ENB371 - Week3 -LN -Elastic Deformation of Soil - 6 Slides Per Page -Colour
Colour Space Models for Soil Science
Transcript of Colour Space Models for Soil Science
www.elsevier.com/locate/geoderma
Geoderma 133 (2
Colour space models for soil science
R.A. Viscarra Rossel a,*, B. Minasny a, P. Roudier a,b, A.B. McBratney a
a Australian Centre for Precision Agriculture, The University of Sydney, NSW 2006, Australiab ENSAM, AgroMontpellier, 34060 Montpellier Cedex 01, France
Received 26 August 2004; received in revised form 5 July 2005; accepted 27 July 2005
Available online 15 September 2005
Abstract
Soil colour is an important soil property. It is frequently used by soil scientists for the identification and classification of soil.
It is also used as an indicator of field soil physical, chemical and biological properties as well as of the occurrence of soil
processes. Measurements of soil colour are commonly made using the Munsell soil colour charts. A number of other colour
space models, that overcome some of the limitations of the Munsell HVC system exist and may be used to more aptly describe
soil colour. We looked at nine colour space models and a redness index: Munsell HVC, RGB, decorrelated RGB (DRGB), CIE
XYZ, CIE Yxy, CIELAB, CIELUV, CIELHC, and Helmoltz chromaticity coordinates. The aims of this paper are to (i) describe
the algorithms used for transformations between these colour space models, (ii) compare their representational qualities and
their relationships to the Munsell soil colour system, and (iii) in a case study, determine the model best suited to describe the
relationship between soil colour and soil organic carbon. The type of colour model to use will depend on the purpose. For
example, if soil colour is being used for merely descriptive purposes, then the Munsell HVC system will remain appropriate; if
it is being used for numerical statistical or predictive analysis, as in our case study, then colour models that use Cartesian-type
coordinate systems will be more useful. Of these, the CIELUV and CIELCH models appear to be more suitable for predictions
of soil organic carbon.
D 2005 Elsevier B.V. All rights reserved.
Keywords: Soil colour; Munsell soil colour; CIE; RGB; Helmholtz chromaticity coordinates; Soil organic carbon
1. Introduction
Soil colour has long been used for soil identifica-
tion and qualitative determinations of soil character-
istics (e.g. Webster and Butler, 1976). The reason is
0016-7061/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.geoderma.2005.07.017
* Corresponding author. Tel.: +61 2 9351 5813; fax: +61 2 9351
3706.
E-mail address: [email protected]
(R.A. Viscarra Rossel).
that various soil components exhibit spectral response
in the visible range of the electromagnetic spectrum,
between wavelengths 400 and 700 nm. Soil colour is
commonly and widely measured using a Munsell soil
colour chart (Munsell Color Company, 1975). It is
intuitively designed to reflect our perception of colour
and its variations. It is a useful system for categorical
qualifications of soil colour, however it does not lend
itself for numerical and statistical analysis as the
Munsell colour space is divided into a series of
006) 320–337
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337 321
non-contiguous slices presented as each page of the
colour book. Melville and Atkinson (1985) discussed
measurements of soil colour using the Munsell soil
colour charts and recommended the use of the CIE-
LAB system.
Soil colour is a continuous variable that varies in
the x, y and z spatial dimensions. It varies across the
landscape and it varies with depth. Vertical variation
in soil colour is used to distinguish different horizons
in a profile and provides an indirect measure of
important soil characteristics including drainage,
aeration, organic matter content and general fertility.
Soil colour is important in many systems of soil
classification. It is common practice for soil scientist
to use soil colour as a determinant of soil type (e.g.
red chromosol, brown kurosol (Isbell, 2002)), field
soil chemical, physical and biological properties (i.e.
soil quality and function) (e.g. Sanchez-Maranon et
al., 1997; Ben-Dor et al., 1997; Lindbo et al., 1998)
and the occurrence of soil processes (e.g gleying
(Van Huyssteen et al., 1997)). Properties such as
soil organic carbon content, iron content, soil water
content, and texture have been shown to have good
correlations with soil colour. Soils with dark surface
horizons are generally associated with high organic
matter contents, categorising them as fertile and
suitable for plant growth (Schulze et al., 1993).
Dark brown and black soils are also thought to
contain high levels of nitrogen, have good aeration
and drainage, and pose a low erosion risk. Generally,
the opposite is thought of light coloured soils. The
colour of iron containing soil minerals that undergo
oxidation and reduction reactions can provide useful
information on the hydrologic condition of a soil.
For example the occurrence of red haematite (a-
Fe2O3) in the soil profile suggests that the soil is
well-drained and has an aerobic environment while
the occurrence of yellow goethite (a-FeOOH)
implies the presence of a reduced soil environment.
Barron and Torrent (1986) looked at the influence of
iron oxides on soil colour. Soil colour can also be
used to qualitatively describe the moisture status of a
soil, for example, due to changes in the refractive
index dry soils are lighter in colour than wet soils.
Thompson and Bell (1996) used a colour index for
identifying hydric conditions in seasonally saturated
soil. Blavet et al. (2000) used soil colour (hue and
redness) to estimate the mean annual rate of soil
waterlogging. Therefore soil colour can be used for
rapid approximation of soil properties, their function
and condition.
The spatial variation of surface soil colour is
increasingly the subject of much remote sensing
research, as the spectral and spatial resolution of air-
borne and satellite imagery improves, and the cost of
images decreases (e.g. Escadafal et al., 1989; Matti-
kalli, 1997; Mathieu et al., 1998; Leone and Escada-
fal, 2001). Soil processes that affect surface soil may
be identified by colour differences (Escadafal, 1993),
hence surface soil colour has been used to categorise,
evaluate and map soils. De Jong (1992) looked at the
use of imaging spectroscopy to map erosion hazard in
the Mediterranean. Coleman et al. (1990) used the
Thematic Mapper (TM) to differentiate surface soils
and found significant correlations between radiance
data and organic matter, iron content, and particle size
distribution. Sanchez-Maranon et al. (1996) used soil
colour (hue, value and chroma) in their evaluation of
calcareous soils for reforestation in Sierra Nevada,
Spain. Francis and Schepers (1997) used soil colour
to devise a selected soil sampling scheme for the site-
specific management of soil nutrients.
The aims of this paper are threefold. (i) To describe
commonly used colour space models that have been
used to designate colour in three dimensional space
and the algorithms used for conversions between
them, (ii) to compare their representational qualities
and their relationship to the Munsell soil colour sys-
tem, and (iii) in a case study, to determine the colour
model(s) best suited for quantitative description of soil
colour and its relationship to soil organic carbon.
Before we delve into the work, we shall briefly
describe the main characteristics of the colour models
we considered.
1.1. Colour space models
Colour is a 3-dimensional psychophysical phenom-
enon. Colour is represented in colour space models
whereby individual colours are specified by points in
these spaces. There are many ways by which one can
measure colour. In this instance we will only refer to
those that attempt to create equal perceived colour
differences (e.g. the Munsell colour system) and
those that link the spectral profile of colours to the
basic units of colour perception, i.e. trichromatic col-
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337322
orimetry (e.g. the RGB, CIE XYZ systems and their
derivatives). For details on the theories and models of
colorimetry, the reader is referred to, amongst many
others, Billmeyer and Saltzman (1981) and Wyszecki
and Stiles (1982).
1.1.1. Munsell HVC soil colour system
Soil colour is commonly described qualitatively
using Munsell soil colour charts and the three vari-
ables: hue, value and chroma (HVC). These three
variables describe a perceptual colour space and not
a quantitative measure of visible light. Hue is
denoted categorically by the letter abbreviation of
the colour of the spectrum (R for red, YR for
yellow-red, Y for yellow) preceded by numbers
from 0 to 10. Within each letter range, the hue
becomes more yellow and less red as the numbers
increase. Value is specified on a numerical scale
from 0 (absolute black) to 10 (absolute white).
Chroma is also described numerically beginning at
0 for neutral greys (the achromatic point) to a max-
imum value of 20, which is never approached with
soil. The system was designed to arrange colours
according to equal intervals of visual perception,
thus the primary advantage of the Munsell system
is its ease of interpretation. However, Munsell HVC
coordinates are psychosensory, based on subjective
perception and comparison and thus the system is
not uniform. The Munsell colour system is repre-
sented in Fig. 1a.
1.1.2. RGB
Colour in the RGB system is produced by any
additive or subtractive mixture of the spectra of the
three primary colours red (R), green (G) and blue
(B). Their corresponding monochromatic primary
stimuli occur at 700, 546 and 436 nm, respectively.
On a 8-bit digital system colour is quantified by
numeric tristimulus R, G, B values that range from
0 (darkness) to 255 (whiteness). Combinations of
R, G, B primaries can produce a gamut of (28)3
different colours (Wyszecki and Stiles, 1982). The
colour gamut of the system forms a cube compri-
sing orthogonal RGB Cartesian coordinates (Fig.
1b). Each colour is then represented by a point
on or in the cube. All grey colours are present in
the main diagonal from black (R =G =B =0) to
white (R =G =B =255).
1.1.3. CIE XYZ
In 1931 the Commission Internationale de l’Eclai-
rage (CIE) standardised colour order systems by spe-
cifying the light source, the observer and the
methodology used to derive the values for describing
colour (C.I.E. 1931). The XYZ colour system was
also accepted then and it has been used ever since.
In this system, Y represents the brightness (or lumi-
nance) of the colour, while X and Z are virtual (or not
physically realisable) components of the primary
spectra (Wyszecki and Stiles, 1982). The relationships
between XYZ and RGB systems are described in the
Methods section below. The system was designed to
produce non-negative tristimulus values for each col-
our. Often this system is used as the platform from
which other colour specifications are made and as
intermediaries for determining perceptually uniform
colour systems such as CIELAB or CIELUV.
1.1.3.1. CIE Yxy. The XYZ tristimulus values are
useful for defining a colour, but the results are not
easily visualised. To overcome this problem, CIE
defined a colour space in 1931 that depicts colour
into two dimensions; this is the CIE Yxy colour space.
The standardising equations are presented in the
Methods section below. The chromaticity co-ordi-
nates x and y are independent of luminance, Y, and
specify colour variations from blue to red and blue to
green, respectively. Thus colour is represented in an
xy chromaticity diagram (Fig. 1c). The CIE chroma-
ticity diagram has one major drawback in that there
is a discrepancy between perceived colour differences
and the actual spacing of colour in the system. Both
XYZ and Yxy systems are perceptually non-linear.
1.1.4. Helmholtz chromaticity
An alternative set of coordinates developed to
overcome the difficulties of the xy chromaticity coor-
dinates are the Helmholtz chromaticity coordinates;
kd, Pe and Y, which describe the dominant wave-
length, the purity of excitation and luminance, respec-
tively. The dominant wavelength of a colour correlates
approximately with the hue of the colour illuminated
by illuminant C. The purity of excitation, measured as
a percentage, correlates in an approximate way with
the saturation of the colour. The luminance refers to
the brightness of the colour and is scaled from 0%
(black) to 100% for a white object with a diffuse
CHROMA
VALUE5R
5YR
5Y
5GY
5G5B
5PB purple-blue
blue
yellow-green
yellow
orangered
4 68
10
white
9
8
7
6
5
4
2
1
black
green
purple
5PHUE
HUE
RED
GREEN
BLUE
BLACK
WHITE
0
255
255
255
YELLOW
CYAN
MAGENTA
Red
Green
Blue
x
y
380
780
520
570
490
600
550
500
450
Purple
achromatic light
daylight
0
0.8 Pure wavelength locus (nm)
(a) (b)
(c) (d)
+a* +u* red
+b* +v*
yellow
-b* -v*
blue
-a* -u*
green
L*
0.8
Fig. 1. (a) The Munsell colour model represented by a cylindrical coordinate system. (b) The RGB model, (c) the CIE xy chromaticity diagram
(d) the CIELu*v* and CIELa*b* colour space model. (a) adapted from http://www.britanica.com/ (b � d) adapted from Wyszecki and Stiles
(1982).
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337 323
reflectance of 1 in the visible range of the spectrum.
The equations defining the parameters of the Helm-
holtz chromaticity coordinates are given in the Meth-
ods section below.
1.1.5. CIELUV and CIELAB
The CIE in 1964 proposed the CIELUV system,
which attempts to overcome the perceptual non-line-
arity of the XYZ and Yxy. The CIELUV system is
obtained after the xy coordinates are transformed to a
uniform chromaticity scale (Wyszecki and Stiles,
1982). CIELAB is an approximately uniform colour
system. Its values are calculated by non-linear trans-
formations of XYZ. Common to both systems is L the
metric lightness function (representing brightness or
luminance) which ranges from 0 (black) to 100
(white); a* and b* and u* and v* the chromacity
coordinates represent opponent red–green scales (+a,
+u reds, �a, �u greens), and opponent blue–yellow
scales (+b, +v yellows, �b, �v blues) (Fig. 1d and e,
respectively). The equations defining these systems
are given in the Methods section below.
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337324
2. Methods
Munsell soil colour charts have appropriately
chosen chips that encompass the possible range
of soil colours. Hence, colour chips from the
Munsell soil colour book (Munsell Color Company,
1994), corresponding to value, chroma combina-
tions for hues between 5R and 5Y inclusive,
were used as a proxy for soil colour and as the
source for the transformations between colour
space models.
2.1. Munsell HVC to CIE XYZ
To transform from Munsell HVC to CIE XYZ,
we used a neural network. To model this transfor-
mation, we used XYZ values (that correspond to
the Munsell soil colour chips) derived from the
Munsell Conversion program Version 6.41 (http://
www.gretagmacbeth.com). Values of Munsell Hue
were converted to angle according to Munsell’s nota-
tion (ranging from 0 to 100). The notation is divided
into 100 steps of equal visual change in hue, with 5
at the beginning (5R) and 100 at the end (10RP). We
found that network with 4 hidden nodes modelled
this transformation adequately.
2.1.1. CIE XYZ to Munsell HVC
The algorithm used for the back transformation
from CIE XYZ to Munsell HVC is based on that by
Miyahara and Yoshida (1988). We modified the algo-
rithm and made it more appropriate for soil colour by
fitting the following relationships to the rescaled CIE
XYZ values. First a non-linear process transform was
performed as follows:
f Xcð Þ ¼ 11:559X13c � 1:695
f Yð Þ ¼ 11:396Y13 � 1:610
f Zð Þ ¼ 11:510Z13c � 1:691
where Xc=1.020X and Zc=0.487Z. Then:
H1 ¼ f Xcð Þ � f Yð Þ
H2 ¼ 0:4 f Zcð Þ � f Yð Þð Þ:
S1 and S2 are then calculated for the correction of
the uniformity of colour components as follows:
S1 ¼ 8:398þ 0:832dcoshð ÞH1
S2 ¼ � 6:102� 1:323dcoshð ÞH2
where h =tan�1(H2 /H1). H, V and C are then
defined as:
H ¼�����tan�1 S2
S1
� �� 100
2p
�����V ¼ f Yð Þ
C ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiS21 þ S22
q:
The modified coefficients on the above equations
were found so that they minimize the colour differ-
ence between the true Munsell soil colour chart sam-
ples and predicted values (Godlove, 1951):
DE ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi2C1C2 � 1� cos
2p100
DH
� �� �þ DCð Þ2þ 4DVð Þ2
s
where C1 and C2 are the chroma units of the two
colour measurements separated by DC chroma units,
DH hue units and DV value units. The equation
accounts for the perceived difference in the magnitude
of value and chroma scales, as well as for the angular
separation of hue.
2.2. CIE XYZ to Yxy
The CIE chromaticity coordinate values are calcu-
lated by normalising X and Y using the following
equations:
x ¼ X
X þ Y þ Zð Þ y ¼ Y
X þ Y þ Zð Þ
where x and y values lie between 0 and 1. Usually
only x and y are given, because z=1�x�y.
2.3. CIE xy chromaticity to Helmholtz chromaticity
coordinates
Any colour, when plotted on the CIE xy diagram
may be specified in terms of its dominant wavelength
Red
Green
Blue
I
DW
CDW
x
y
S
S,
P
Fig. 2. CIE xy chromaticity diagram and calculation of Helmholz
coordinates. Adapted from Wyszecki and Stiles (1982).
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337 325
kd (DW). The DW of a colour is defined as the
wavelength of the monochromatic stimulus that,
when mixed with a specified achromatic stimulus
(such as CIE standard illuminant C or D65), matches
the given stimulus in colour (Wyszecki and Stiles,
1982). From Fig. 2, it is the wavelength of the colour
of the visible spectrum whose chromaticity is on the
same straight line as the sample point (S) and the
achromatic point (I) (for illuminant C this point is
xw=0.3101; yw=0.3163). For non-spectral colour, i.e.
colours that do not appear in the visible spectrum, a
complementary dominant wavelength (CDW) is used
(Fig. 2). From Fig. 2, these colours are located in the
triangular area encompassed by I, Red and Blue. The
reason for this is that the DWof sample (SV), indicatedby the interception point (P) does not have a corre-
sponding wavelength. Thus the line from I to P is
extended backwards to determine the CDW (Fig. 2).
Dominant wavelengths for the Munsell colour chips
were calculated from a table produced by Judd (1933)
in Wyszecki and Stiles (1967) after computation of the
following ratios:
x� xw
y� ywand
y� yw
x� xw
The purity of excitation of a given colour is an
exactly defined ratio of distances in the xy chromati-
city diagram. It is the ratio of the distance between the
illuminant point (I) to the sample point (S) and that
form N to the spectrum locus DW (Fig. 2). The purity
of excitation may be calculated using:
Pe ¼x� xw
yb � ywor Pe ¼
y� yw
xb � xw
where x and y refer to the chromaticity coordinates,
xw and yw refer to the chromaticity coordinates of the
achromatic stimulus and xb and yb are the chromati-
city coordinates of the boundary colour stimulus
(Wyszecki and Stiles, 1982).
2.4. CIE XYZ to CIELAB and CIELUV
CIE XYZ tristimuli were standardised with values
corresponding to the D65 white point: X0=95.047,
Y0=100 and Z0=108.883. We then transformed the
standardised tristimuli to the CIELAB and CIELUV
Cartesian coordinate systems using the following
equations (CIE, 1978):
L ¼ 116� Y
Y0
� �13
� 16 forY
Y0
� �N0:008856
¼ 903:3� Y
Y0
� �otherwise
where L is the metric lightness function (or luminos-
ity), which is common to both CIELAB and CIELUV
models. The a* and b* chromacity coordinates were
derived using:
a* ¼ 500� X
X0
� �13
� Y
Y0
� �13
" #
b* ¼ 200� Y
Y0
� �13
� Z
Z0
� �13
" #
The u* and v* coordinates were derived using:
u* ¼ 13L� uV� unVð Þ
where
uV ¼ 4X
X þ 15Y þ 3Zð Þ ;unV ¼4X0
X0 þ 15Y0 þ 3Z0ð Þ and
v* ¼ 13L� v V� vnVð Þ
where
v V ¼ 9Y
X þ 15Y þ 3Zð Þ ; vnV ¼9Y0
X0 þ 15Y0 þ 3Z0ð Þ
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337326
2.4.1. CIELCH
To facilitate visualisation of the colour within the
CIELAB spherical colour space, it can be transformed
into cylindrical coordinates to provide CIE hue (h*)
and chroma (c*) values, as follows:
h* ¼ arctanb*
a*
� �
c* ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffia*ð Þ2 þ b*ð Þ2
q
The CIELCH system was designed to identify the
components of colour in terms of correlates of per-
ceived hue, chroma and lightness.
2.4.2. Redness index
We also computed a redness index (RI) introduced
by Barron and Torrent (1986) for the estimation of
haematite content in soils. It is based on the Kubelka–
Munk theory, which provides a good technique to
calculate the colour of haematite containing mixtures
(Barron and Torrent, 1986). In this instance we imple-
mented their index based on the CIELAB colour
space:
RI ¼L a*ð Þ2 þ b*ð Þ2� 0:5
d108:2
b*dL6
where RI is a simple multiplicative index in which
each variable is given an exponent. Barron and Tor-
rent (1986) tested various exponents until maximum
correlation was obtained between the RI and haema-
tite content.
2.5. CIE XYZ to RGB
To transform from CIE XYZ into RGB, we first
rescaled the XYZ tristimulus between 0 and 1 and
then performed the following three-by-three matrix
(A) transformation for illuminant D65 and 28 standardobserver (Wyszecki and Stiles, 1982):
R
G
B
2435¼ 3:240479 � 1:537150 � 0:498535� 0:969256 1:875992 0:0415560:055648 � 0:204043 1:057311
24
35d X
Y
Z
2435
2.5.1. RGB to CIE XYZ
For the inverse transform the R, G and B data were
also rescaled between 0 and 1 before transformation
into CIE XYZ using the following matrix (A�1) trans-
form (Wyszecki and Stiles, 1982):
X
Y
Z
24
35 ¼ 0:412453 0:357580 0:180423
0:212671 0715160 0:0721690:019334 0:119194 0:950227
24
35d R
G
B
24
35
where Y represents the luminance component of the
image and X and Z two additional components whose
spectral composition correspond to the colour match-
ing characteristics of human vision (CIE, 1986). In
XYZ, any colour is represented as a set of positive
values.
2.6. Decorrelation of RGB data
RGB data are highly correlated. To decorrelate the
tristimuli we transformed the RGB data into three
statistically independent components:
HRGB ¼2dGð Þ � R� B
4
IRGB ¼Rþ Gþ B
3;
SRGB ¼R� B
2;
where HRGB, IRGB and SRGB represent hue, light
intensity and chromatic information, respectively.
Fig. 3 summarises the order and manner in which
all the above transformations were made.
2.7. Case study: soil colour and its relationship to soil
organic carbon
2.7.1. Soil sampling and laboratory analyses
The soil used in this study comprises A-horizon
samples (0–20 cm), originating from Australia and
France. Forty-four soil samples were collected from
various locations in Victoria (13), South Australia (5),
Western Australia (15) and Tasmania (11). Seventy-
seven samples were collected from different locations
in NSW, Australia. Forty-five soil samples were col-
lected from various locations in Brittany, France. The
soils were selected to provide our study with a repre-
Munsell HVC
CIE XYZ
RGB
DRGBHRGB, IRGB, CRGB
CIELa*b*
CIELc*h*
CIELu*v*RI
CIE xyY
Helmholtzλd, Pe, Y
Fig. 3. Colour space transformations (and back transformations)
starting from RGB and/or Munsell HVC tristimuli. Munsell HVC
and RGB systems are commonly used in soil science and remote
sensing studies as the starting colour systems used to describe soil
colour (e.g. RGB tristimuli are easily extracted from satellite
images; field measurements of soil colour using the Munsell soil
colour book). Broken lines represent non-linear transformations,
while unbroken lines represent linear transformations.
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337 327
sentative range in soil colour. Once collected, the soil
was air-dried and ground to a size fraction b2 mm.
The soil analysis conducted is described in Table 1.
Fourteen soil OC and colour data were also used from
the Canadian study by Shields et al. (1968).
2.7.2. Soil sample preparation for colour
measurements
Approximately 20 cm3 portions of each ground,
air-dry sample were placed into 30 cm3 petri dishes
for soil colour measurements. The surface of the
samples was smoothed to ensure even micro topogra-
phy. Soil colour measurements were taken of each
sample in both dry and wet states. Wetting the sam-
ples involved spraying approximately 4 ml of deio-
nised water (depending on the air-dry water content)
as a fine mist onto the soil surface to achieve even
moistening without ponding. After adding 20% water
by volume, there were no apparent changes in colour.
Table 1
Laboratory methods of soil analyses
Soil property Technique
pHCa 1 :5 soil :0.01M CaCl2pHW 1:5 soil :H2O extract
Organic carbon dag/kg dichromate oxidation
combustion
Clay content dag/kg pipette
pipette
Samples were left for 1 h before colour measurements
to minimise glistening and reduce specular reflection
and other measurement inconsistencies. These sam-
ples were used for both Munsell and spectrometric
measurements of soil colour.
2.7.3. Munsell measurements of soil colour
Soil colour was measured using the Munsell col-
our book (Munsell Color Company, 1994). Measure-
ments were performed under diffuse natural daylight
lighting conditions. The colour difference between
the replicate observations (of both dry and wet mea-
surements) was calculated using the equation derived
by Godlove (1951). The equation accounts for the
perceived difference in the magnitude of value and
chroma scales, as well as for the angular separation
of hue.
2.7.4. Spectrometric measurements of soil colour
The spectral reflectance of the Australian soil sam-
ples was measured using an ultraviolet-visible-near
infrared spectrometer (Varian Cary 500) equipped
with a diffuse reflectance accessory, with a spectral
range of 350–2500 nm. In this instrument, samples are
placed in a dark enclosure before measurements. The
French samples were scanned with a FieldSpec visi-
ble-near infrared spectrometer with a spectral range of
700–1300 nm. The scanner fibre-optic probe was
placed in an enclosure 0.1 m above the sample and
two halogen lamps illuminated the samples from 458angles. The optics of the instrument was set to 108 and10 spectra were collected and averaged for every
sample. In both instances, a white reference block
supplied with each spectrometer was used to calibrate
the instruments. Spectra were collected directly from
the soil surface of each sample at 2 nm intervals. The
reflectance data in the ranges between 450–520 nm,
520–600 nm and 630–690 nm corresponding to the
Location Reference
Australia White (1969)
France AFNOR (1996)
Australia Based on Walkley and Black (1934)
France AFNOR (1996)
Australia Rayment and Higginson (1992)
France AFNOR (1996)
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337328
red, green and blue Landsat bands 1, 2 and 3, respec-
tively, were averaged and multiplied by 255 to get the
8-bit pixel colour encoding. These RGB values were
transformed to the other colour space models
described previously using ColoSol, software devel-
oped to perform both single tristimulus transforma-
tions and multiple colour space transformations in an
ASCII text file (Viscarra Rossel, 2004). We started
with RGB colour because in soil science and remote
sensing studies these are commonly used as the start-
ing colour systems, e.g. RGB tristimuli are easily
extracted from satellite images.
2.7.5. Relationship between soil colour and soil
organic carbon (OC)
The soil OC data was positively skewed, hence it
was normalised using a square root transform. We then
correlated colour parameters of the different colour
models to soil OC contents. Based on these, relation-
ships were derived between soil OC and selected
colour parameters. We regressed soil OC (using multi-
ple linear regression) as a function of the tristimuli
values of each colour model, i.e. soil OC= f(tristimuli,
e.g. L, u*, v*). To quantify the accuracy of the rela-
tionships, we used the adjusted coefficient of determi-
nation (Radj2 ) and cross-validated (Efron and Tibrishani,
1993) root mean squared error (RMSE).
3. Results
Three hundred and seventy two Munsell soil colour
chips were used in the colour space transformations.
The CIE XYZ system served as a platform from where
the various other colour space transformations were
executed (see Fig. 3). The neural network estimates of
CIE XYZ from Munsell HVC data were accurate.
There were no concerns with over-fitting or biasedness
of predictions as we always remain within the range of
the Munsell soil colour data. Remember that this step
was introduced to computerise the transformation from
Munsell HVC to CIE XYZ. The reverse transforma-
tion using the modified Miyahara and Yoshida (1988)
algorithm was also accurate. The average difference
between the estimated Munsell values and those from
the Munsell soil colour chart was 0.27 units. Highest
errors occurred for 5R at chroma values of 6 to 8. We
will now compare the resulting transformations and
explore the relationships between the Munsell HVC
system and the various other transformed colour space
models.
3.1. Relationships between Munsell HVC and other
colour space models for soil
The median angular hue of the Munsell soil colour
chip data was 158, corresponding to a Munsell hue of
5YR. Munsell value ranged from 2 to 8, while chroma
ranged from 1 to 8 units and was positively skewed.
Fig. 4(a and b) presents the relationships between
Munsell H and C vs. CIE x and CIE y chromaticities,
while Fig. 4(c and d) shows the relationships between
Munsell H and C vs. the Helmholtz coordinates kd
and Pe.
Redder hues (Munsell H) show to have a smaller
range in CIE y chromaticity than yellower hues (Fig.
4a). Low Munsell C values have a smaller range in
CIE x chromaticity than more saturated Munsell C
values (Fig. 4b). This apparent non-uniformity in the
distribution in CIE y chromaticity for corresponding
Munsell hues, illustrates the non-uniformity of the
CIE xy system. Unlike the Munsell HVC system,
which has colour samples that are perceptually equi-
distant, the CIE xy system is perceptually non-uni-
form, giving greater bias to the yellow and green
colour gamut (Fig. 1c shows the CIE xy diagram).
The Helmholtz coordinates kd and Pe were correlated
to Munsell H and C parameters, respectively (Fig. 4c
and d). Low Munsell C values have a smaller range in
Pe values than more saturated Munsell C values (Fig.
4d). There is good correlation between CIE x and Pe
parameters (cf. Fig. 4b and d). The average kd for the
Munsell soil colours was 590 nm with a range of 575
to 615 nm. Yellow Munsell hues have wavelength in
the range from 574 to 585 nm, red Munsell hues have
wavelengths in the range from 590 to 615 nm and
yellow-red hues have wavelengths from approxi-
mately 580 to 600 nm (Fig. 4c).
Fig. 5 shows the Munsell soil colour chart gamut as
represented by the CIE xy chromaticity coordinates
and the Helmholtz coordinates.
Generally, the CIE x chromaticity coordinate was
well correlated to parameters of the various colour
space models that describe the chromaticity of the
samples, while the CIE y coordinate was better corre-
lated to parameters describing their hue.
1
2
3
4
5
6
7
8
Chr
oma
(Mun
sell)
.35 .4 .45 .5 .55 .6x
0.3
0.35
0.4
0.45
y
5 10 15 20 25Hue angle (Munsell)
(a)
(c)
(b)
(d)
1
2
3
4
5
6
7
8
Chr
oma
(Mun
sell)
0 20 40 60 80 100Pe (%)
5
10
15
20
25
Hue
ang
le (
Mun
sell)
580 590 600 610
λd (nm)
Fig. 4. Scatterplots of relationships between Munsell vs. CIE xy and Helmholtz coordinates: (a) Munsell hue (H) vs. CIE y, (b) CIE x vs.
Munsell chroma (C), (c) dominant wavelength kd vs. Munsell H and (d) purity of excitation (i.e. saturation) Pe vs. Munsell C. Different markers
represent different levels of chroma.
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337 329
Fig. 6 shows the relationships between Munsell H
and C vs. CIELAB a* and b* and CIELUV u* and v*
coordinates.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
y
0 .1 .2 .3 .4 .5 .6 .7 .8x
(a) (b)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
y
0 .1 .2 .3 .4x
Red
Green
Blue
Fig. 5. The Munsell soil colour chart gamut as represented by (a) CIE xy
colour gamut. Different markers represent different levels of chroma. (b) H
Pe (%).
CIELAB +a* and CIELUV +u* coordinates
describe red Munsell hues with values of chroma
ranging from zero for the achromatic point to positive
(c)
.5 .6 .7
615 nm 600 nm
590 nm
574 nm
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
y
0 .1 .2 .3 .4 .5 .6 .7x
2040
60
90 %
chromaticity coordinates. Black markers represent the Munsell soil
elmholz dominant wavelength kd (nm) and (c) Helmholz saturation
5
10
15
20
25
Hue
ang
le (
Mun
sell)
0 10 20 30 40 50 60
v*
1
2
3
4
5
6
7
8
Chr
oma
(Mun
sell)
0 10 20 30 40 50 60v*
1
2
3
4
5
6
7
8
Chr
oma
(Mun
sell)
10 20 30 40 50 60u*
1
2
3
4
5
6
7
8
Chr
oma
(Mun
sell)
0 10 20 30 40 50 60b*
1
2
3
4
5
6
7
8
Chr
oma
(Mun
sell)
0 10 20 30 a*
5
10
15
20
25
Hue
ang
le (
Mun
sell)
10 20 30 40 50 60
u*
5
10
15
20
25
Hue
ang
le (
Mun
sell)
0 10 20 30 40 50 60 b*
5
10
15
20
25
Hue
ang
le (
Mun
sell)
0 10 20 30 a*
(a)
(e)
(b)
(f)
(c)
(g)
(d)
(h)
Fig. 6. Scatterplots of relationships between (a–d) Munsell hue (H) and (e–h) Munsell chroma (C) vs. CIE a* b* and CIE u* v* c rdinates. Different markers represent different
levels of chroma.
R.A.Visca
rraRossel
etal./Geoderm
a133(2006)320–337
330
70
70
oo
0
10
20
30
40
50
60
70
80
90
100
L
(a)
-100
-50
0
50
100
-100 -50 0 50 100 150
u- u+
v+
v-
(c)
R
YR
Y
G
GY
BG
B PB
RP
P -50
0
50
100
-50 0 50
R
YR
Y
G
GY
BG
B PB
RP
P
a+a-
b-
b+(b)
Fig. 7. Munsell soil colour gamut (shaded area in (a) and black markers in (b) and (c)) with relation to the entire colour gamut (light grey
markers) represented by the CIELa*b* and CIELu*v* colour systems. (a) Shows the lightness function L, (b) a plot of a* vs. b* and (c) a plot
of u* vs. v*. Different black markers in (b) and (c) represent different levels of chroma. Letters represent Munsell hue coding: Y = yellow, G =
green, B = blue, P = purple and R = red.
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337 331
values of a* and u*. Hence, yellower Munsell hues
have a smaller range of +a* and +u* values than do
redder hues (Fig. 6a and c, respectively). Redder
Munsell hues have smaller ranges of CIELAB +b*
and CIELUV +v* values than do yellower hues (Fig.
2
3
4
5
6
7
8
Val
ue (
Mun
sell)
50 100 150 200 250R
2
3
4
5
6
7
8
Val
ue (
Mun
sell)
50 100G
5
10
15
20
25
Hue
ang
le (
Mun
sell)
-20 -10 0 10 20HRGB
2
3
4
5
6
7
8
Val
ue (
Mun
sell)
50 100IRG
(a)
(d)
(b)
(e)
Fig. 8. Scatterplots of relationships between (a–c) Munsell value (V) and RG
and chroma (C) vs. DRGB colour coordinates HRGB, IRGB and SRGB, respe
6b and d, respectively), as b* and v* describe yellow
hues with values of chroma extending out from the
achromatic point. The range of a*, b* and u*, v*
chromaticity coordinates increases with increasing
Munsell chroma (Fig. 6e–h). There was a perfect
150 200
2
3
4
5
6
7
8
Val
ue (
Mun
sell)
0 50 100 150 200B
150 200B
1
2
3
4
5
6
7
8
Chr
oma
(Mun
sell)
10 20 30 40 50 60 70SRGB
(c)
(f)
B colour coordinates and (d–f) between Munsell hue (H), value (V)
ctively. Different black markers represent different levels of chroma.
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337332
relationship between Munsell value and the metric
lightness function L (Radj2 =0.99). Fig. 7 shows the
Munsell colour chart gamut as represented by the
CIELAB and CIELUV colour coordinates, highlight-
ing the range of the Munsell soil colour chips.
In a perfect match between the Munsell and CIE-
LAB and CIELUV systems, hue and chroma values
would form a more symmetrical and circular
dspiderwebT effect (Melville and Atkinson, 1985). In
Fig. 7, hue and chroma values are more symmetrical
in the CIELAB diagram (Fig. 7b) than in the CIELUV
diagram (Fig. 7c), thus CIELAB more closely repre-
sents the Munsell colour system. Fig. 8 illustrates the
relationship between Munsell coordinates and the
RGB and DRGB colour space models.
The main disadvantage of the RGB system for
describing soil colour is the high degree of correla-
tion and the high influence of illumination intensity
on each of the dimensions. Fig. 8a–c shows the
relationship between Munsell V and each of the R,
G and B coordinates, stressing the high influence
and proportionality of illumination on the RGB data.
0
50
100
150
200
250
R
0 50 100 150 200 250G
0
50
100
150
200
250
R
0 50 100
0
50
100
150
200
IRG
B
-50 0 50HRGB
-100
-50
0
50
100
SR
GB
-50 0HRG
(a)
(d) (e)
(b)
Fig. 9. The Munsell soil colour gamut (black markers) with relation to the e
colour system and the (d–f) DRGB colour system. Different black markers
represents the line of greys.
To eliminate this problem we de-correlated the RGB
model (DRGB) into coordinates that correlate well
with the more perceptual parameters of hue, value
and chroma, i.e. HRGB, IRGB, SRGB. The HRGB, IRGBand SRGB coordinates provide good approximations
to the Munsell H, V and C coordinates, respectively
(Fig. 8d–f). The DRGB colour model is more stable
with changes in illumination than the RGB model.
Fig. 9 shows the Munsell colour gamut as repre-
sented by the RGB and DRGB colour systems,
highlighting the range and configuration of the Mun-
sell soil colour chips in each of the colour space
models.
3.2. Case study: soil colour and its relationship to soil
organic carbon
The surface soil samples used in this study encom-
pass a wide geographic extent. This variation is
reflected in the wide ranging distributions of their
soil properties as well as in their perceived and quan-
tified soil colour measurements (Table 2).
150 200 250B
0
50
100
150
200
250
G
0 50 100 150 200 250B
50B
0
50
100
150
200
IRG
B
-100 -50 0 50 100SRGB
(c)
(f)
ntire colour gamut (light grey markers) represented by the (a–c) RGB
represent different levels of chroma. In (a), (b) and (c) the solid line
Table 2
Statistics of soil organic carbon and colour parameters from differ-
ent colour space models
Mean Standard
Deviation
Median Range
OC dag/kg 1.80 1.37 1.39 0.00–8.90
SQRT. OC dag/kg 1.25 0.49 1.18 0.00–2.98
pHCa 5.95 1.33 5.99 3.66–9.73
Clay dag/kg 21.92 13.47 17.13 1.83–63.33
Munsell H8 19.2 2.70 20.00 12.5–22.50
Munsell V 3.38 0.70 3.00 2.00–5.00
Munsell C 2.98 1.16 3.00 1.00–6.00
H8 21.46 2.55 22.13 11.71–24.98
V 2.44 0.68 2.39 0.72–4.36
C 2.24 0.70 2.17 0.43–4.12
X 4.95 2.54 4.43 0.59–14.73
Y 4.74 2.53 4.14 0.57–14.11
Z 3.01 1.75 2.59 0.43–9.12
x 0.39 0.02 0.39 0.34–0.44
y 0.37 0.01 0.37 0.35–0.40
kd (nm) 582.61 2.55 582.82 573.30–589.02
Pe 37.46 8.38 37.07 16.58–55.83
L 24.68 7.40 24.12 5.15–44.39
a* 5.93 2.27 5.77 0.10–13.24
b* 11.75 3.67 11.39 2.75–21.66
u* 11.58 4.33 10.95 0.98–24.79
v* 10.66 3.96 10.31 1.60–21.72
c* 13.24 4.06 12.84 3.07–23.87
h* 1.10 0.12 1.09 0.87–1.54
R 72.76 18.68 73.39 22.00–127.24
G 55.29 16.32 53.18 16.00–99.29
B 41.21 13.63 40.09 12.00–79.00
HRGB �0.85 0.97 �0.97 �4.21–2.23IRGB 56.42 15.81 55.10 16.67–100.66
SRGB 15.78 4.93 15.30 3.00–29.13
RI 81.59 165.15 21.00 1.00–1056.50
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337 333
The soil samples used in this study had a wide
range of soil OC and a representative range in soil
colour (Table 2). The average kd of the samples was
20
40
60
Cou
nt A
xis
0 1 2 3 4ΔE (wet)
m = 0.61σ = 1.02
Fig. 10. Histogram of colour differences (DE) between replicate wet and
standard deviation (r).
583 nm, ranging from 573 to 589 nm. The angular
Munsell H of our samples ranged from 12.5 (2.5YR)
to 22.5 (2.5Y), with Munsell V ranging from 2 to 5
units and Munsell C from 1 to 6 units. Although
Munsell colour chart measurements are the most com-
mon method used to describe soil colour, they are
prone to perceptual human errors. Colour differences
(DE) between replicate measurements were smaller
for wet Munsell determinations due to the homogeni-
sation of colour caused by wetting (Fig. 10).
The average difference between replicate dry
measurements was 2.3 colour difference units,
while for wet measurements it was 0.6 units. Preci-
sion was also lower for the dry measurements, as
shown by the standard deviation of the difference
between replicates (Fig. 10). The subjective nature of
the measurements, the need for more than one obser-
ver to estimate soil colour and the resulting lack of
precision is a major drawback of Munsell soil colour
measurements (Post et al., 1994). There are various
other studies suggesting the use of spectrophot-
ometers (e.g. Barrett, 2002) and digital cameras
(e.g. Viscarra Rossel et al., 2003) for rapid, quanti-
tative measurements of field soil colour.
3.3. Relationship between colour model parameters
and soil organic carbon (OC)
Rapid and accurate quantification of the spatial
distribution of soil OC is desirable in agriculture.
Conventional techniques to quantify soil OC are
expensive, time-consuming and on occasions inaccu-
rate (e.g. McCauley et al., 1993). Soil colour may be
used to quantify soil OC. There are a number of
studies reporting relationships between soil colour
10
20
30
Cou
nt A
xis
0 1 2 3 4 5 6ΔE (dry)
m = 2.33 σ = 1.58
dry Munsell soil colour measurements showing the mean (m) and
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337334
and soil OC. Most of these deal with the Munsell
HVC system. For example, Franzmeier (1988)
reported relationships between soil organic matter
and Munsell value and chroma with R2 values of
b0.48. Schulze et al. (1993) reported a weak relation-
ship (R2=0.31) between wet Munsell value and soil
OC for soil with varying textural composition. Lindbo
et al. (1998) used a chroma meter to measure relation-
ships between soil colour, soil OC and hydromorphol-
ogy. They reported an R2 value of 0.63 for the
relationship between dry Munsell value and soil OC.
Konen et al. (2003) used a chroma meter to develop
relationships between soil colour, soil OC and texture.
They showed logarithmic relationships between
reflectance, Munsell value, Munsell chroma and soil
OC. The R2 values of their relationships ranged from
0.68 to 0.77. The basis for all of this work is the fact
that darker soil contains higher amounts of soil OC
than lighter coloured soil. This darkening of soil with
higher OC content is due to the effect of saturated
organic matter and to variations in composition and
Table 3
Correlations between soil organic carbon (OC) and soil colour parameters
OC (All n =180) OC (Australia n =44) OC
Munsell H �0.19 �0.47Munsell V �0.59 �0.46Munsell C �0.45 �0.26H �0.18 �0.43 �0V �0.72 �0.78 �0C �0.68 �0.70 �0X �0.79 �0.78 �0Y �0.77 �0.77 �0Z �0.69 �0.74 �0x �0.28 �0.41 �0y �0.45 �0.62 �0kd �0.05 0.09 �0Pe �0.35 �0.52 �0L �0.74 �0.79 �0a* �0.37 �0.44 �0b* �0.65 �0.71 �0u* �0.67 �0.68 �0v* �0.76 �0.75 �0c* �0.62 �0.68 �0h* �0.17 �0.39 �0R �0.79 �0.79 �0G �0.70 �0.77 �0B �0.59 �0.72 �0HRGB �0.04 �0.06 �0IRGB �0.72 �0.71 �0SRGB �0.68 �0.79 �0RI 0.30 0.62 0
quantity of black humic acid (Schulze et al., 1993).
Organic materials in soil contribute to soil colour
through the formation of organo-mineral complexes.
Correlations between soil OC and soil colour vari-
ables from the various colour systems examined are
shown in Table 3.
Organic carbon was also well correlated to the
lightness parameters of the different colour models,
and to a lesser extent to their chromaticity. Generally,
highest correlations were obtained for R of the RGB
and v* of the CIELUV models (Table 3). The R
coordinate of the RGB model showed the strongest
single parameter relationship to soil OC with a Radj2 of
0.66 (Fig. 11a). The reason for this may be that R
contains combined information on both brightness
and chromaticity. Thus measurements will also be
very sensitive to the lighting condition at the time of
measurements (see above).
The L and v* parameters of the CIELUV model
also showed good relationships, with Radj2 values of
0.56 and 0.52, respectively (Fig. 11b and c). Combin-
(NSW n =77) OC (France n =45) OC (Canada n =14)
�0.21�0.88�0.63
.41 0.12 �0.87
.68 �0.86 �0.84
.71 �0.85 �0.83
.68 �0.83 �0.84
.66 �0.83 �0.83
.52 �0.77 �0.70
.43 �0.65 �0.37
.64 �0.86 �0.57
.05 �0.32 0.51
.54 �0.74 �0.47
.70 �0.87 �0.87
.36 �0.68 �0.19
.72 �0.88 �0.81
.64 �0.82 �0.79
.75 �0.89 �0.91
.69 �0.85 �0.80
.36 0.03 �0.87
.75 �0.88 �0.90
.66 �0.86 �0.87
.50 �0.76 �0.72
.18 0.12 �0.77
.71 �0.86 �0.84
.68 �0.87 �0.84
.50 0.47 0.83
0
0.5
1
1.5
2
2.5
3
20 40 60 80L + u* + v*
y = 5.677 – 1.167 log10 (L + u*+ v*)RMSE = √0.27 dag/kg
R2adj. = 0.68
0
0.5
1
1.5
2
2.5
3
0 5 10 15 20 25
u*
y = 3.117 – 0.790 log10 (u*)
RMSE = √0.34 dag/kgR2adj. = 0.52
0
0.5
1
1.5
2
2.5
3
10 20 30 40L
y = 4.843 – 1.139 log10(L)RMSE = √0.31 dag/kg
R2adj.= 0.56
0
0.5
1
1.5
2
2.5
3
SQ
RT
OC
dag
/kg
0 5 10 15 20v*
y = 3.317 – 0.905 log10 (v*)
RMSE = √0.30 dag/kg R2
adj.= 0.63
0
0.5
1
1.5
2
2.5
3
SQ
RT
OC
dag
/kg
20 40 60 80 100 120R
y = 7.304 – 1.425 log10 (R)RMSE = √0.28 dag/kg
R2adj. = 0.66
(a) (b) (c)
(d) (e)
Fig. 11. Relationships between soil OC and soil colour parameters from the RGB model: R, and the CIELUV model: L, u*, v* and combined
L +u*+v*. French data (!), Australian data (o), NSW data (+) and Canadian data (�). (n =180).
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337 335
ing the parameters of the CIELUV model improved
the relationship producing better Radj2 and RMSE
values (Fig. 11e). When we regressed soil OC as a
function of the tristimulus of each colour model, we
found that the Munsell HVC model produced the
weakest relationships and least accurate results
(Radj2 =0.36 and RMSE=0.62 dag C/kg soil), followed
by the CIE Yxy (Radj2 =0.54 and RMSE=0.57 dag C/
kg soil) and the Helmholtz coordinates kd, Pe, Y
(R2adj=0.56 and RMSE=0.57 dag C/kg soil). The
CIELCH model produced the strongest relationship
with an Radj2 . value of 0.67 and an RMSE of 0.51 dag
C/kg soil. There was no significant difference
(a =0.01) between the multiple linear regression pre-
dictions of soil OC using tristimuli of each of the
remaining colour models (Radj2 =0.66 and RMSE=
0.52 dag C/kg soil).
Using such relationships it may be possible to
differentiate surface soil OC content across the land-
scape based on soil colour. Similarly, they may be
used to characterise the within-field variation in soil
OC. The potential exists for the development of a
proximal don-the-goT soil colour sensor to provide
detailed information on the spatial variability of soil
OC. Soil colour, measured using such proximal as
well as remote sensors may also provide useful ancil-
lary information for the production of digital soil
maps (McBratney et al., 2003).
4. Conclusions
In this paper we described a number of colour
space models that may be used for quantitative
descriptions of soil colour and algorithms that may
be used to convert between them. We also compared
their representational qualities and evaluated them
against the more commonly used system for describ-
ing soil colour, the Munsell HVC system. Any of the
colour space models may potentially be used in soil
science. Munsell H showed good correlations to CIE
y, kd, h* and HRGB; Munsell V to the metric lightness
function L, CIE Y, R, G, and B and IRGB and Munsell
C to CIE x, Pe, a*, b*, u*, v* and c* and SRGB.
R.A. Viscarra Rossel et al. / Geoderma 133 (2006) 320–337336
However, conjunctively the Munsell HVC parameters
were best correlated to the CIELCH and DRGB mod-
els and their respective parameters.
Soil colour alone is not a functional attribute of
soil, therefore in a case study, we evaluated the use-
fulness of each of the colour models studied by look-
ing at relationships between soil colour and soil
organic carbon. Organic carbon was correlated to
parameters from the various colour space models
that describe both the lightness of the colour and its
chroma.
Which colour model is best suited for descriptions
of soil colour? The answer depends on the purpose.
For example, if soil colour is being used for merely
descriptive purposes, then the Munsell HVC system
will remain appropriate; if it is being used for quanti-
tative, numerical or predictive analysis then colour
models that use Cartesian-type coordinate systems
will be more suitable. For predictions of soil OC,
the Munsell HVC system was the least suitable of
all of the models tested while the CIELUV and
CIELCH models were slightly more suitable than
other colour systems.
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