 0 3 4 - 4 2 5 7 ( 0 1 ) 0 0 3 2 1 - 2
R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417
Fig. 1. Location of Pyramid Hill. The boundary of the MDB is shown.
These methods are time-consuming and expensive, and so
efforts are being made to develop more cost-effective
methods of mapping soil salinization. These include airborne EM techniques (Street & Anderson, 1993), airborne
radar (Taylor et al., 1996), and hyperspectral remote sensing
techniques (Dehaan & Taylor, in press). In this paper, we
describe the spectral features in the 400 to 2500 nm range of
field-derived spectra of selected soils and vegetation from
within the Tragowel Plains Research and Development
Block (R&D Block) (Fig. 1). These are used to develop a
strategy for the use of field-derived spectra for the mapping
of salinized soils from HyMap airborne scanner imagery.
1.1. Background studies
The potential of remote sensing to identify and monitor
soil salinization has been studied using satellite imagery, such
as Landsat Thematic Mapper (TM). The low spectral resolution of Landsat TM data, and the use of traditional classification techniques, requiring ground studies in numerous
training areas, have meant that the use of this type of imagery
has been largely unsuccessful (Fraser & Joseph, 1998).
Hyperspectral data allow for the quantitative assessment
of endmember abundances on a pixel-by-pixel basis (Sabol,
Adams, & Smith, 1992), and hence, interpretative techniques differ greatly from those traditionally applied to
satellite data. Hick and Russel (1990), using Geoscan — a
24-channel instrument that approached hyperspectral
imaging capabilities, concluded that vegetation vigour,
as shown by near-infrared reflectance (NIR), is the best
indicator of the impacts of increasing soil salinization. They
also showed the importance of minor constituents, such as
MgCl2, as contributors to the spectral signature of salinized
soils. Taylor, Bennett, Mah, and Hewson (1994) showed
how principal components analysis of Geoscan imagery
allowed the qualitative mapping of soil salinization at
Pyramid Hill, Victoria.
Chapman, Rothery, Francis, and Pontual (1989), Crowley (1991), and Drake (1995) describe the spectral features
407
of a number of evaporite minerals from natural playas.
Crowley showed that many saline minerals exhibit diagnostic near-infrared absorption bands, chiefly attributed
to vibrations of hydrogen-bonded structural water molecules, and that visible, NIR spectra can be used to detect
minor hydrate phases within mineral mixtures dominated by
halite. Drake, op. cit., quoting Hunt, Salisbury, and Lenhoff
(1971), describes gypsum’s absorption features, which
occur at around 1000, 1200, 1400, 1600, 1740, 1900, and
2200 nm, as being due to combinations of O – H stretches,
H – O – H bending fundamentals, and various overtones. The
various hydrated chlorides and sulfates of sodium, potassium, calcium, and magnesium also show these absorption
features, but variation in the way the water is held within the
crystal lattice causes subtle but consistent differences in
absorption feature position. USGS library spectra (Clark,
Swayze, Gallagher, King, & Calvin, 1993) of some of these
hydrated chlorides and sulfates are shown in Fig. 2a.
Crowley (1993) showed that hyperspectral images collected
by the Airborne Visible/Infrared Imaging Spectrometer
(AVIRIS) could successfully identify and map hydrated
chlorides and sulfates in playas in western US.
1.2. The HyMap spectrometer
The HyMap Imaging System first flew in 1997 and,
having 128 channels, is Australia’s first true hyperspectral
imager. The HyMap instrument can be configured to give a
variety of spectral and spatial resolutions, but generally,
bandwidths vary between 10 and 20 nm across the spectral
range and spatial resolutions vary from 3 to 10 m according
to aircraft height. The instrument fits into any light, twinengine aircraft fitted with a standard camera port and is built
by Integrated Spectronics. The HyMap instrument has a
high signal-to-noise ratio of > 500:1 (Cocks, Jenssen, Stewart, Wilson, & Shields, 1998).
1.3. Interpretation strategies
The high spectral resolution of hyperspectral data sets
allow spectra to be unmixed with a higher accuracy and
enables the separation of components (such as senesced
vegetation and soils) that are difficult to separate with
broadband sensors (Sabol et al., 1992). A comprehensive
analysis of the materials contributing to the spectral signature of each pixel can therefore be determined. Fieldderived or image-derived endmembers can be used to unmix
these spectral components from hyperspectral imagery
(Adams, Smith, & Gillespie, 1993). The acquisition of field
spectra has disadvantages in that additional expensive field
spectrometers and visits to the field are required. However,
the field of view of the field instrument is often smaller than
the Instantaneous Field of View (IFOV) of an airborne
instrument. This allows minor ground components to be
identified more readily. Soils are mixtures of mineral- and
vegetation-derived constituents. These constituents, and the
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R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417
Fig. 2. (a) USGS library reflectance spectra of various salt species; (b) field-derived reflectance soil terrain element spectra; (c) continuum-removed soil
terrain element spectra; (d) field-derived reflectance vegetation terrain element spectra (values offset for clarity); (e) continuum-removed vegetation terrain
element spectra.
degree of crystallinity of the clay mineral components,
demonstrated later, will vary in abundance according to
the degree of salinization. Soil spectra and chemistry (also
described later) suggest that salinization and spectral prop-
R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417
erties vary continuously between two extreme endmembers,
i.e., highly saline salt scalds and nonsaline bare soil.
However, the spatial distribution of salinity is such that
large areas have commonly reached a uniform level of
salinity and hence map as intermediate soil types at all
scales of investigation. In this study, we will use the term
‘‘terrain elements’’ to describe both homogenous areas of
common soil or vegetation cover that have distinctive
spectral properties at the scale measured by the field
spectrometer and also are sufficiently abundant that their
distribution can be mapped by field or hyperspectral techniques. A terrain element is an intimate mixture of soil
minerals or vegetation species and thus is not likely to be a
pure endmember in the classic sense.
Image-derived endmembers will fail to include minor
terrain elements that never reach abundance levels such that
they dominate an entire pixel. These may still be significant
indicators of salinization. Field-derived spectral endmembers may therefore be more effective at characterizing
differences in the degree of soil salinization. In this paper,
we measure field-derived spectra of selected saline soil and
associated vegetation terrain elements and determine the
best way of using these to map salinized land with HyMap
hyperspectral imagery.
Spectral absorption features are often mapped best if the
analysis is limited to a restricted spectral range that encompasses just the absorption features of interest (Taylor et al.,
1994). Continuum removal is the normalization of reflectance spectra using common spectral reference points to
create a continuum spectrum, then continuum-normalizing
the spectrum by dividing the original by the continuum.
This is done for each spectrum and, in the case of imagery, it
is done for each pixel spectrum. Continuum removal is a
decorrelation technique that maximizes the effects of spectral absorption features. We therefore also investigated
whether field-derived terrain element spectra are best mapped using the entire range of bands available or using
restricted parts of the spectral range encompassing specific
absorption features and whether spectra are best mapped
using reflectance or continuum-removed data.
We also evaluate several mapping methods including
Spectral Angle Mapper (SAM) (Kruse et al., 1993), Matched
Filtering (MF) (Boardman, Kruse, & Green, 1995; Harsanyi
& Chang, 1994), and Spectral-Feature-Fitting (SFF) (Clark
& Swayze, 1995). SAM is an automated method for comparing reference spectra to image spectra. This method
calculates the ‘‘spectral angle’’ between the reference and
image spectra by treating them as vectors in n-dimensional
space with dimensionality equal to the number of bands. It
ranks the similarity of reference to image spectra, scoring
similar spectra highly. MF allows the abundance of endmembers to be determined without a knowledge of the
remaining endmembers. SFF is an absorption feature-based
method that matches image spectra to reference spectra using
a least squares technique and continuum-removed data.
Absorption feature depth, relating to material abundance, is
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output in one image, while in a second image, the root mean
square error is calculated using a least squares technique.
Linear unmixing (Adams, Smith, & Johnson, 1986; Gillespie
et al., 1990) was not assessed. This method requires all
endmembers to be present, and without knowledge of all the
endmembers, unmixing results are likely to be spurious.
1.4. Test site description
The Tragowel Plains agricultural region is located within
the MDB, near the town of Pyramid Hill. Salinization, crop
testing, and remediation measures for salinized environments have been studied at the Tragowel Plains R&D Block
by the Victorian Department of Agriculture since 1987.
Irrigation on the R&D Block is mostly used to support crop
cultivation and crop trials, with some annual and perennial
pasture grown for sheep and cattle grazing. Vegetation, soil
conductivity measured with a Geonic EM38 instrument, and
depth to the water table are routinely monitored, making the
site an excellent location for the testing of salinity mapping
techniques. The location of the R&D Block is shown on all
distribution maps as a superimposed line drawing that
represents a total of 130 ha within the total image area.
Terrain element distribution maps shown in this paper are
image swaths oriented approximately northwest (top) to
southeast (bottom) with a swath width of 2.5 km.
Soils on the R&D Block are developed upon fluviolacustrine sands, silts, and clays belonging to the Shepparton
Formation (Macumber, 1991). The soils on the R&D Block
form a red/brown sandy loam; however, in salinized areas, the
soil’s color changes to grey. Rising water tables first intersect
the ground surface in areas of atypical low relief (Norman et
al., 1989), which on the R&D Block coincide with a series of
palaeo channels. In these palaeo channels, efflorescing salts
form white scalds and crusts, and hence, the areal expression
of salinized areas resembles abandoned stream channels.
1.5. Sample and image acquisition and preprocessing
A hyperspectral image was acquired by the HyMap
spectrometer over the Pyramid Hill region in May 1999.
The HyMap spectrometer was flown so as to give 5 m IFOV
for a data swath width of 512 pixels, providing a ground
swath width of approximately 2.5 km. Spectral data were
acquired in 128, approximately 20 nm, bands covering the
spectral range 400– 2500 nm. A dark current correction was
applied to the data. There was little systematic variation in
reflectance across the imagery, and hence, no corrections for
across-track, path –length differences or shadowing were
applied. Images were not registered to geographic coordinates, and therefore, all terrain element distribution maps
presented here still contain minor geometric distortions.
Calibration of the HyMap images was carried out using
field spectra of appropriate bright and dark targets (tar seal,
galvanized metal roofing, standing water, and healthy green
grass) using the methods of Roberts, Yamaguchi, and Lyon
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(1986), implemented in the Empirical Line method in the
Environment for Visualizing Images (ENVI), Better Solutions Consulting (1993 – 1999). Calibrated imagery and
spectra viewed in ENVI show that the spectra of various
objects closely resemble those expected from field and
library spectra.
A FieldSpec FR hand-held spectrometer manufactured
by Analytical Spectral Devices, and having a spectral
resolution of 1 nm, was used to acquire reflectance spectra
in the range 350– 2500 nm of in situ soils and vegetation.
This occurred 2 weeks after image acquisition. Instrument
calibration and optimization were carried out between each
spectral measurement using a Labsphere spectralon panel as
a white reference. Spectra were collected using the accessory reflectance probe with its internally controlled light
source. The field of view was restricted to 25 mm for all
measurements. Five spectral measurements were made for
each vegetation type by placing sufficient leaf matter on a
flat surface so as to cover the entire 25 mm field of view.
Spectra of soils were measured in situ. Spectra and associated soil samples were acquired in areas where salinization
was most blatant and along a series of measured traverses
across the R&D Block. Delineation of these salinized areas
was carried out using 1999 EM38 soil salinization maps
provided by staff from the R&D Block.
Forty-one soil samples collected from the R&D Block
were air-dried and powdered. EC1:5 water extracts, made
by adding 25 g of deionized water to 5 g of each sample,
were used to measure electrical conductivity, using a
TPS900C Conductivity Salinity Meter, and as material for
mineralogical analysis using conventional X-ray diffraction
(XRD) techniques.
2. Results
2.1. Spectral properties of soils
The field spectra of four representative soil samples
collected from the R&D Block illustrate the effects of
varying degrees of soil salinization. Reflectance and continuum-removed spectra of a salt crust (EC1:5 of 51,800 mS/
cm), highly salinized soil (EC1:5 of 19,780 mS/cm), moderately salinized soil (EC1:5 of 9710 mS/cm), and a low
salinized soil (EC1:5 of 2430 mS/cm) are shown in Fig. 2b
and c, respectively.
These spectra show absorption features at 505, 920,
1415, 1915, and 2205 nm. The salt scald and highly salinized soil show additional distinct absorption features at
680, 1180, and 1780 nm. The four spectra are similar to one
another with hydroxyl features at approximately 2200 nm
becoming less developed as samples become more salinized. The reduction of the 2200 nm absorption feature
intensity seen in the more salinized soils may be a result of a
loss of crystallinity in the clay minerals (Fraser, Camuti,
Huntington, & Cuff, 1990). A reflectance high at 800 nm
becomes more prominent, and the overall slope of the curve
between 800 and 1300 nm decreases as samples become
more salinized. This decrease in slope between 800 and
1300 nm parallels changes in soil color from red to grey and
could be due to either changes in oxidation state of the iron
present (Hunt & Ashley, 1979) within the soils or due to
variations in the evaporite mineral content within the soils.
The deeper uncombined water features at 1415 and 1915 nm
broaden and become more asymmetrical as salinization
increases, suggesting an increase in soil moisture content
(Bowers & Hanks, 1965) with increasing salinization. The
increase in depth and complexity of the absorption features
at around 680, 920, 1180, and 1780 nm resembles features
described by Crowley (1991) and Drake (1995) as being due
to hydroxyl ions and water within the lattice of various
hydrated evaporite minerals.
Forty-one soil spectra were examined. Only soils with an
EC1:5 value greater than 9710 mS/cm (6214 ppm of total
salts) showed the hydrate-related absorption feature at 1180
nm. The majority of soils from salt crusts and highly
salinized areas had salt efflorescence at the surface and
EC values ranged from 52,000 to 15,000 mS/cm, while the
remaining soil samples collected from low salinized areas
had EC values of less than 3000 mS/cm. The less salinized
soil samples do not show the complex, hydrate-related,
absorption features but often possess a well-defined, narrow,
and deep hydroxyl feature at 2200 nm suggestive of wellcrystallized clay minerals in the soils. The spectra of the salt
crust and highly salinized soil shown in Fig. 2b and c are
consistent with the features that would be shown by damp
halite (NaCl) with minor amounts of hydrated sulfate
minerals such as bassanite (2CaSO 4H2O), polyhalite
(K2Ca2Mg(SO4)42H2O), and gypsum (CaSO42H2O). It is
likely that the evaporite materials present in salinized crusts
and scalds are mostly sodium chloride (halite) with minor
amounts of various sodium, potassium, calcium, and magnesium sulfates. The surface efflorescence is ephemeral due
to its sensitivity to changes in water table height and recent
climate, making exact determintion of composition and
hydration state of the salts involved difficult.
2.2. Chemical properties of soils
Groundwater analyses provided by Pyramid Hill Salt
show that the groundwater at Pyramid Hill is similar in
composition to modern seawater and contains as major
constituents dissolved sodium, potassium, magnesium, calcium, chloride, and sulfate. Salt crusts and soils containing
hydrated chlorides and sulfates are therefore feasible and
XRD and X-ray fluorescence (XRF) techniques were utilized to assist in the identification of such minerals.
Forty-one soil samples collected from the R&D Block
were crushed in a tungsten carbide bowl using a Rocklabs
Tema mill and oven-dried. Splits of these homogenized
samples were then examined using a PW 1830 XRD
Spectrometer. Cluster analysis of chemical analyses deter-
R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417
mined by XRF shows that salinization involves the addition
of chloride, sulfate, sodium, and calcium ions with minor
amounts of other elements.
XRD determinations of all soil samples show that they
contain quartz, illite, and kaolinite. The more salinized
samples also contain halite and traces of anhydrite, gypsum,
polyhalite, bassanite, and bloedite. Oven drying prior to
analysis by XRD has possibly changed some of the hydrate
mineralogy, and hence, more elaborate methods of sample
collection, preparation, and analysis are required to more
confidently confirm the mineralogy of these soils.
2.3. Spectral properties of halophytic vegetation
The appearance of salt-tolerant (halophytic) plants, in
conjunction with the disappearance of salt-sensitive plants,
is one of the earliest and most recognizable signs of
salinization. Halophytic plants have usually developed strategies for survival in saline environments that give them
distinctive leaf and stem structures (Larcher, 1980). Fig. 2d
and e, respectively, show field-derived reflectance and
continuum-removed spectra for the most abundant halophytic vegetation at the time of image acquisition. Samphire
(Halosarcia pergranulata), a dense succulent shrub with
numerous woody stems and short, segmented branchlets, is
characterized by having two reflectance peaks at 555 and
624 nm due to the presence of accessory pigments, a
reflectance minimum at 680 nm due to chlorophyll absorption, a distinctive slope to the infrared reflectance plateau
between 1250 and 1400 nm, and absorption features at 985,
1188, 1440, and 1925 nm due to absorption by liquid water
contained in the plant’s tissue. The green peak predominates, but there is also a small peak in the red. This is
attributed to the varying colors exhibited by this species’
segmented branchlets, which are predominantly green but
under certain seasonal and saline conditions may also
appear yellow and red.
A second halophyte, sea blite (Sueda australis), a straggly shrub with small, narrow, succulent leaves that are
commonly a purple/red and to a lesser extent light green
color, is characterized by a strong peak in the red at 640 nm,
a small chlorophyll absorption feature at 680 nm, and a red
edge that tapers between 740 and 920 nm towards the NIR
high due to cell structure differences. Water absorption
features similar to those of samphire described above are
also present. The unusual spectral properties of both plants
are thought to be due to their predominantly, fleshy, waterrich leaves and distinctive pigments. Both plants often occur
in small clumps of 1 –2 m width and therefore comprise
mappable terrain elements. Sea barley grass (Critesion
marinum) measured in the field at the R&D Block has a
typical dry grass spectrum with prominent ligno-cellulose
bands and water bands at 1450, 1720, 1920, and 2080 nm.
The spectrum shows a uniform increase in reflectivity from
400 to 1320 nm, indicating the absence of chlorophyll
absorption (Elvidge, 1990). Sea barley grass occurs in
411
patches up to several meters across and therefore also
comprises a mappable terrain element.
The field spectra of halophytic plants, round-leaf wilsonia (Wilsonia rotundifolia) — a small perennial, with
mat-forming stems and small succulent leaves, commonly
green and yellow; the native environmental weed water
buttons (Cotula coronopifolia) — a semisucculent plant
with jagged leaves, stems, and yellow dome-shaped
flower; the native plant creeping saltbush (Altriplex semibaccata); and introduced weed bathurst burr are also
shown in Fig. 2d and e. The spectral features of these
plants are distinctive in the visible and NIR, but these
species are not sufficiently abundant to be mappable from
imagery. Fig. 2d and e also show field-derived reflectance
and continuum-removed spectra of cultivated mixed rye
and clover pasture vegetation. These spectra show simple
green vegetation spectra with deep chlorophyll absorption
features on either side of the green peak at 450 and 680
nm, a nearly flat infrared reflectance high between 1250
and 1450 nm, and water absorption features at 1000, 1450,
and 1950 nm.
3. Mapping of salinization indicators
Mapping methods evaluated include SFF, MF, and
SAM. Terrain element distribution maps were created using
the whole wavelength range, selected wavelength ranges,
continuum-removed, and reflectance data for all selected
spectra. The Farm Manager, Bill Elder, provided us with
independent assessments of vegetation species at the time
of image acquisition on a paddock-by-paddock basis;
however, no maps that showed their within-paddock spatial
distribution were available. We confirmed the spatial distribution of halophytic vegetation shortly after imaging and
made a subjective comparison of actual halophytic vegetation ground cover with that identified by field spectra using
HyMap imagery. SFF terrain element maps created using
restricted wavelengths were the most similar to the subjective assessments of actual ground cover. They showed
good results for both the high and moderate indicator
classes of salinization, as determined from the ground
geophysics, in the classification accuracy assessment (see
Salinization Indicator Comparisons), and so discussions
from here are limited to mapping results achieved by the
SFF method. All terrain element distribution maps were
enhanced by examining the image histogram, identifying
the break in slope, and stretching the image to only show
the SFF scores above the background population for each
terrain element spectra.
3.1. Soil terrain element distribution maps
SFF terrain element distribution maps created from
field-derived soil spectra of the salt crust, highly salinized
soil, and low salinized soil, and utilizing selected wave-
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lengths over the VNIR and SWIR parts of the spectrum,
are presented in Fig. 3a –c. The salt crust class occurs in
areas where salt scalds have developed and occurs coincident to the edges of regional palaeo channels. These
palaeo channels are utilized for surface drainage as part of
a farm management plan and represent the strongest
expressions of salt efflorescence. The highly salinized soil
class occurs in areas adjacent to where the salt crust class
occurs and represents soils that are equally salinized, but
lack surface salt efflorescence. Both spectra show shallow
Fig. 3. SFF terrain element distribution maps. All distribution maps presented here are image swaths oriented approximately northwest (top) with a swath width
of 2.5 km. The R&D Block is shown as line drawing superimposed on all maps. (a) Salt crust distribution; (b) highly salinized soil distribution; (c) low
salinized soil distribution; (d) samphire distribution; (e) sea blite distribution; (f) sea barley grass distribution.
R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417
and broad clay absorption features at 2200 nm, indicating
a poor clay structure (Fraser et al., 1990). The low saline
soil class map can be reconciled to areas of only slight
salinization based on results derived from ground geophysics, and coincides with areas laid bare by farming operations such as ploughing.
Field-derived spectra of bare, unsalinized soils were not
collected from the R&D Block and are not described in this
paper. However, Dehaan and Taylor (in press) demonstrated
that image-derived endmembers of nonsalinized soils,
including bare ploughed paddocks, irrigated ploughed soil,
and a foreign roadbase of goethitic/kaosmectite clay, can be
directly identified from the HyMap imagery and successfully mapped in the R&D Block.
3.2. Halophytic vegetation terrain element
distribution maps
Samphire and sea blite occur only in areas where the soil
is badly salinized and therefore are useful indicators of
salinization. They are often closely associated but not
universally so. Their terrain element distribution maps are
shown in Fig. 3d and e and provide a good representation of
the spatial distribution of these halophytes at the time of
image acquisition. Distribution maps for sea barley grass are
413
shown in Fig. 3f and can be reconciled to soils that are
moderately salinized but that also contain several other
species of grasses (spectra not collected) including tall
wheat grass, windmill grass, and hill wallaby grass. Native
grasses have been mapped in areas associated with salinization; however, some nonsalinized areas also contained these
same species. Therefore, grasses should be used cautiously
as indicators of salinization because their distribution is
controlled by cropping and grazing practices in addition to
their salt tolerance.
3.3. Salinization indicator comparisons
The Victorian Government acquired EM38 data covering
the R&D Block shortly after HyMap image acquisition.
These results have allowed us to test the validity of the
hyperspectral results. A synthetic true color composite,
comprised of HyMap Bands 3, 9, and 15 (455, 545 and
638 nm) displayed as blue, green, and red, is illustrated in
Fig. 4a. This can be compared with a three-color image of
the distribution of the three most significant indicators of
high salinization illustrated in Fig. 4b. The terrain element
spectra used to derive the salinized land indicator map are
from a highly salinized soil and the two halophytes samphire and sea blite.
Fig. 4. (a) Synthetic true colour composite of HyMap imagery using Bands 3, 9, and 15. (b) Distribution of three salinized land terrain element indicators,
highly salinized soil, samphire, and sea blite. Colored areas in this diagram represent areas that are salinized. Mixtures of the terrain element indicators are
shown by the cyan and yellow colors.
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Fig. 5. 1999 EM data in dS/m (solid thick line); EM values are multiplied by five for clarity. SFF scores as DN values for the highly salinized soil class (thin
solid line) and averaged SFF scores (dashed thick line) for an X – Y profile across the R&D Block.
A traverse across the R&D Block showing EM38
results and the derived SFF scores for the high salinized
soil class are presented in Fig. 5. The EM38 data were
acquired using a coarse sampling grid of 60 m between
each measurement. The HyMap-derived data include 250
readings over the same distance and therefore show
considerably more detail and variability over the same
measured traverse. The SFF scores for the highly salinized soil class are also illustrated after averaging to the
same spatial resolution as the ground geophysics (dashed
thick line in Fig. 5). Hyperspectral-derived scores for
the highly salinized soil terrain element show an almost
identical pattern to the conductivities achieved by
ground geophysics.
Fig. 6. The best classification results for each mapping method: (a) SFF, (b) MF, and (c) SAM. Black areas represent unclassified; white areas represent high
salinization class; grey areas represent moderate salinization. (b) 1999 EM data with the location of the X – Y profile used in Fig. 5 illustrated.
R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417
415
Table 1
Accuracy assessment results for the best classifications achieved using the mapping methods SFF, SAM, and MF
Overall accuracy
SFF
62.4%
20,305/32,533
SAM
70.1%
22,822/32,533
MF
55.1%
17,930/32,533
Ground truth
High indicators
Moderate indicators
High indicators
Moderate indicators
High (EM)
Moderate (EM)
Total
High (EM)
Moderate (EM)
Total
High (EM)
Moderate (EM)
Total
73.5
12,206
26.5
4394
49.2
7834
50.8
8099
61.6
20,040
38.4
12,493
95.5
21,087
4.5
985
83.4
8726
16.6
1735
91.6
29,813
8.4
2720
84.9
14,569
15.1
2592
78.1
12,010
21.9
3362
81.7
26,579
18.3
5954
Commission
Omission
Commission
Omission
Commission
Omission
39.09
7834/16,600
35.17
(4394/15,933)
26.47
4394/16,600
49.17
7834/15,933
29.27
8726/22,072
36.22
985/1735
4.47
985/22,072
83.41
8726/1735
45.19
12,010/17,161
43.54
2592/15,372
15.10
2592/17,161
78.13
12,010/15,372
Accuracy assessment results as percentages in bold and as the number of pixels in italics.
An error or confusion matrix is an effective way of
representing mapping accuracy. The accuracy of each category is plainly described along with both errors of inclusion (commission errors) and errors of exclusion (omission
errors) present in the classification (Congalton, 1991).
Errors of inclusion represent those pixels that belong to
another class but are labelled as belonging to the class of
interest. Errors of exclusions represent those pixels that
belong to the ground truth class that have not been classified
into the proper class. Accuracy assessments were made
using the confusion (contingency) matrix implemented in
ENVI. HyMap-derived classification maps and classified
EM38 data covering the R&D Block were registered to
geographic coordinates. EM38 data were then merged into
two classes — high salinization ( >8.6 dS/m) and moderate
salinization (3.8 – 8.6 dS/m). Terrain element classes were
also combined into two representative classes. The samphire, seablite, salt crust, highly and moderately salinized
soil classes were merged into a high salinization class, and
the sea barley and low salinized soil class were merged to
form a moderate salinization class. The best classification
results for each of the mapping methods SAM, MF, and
SFF are shown in Fig. 6 along with the classified 1999
EM38 data. Accuracy assessments for each of these classifications are shown in Table 1. The SAM method achieved
the highest overall accuracy; however, most of this accuracy
is attributed to the high salinization class, which showed
errors of exclusion (omission errors) of only 4.47%. The
moderate salinization class showed a high error of exclusion
(omission errors) of 83.41%. Classifications achieved using
the SFF mapping method and selected wavelength ranges
show modest agreement (62%) with the classes mapped by
the 1999 EM38 data. The SFF method, while not achieving
the highest overall accuracy, performed the best in that both
classes were classified moderately well shown by the
overall lower errors of exclusion (omission errors). Similar
errors of inclusion (commission error) for both methods
were observed.
Visual comparison of the two data sets suggests that poor
accuracies were probably due to our failure to correctly
identify all significant terrain element components such as
other dry grasses, nonsalinized soils, and crops such as
soybeans and lucerne. However, these results demonstrate
that hyperspectral data have the ability to show the extent of
salinization in both a spatial and semiquantitative sense.
4. Discussion
Soil salinization is caused by a number of factors — the
most significant of which is the rise of saline groundwater to
where it approaches the ground surface. Capillary rise
causes the direct precipitation of saline minerals in surface
soils. Local topography, rainfall history, seasonal effects,
drainage, farming practices, soil composition, and vegetation cover greatly influence the extent to which the effects of
salinization are observed at the ground surface. Indicators of
increasing salinization at the surface are therefore likely to
be varied because of these factors.
EM methods measure soil conductivity properties, while
optical remote sensing methods measure the spectral properties of surface materials. A high level of agreement
between ‘‘salinization’’ maps produced by the two methods
was always unlikely since each method measures the effects
of salinization in different ways. Components such as
cultural features (dams and drains) and paddocks with crops
are not separately mapped in the EM data, whereas HyMap
imagery is likely to exclude these components because their
spectral properties will differ from the indicator terrain
element spectra being mapped. The different spatial resolutions of the EM38 (60 m) data and the HyMap imagery
(IFOV of 5 m) used in this study may have also contributed
416
R.L. Dehaan, G.R. Taylor / Remote Sensing of Environment 80 (2002) 406–417
to the modest results in our accuracy assessment, as class
boundaries for the EM data would differ if a higher spatial
resolution had been used and cultural features had been
removed. Although only modest agreement with EM data
was achieved, we believe that HyMap hyperspectral
imagery compliments EM data in that it characterizes the
surface expression of salinized land. Airborne hyperspectral
imagery is likely to offer a cost-effective method of mapping salinized land at a regional scale. If such maps could be
generated on a multitemporal basis, they would form an
excellent tool for the monitoring of salinized land and
assessments of rehabilitation techniques.
Satellite hyperspectral imaging systems such as Hyperion
(manufactured by TRW) provide the ability to routinely map
large areas of the Earth’s surface. Factors such as signal-tonoise ratio and IFOV (Hyperion 30 m) of spaceborne
instruments may affect their ability to detect the indicators
we have described in this paper especially in areas where
these indicators are in low abundances. These factors are the
subject of ongoing work.
5. Conclusions
(1) Salinized soils have distinctive spectral features in
the visible and near-infrared parts of the spectrum, related to
combined water in hydrated evaporite minerals. These
features allow the recognition of minerals such as gypsum,
bassanite, and polyhalite. These minerals identified by
XRD, and occurring in salinized areas, allow salinized soils
to be mapped by the HyMap hyperspectral scanner.
(2) All salinized soils sampled contain halite as the
dominant evaporite mineral. A reflectance high, or shoulder
at 800 nm, is observed in such soils. A slope reduction
between 800 and 1300 nm is also evident and is probably
due to elevated contents of evaporite minerals. Clay mineral
crystallinity, related to the depth of the 2200-nm hydroxyl
absorption feature, is observed to reduce as salinization
increases in soils.
(3) All mapping methods evaluated were moderately
successful. Salinized terrain element distribution maps created using the SFF method and a restricted spectral range
covering the hydroxyl (2200 nm), reflectance high (800 nm),
slope reduction (800 –1300 nm), and hydrate-related absorption show a spatial distribution similar to results achieved by
field mapping and ground-based EM measurements.
Mapping of saline soils using HyMap hyperspectral
imagery and field-derived spectra is an efficient method
that produces, fast, wide-coverage, and reliable distribution
maps of both soil and vegetation indicators of salinized
land. Such maps, if acquired on a multitemporal basis, are
likely to be useful tools for monitoring changes in salinization. The ephemeral nature of the salts found in areas
associated with irrigation-induced salinization is likely to
cause subtle changes that are more likely to be evident in
spectra collected from the field.
Acknowledgments
Financial and logistical support from Terry Cocks of
Integrated Spectronics, North Ryde, is gratefully acknowledged. The assistance provided by staff from the Victorian
Department of Agriculture at the R&D Block, Pyramid Hill,
is also gratefully acknowledged. The authors would also
like to thank two anonymous reviewers for their constructive suggestions to improve the manuscript.
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