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The increase in soil organic matter (SOM) content contributes to the mitigation of the effects of global climate change; thus, it is important to know its levels. However, the SOM analysis can eventually be expensive and time-consuming, as well as generating toxic waste. The measurement of soil color may be an indirect method more practical to estimate the SOM than traditional techniques. The principal aim of the study was to use color parameters through the CIE Lab system and some color indices, such as saturation and redness indices, to estimate the SOM in a karst area of the municipality of Chetumal in the Yucatan Peninsula, Mexico. The percentage of SOM was measured in 50 soil samples by conventional methods while the soil color was analyzed with the CIE Lab system. Both variables were correlated with the redness index. Based on color, the samples were separated into five groups, ranging from pinkish white to brownish gray. Multiple regression equations (SOM vs soil color parameters) were performed for each group and a medians comparison analysis was applied. The correlation adjustment between the redness index and SOM is R2> 0.86. The values of the multiple regression equations were R2> 0.8. We conclude that the soil redness index, now named soil organic matter index, can be used as a relatively quick approach to estimate the percentage of SOM in karst areas
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García-Ruiz et al.
Proxy method to estimate SOM
Ecosist. Recur. Agropec. 9(1): e3189, 2022
Colorimetric method to estimate the soil organic matter in karst areas
Método colorimétrico para estimar la materia orgánica del suelo en áreas de karst
Rafael García-Ruiz1,
Rufo Sánchez-Hernández2,
Francisco Bautista3,
Avto Goguitchaichvili4
1Servicio Arqueomagnetico Nacional,
Instituto de Geofísica, Universidad
Nacional Autónoma de México. An-
tigua Carretera a Pátzcuaro Núm.
8701, Colonia Ex-hacienda de San
José de la Huerta, CP. 58190. More-
lia, Michoacán, México.
2División de Ciencias Agropecuarias,
Universidad Juárez Autónoma de
Tabasco. Carretera Villahermosa-
Teapa km. 25, Ranchería La
huasteca, CP. 86280. Centro,
Tabasco, México.
3Centro de Investigaciones en
Geografía Ambiental, Universidad
Nacional Autónoma de México, An-
tigua Carretera a Pátzcuaro núm.
8701, col. Ex-hacienda de San José
de la Huerta, CP. 58190. Morelia,
Michoacán, México.
4Laboratorio Universitario de
Geofísica Ambiental, Instituto de
Geofísica, Universidad Nacional
Autónoma de México. Antigua
Carretera a Pátzcuaro Núm. 8701,
Colonia Ex-hacienda de San José
de la Huerta, CP. 58190. Morelia,
Michoacán, México.
Corresponding author:
Scientific article
Received: November 9, 2021
Accepted: February 28, 2022
How to cite: García-Ruiz R,
Sánchez-Hernández R, Bautista F,
Goguitchaichvili A (2022) Colorimetric
method to estimate the soil organic
matter in karst areas. Ecosistemas y
Recursos Agropecuarios 9(1): e3189.
DOI: 10.19136/era.a9n1.3189
ABSTRACT. The increase in soil organic matter (SOM) content contributes to the
mitigation of the effects of global climate change; thus, it is important to know
its levels. However, the SOM analysis can eventually be expensive and time
consuming, as well as generating toxic waste. The measurement of soil color
may be an indirect method more practical to estimate the SOM than traditional
techniques. The principal aim of the study was to use color parameters through
the CIE Lab system and some color indices, such as saturation and redness
indices, to estimate the SOM in a karst area of the municipality of Chetumal in
the Yucatan Peninsula, Mexico. The percentage of SOM was measured in 50 soil
samples by conventional methods while the soil color was analyzed with the CIE
Lab system. Both variables were correlated with the redness index. Based on color,
the samples were separated into five groups, ranging from pinkish white to brownish
gray. Multiple regression equations (SOM vs soil color parameters) were performed
for each group and a medians comparison analysis was applied. The correlation
adjustment between the redness index and SOM is R2> 0.86. The values of the
multiple regression equations were R2> 0.8. We conclude that the soil redness
index, now named soil organic matter index, can be used as a relatively quick
approach to estimate the percentage of SOM in karst areas.
Key words: Organic matter, redness index, soil color, proxy method, karst.
RESUMEN. El aumento del contenido de materia orgánica del suelo (MOS)
contribuye a mitigar los efectos del cambio climático global, por lo que es importante
conocer sus niveles. Pero el análisis del MOS puede resultar costoso y tardado,
además de generar desechos tóxicos. La medición del color del suelo puede ser
un método indirecto más eficaz para estimar el MOS con respecto a los métodos
tradicionales. El objetivo del estudio fue utilizar parámetros del color mediante el
sistema CIE Lab y algunos índices de color, como los índices de saturación y de
rojez, para estimar el MOS en una zona kárstica del municipio de Chetumal en la
Península de Yucatán, México. El porcentaje de MOS se midió en 50 muestras de
suelo mediante métodos convencionales, y el color del suelo se midió con el sistema
CIE Lab. Ambas variables se correlacionaron con el índice de rojez. Según el color,
las muestras se separaron en cinco grupos, que van del blanco rosado hasta el gris
parduzco. En cada grupo se realizaron ecuaciones de regresión múltiple (MOS vs
parámetros del color del suelo) y se aplicó un análisis de comparación de medianas.
El ajuste de la correlación entre el índice de rojez y el MOS es de R2> 0.86. Los
valores de las ecuaciones de regresión múltiple fueron de R2> 0.8. El índice de
rojez del suelo, ahora nombrado índice de materia orgánica del suelo puede ser
utilizado como una técnica rápida, para estimar el porcentaje de MOS en zonas
Palabras clave: Materia orgánica, índice de rojez, color del suelo, método proxy,
E. ISSN: 2007-901X
García-Ruiz et al.
Proxy method to estimate SOM
Ecosist. Recur. Agropec. 9(1): e3189, 2022
The significance of soil organic matter has re-
cently been recognized as a natural process of car-
bon storage that may help to mitigate climate change
(Viscarra-Rossel et al. 2008, Powlson et al. 2011,
Ontl and Schulte 2012). This has led to the or-
ganization of a worldwide network to develop large
databases of soil organic carbon inventories (Paz and
Etchevers 2016). However, the traditional method of
analysis of organic matter in soils is relatively expen-
sive, requires intensive laboratory work, and is defini-
tively time-consuming polluting waste are produced
from the analysis. A far more practical technique for
determining a soil’s composition is the indirect method
of studying the soil’s color for indications of its compo-
sition (Levin et al. 2005, Viscarra-Rossel et al. 2008,
Cortés et al. 2015, Hausmann et al. 2016).
The soil color is the physical property of pri-
mary consideration in the identification of soil types
(Spielvogel et al. 2004), soil ethnopedological classes
(Bautista and Zink 2010, Sánchez-Hernández et al.
2018), and orders or primary groups of soils (IUSS
Working Group WRB 2015). The study of soil color
has also been widely used in the research of soil
genesis (Kumaravel et al. 2010), as well as for the
identification of fertile soils (Schulze et al. 1993,
Leirana-Alcocer and Bautista 2014) and automated
identification of soil horizons (Zhang and Hartemink
Some compounds that give color to the soil are
minerals and organic matter. For example, the colors
vary with the presence of iron oxides (Torrent et al.
1983, Schwertmann 1993, Levin et al. 2005, Viscarra
et al. 2008), soluble salts such as calcium carbonate,
gypsum and others (Sánchez et al. 2004), heavy
metals (Cortés et al. 2015, Marín et al. 2018, Del-
gado et al. 2019) and organic carbon (Torrent et al.
1983, Bédidi et al. 1992, Schwertmann 1993, Lindbo
et al. 1998, Viscarra-Rossel et al. 2008, Vodyanitskii
and Savichev 2017).
Colorimeters for the analysis of solid samples,
such as soil, have been manufactured in recent years.
At the same time, several color systems have been
developed that can be expressed numerically, as
CIE-RGB, CIE-L*a*b* and CIE-XYZ (Leirana-Alcocer
and Bautista 2014, Cortés et al. 2015, Aguilar et al.
2013, Marín et al. 2018). These color measurement
systems allow mathematical relationships to be es-
tablished with other soil properties (Leirana-Alcocer
and Bautista 2014, Levin et al. 2005, Cortés et al.
2015, Marín et al. 2018, Delgado et al. 2019). The
CIE-L*a*b* parameters are useful in obtaining the op-
timum redness index (Kirillova et al. 2014), which
is helpful in determining the presence and contribu-
tion of Fe-oxides on percentage (Vodyanitskii and
Savichev 2017). L* represents the contrast ranging
from black to white (0-100) and a* and b* are chro-
matic coordinates, a* being the variance from red to
green and b* that from yellow to blue (CIE 1978).
Simon et al. (2020) concluded that the rela-
tion between the color of soil and other properties
as organic matter, texture, soil chemical composition,
and particle size are variables; thus it is necessary
to develop precise predictive models under soil spe-
cific properties of each place. Shields et al. (1968),
indicated that the concentration and nature of the or-
ganic carbon in the organic matter generate several
colorations. According to Chen et al. (2018), the
coordinates a y b of a system of colors CIE L*a*b*,
correlate intimately with soil organic carbon concen-
In the tropical karst areas of Mexico on the
Yucatan peninsula, there are large areas with soils
of contrasting colors, which vary between white
limestone rock and black organic matter (Bautista et
al. 2003, Bautista 2021, Fragoso et al. 2017). To
improve the accuracy of soil organic carbon inven-
tories, it will be necessary to analyze thousands of
soil samples; for this reason, it will be essential to
generate models for estimating soil organic matter
with proxy technologies. Hypothetically, the physical
and chemical characteristics of a karstic soil don’t im-
pede the development of robust models to predict the
organic matter concentration from soil color proper-
ties. Thus, the aim of this study was to explore the
use of soil color parameters in order to estimate the
organic matter in soils from a karstic zone in the Yu-
catan Peninsula in México.
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García-Ruiz et al.
Proxy method to estimate SOM
Ecosist. Recur. Agropec. 9(1): e3189, 2022
The study zone is in Chetumal (Quintana Roo),
the south Yucatan Peninsula at the southeastern part
of Mexico. This region is a large limestone plain
with a tropical climate, where the Leptosols domi-
nate the poorly developed karst plains, although there
are also other soil groups such as Gleysols, Verti-
sols, Phaeozems, and Luvisols (Bautista et al. 2011,
Fragoso et al. 2017). On the peninsula of Yu-
catán, the dominant vegetation is the low and sub-
deciduous tropical forest and medium and perennial
tropical forest.
Fifty soil samples were collected; these sam-
ples were air-dried in the shade and sieved with
a 2 mm mesh. The soil samples were selected
from a set of samples considering that the colors
should be between the white color where limestone
predominates and the black color due to the high per-
centage of organic matter in the soil. The chemical
analyzes performed on the soil samples were: pH
(Lean 1982), organic matter was measured using
the wet oxidation method with potassium dichromate
(Nelson and Sommers 1982), exchangeable cations
Ca, Mg, Na and K with ammonium acetate (Okalebo
et al. 1993).
An X Ray Diffraction (XRD) analysis was per-
formed to identify minerals present in a soil sample.
As calcite was the dominant mineral and prevents
the identification of residual minerals, 10% HCl was
added to destroy the carbonates. A soil sample was
placed on a silicon sample holder coated with silicone
grease suitable for XRD; subsequently, they were
analyzed on a Siemens D-5000 diffractometer, Bragg-
Brentano Mode, with a monochromatic Cu tube (l =
1.5418 Å), a step time of 3 seconds, step size 0.02
degrees, at 34KV and 25 mA.
The organic matter index
The color of the soil samples was analyzed
using a Konica Minolta CR-5 reflectance and trans-
mission colorimeter. The color parameters were ob-
tained using the system CIE-L*a*b* and CIE-XYZ
defined by the International Commission on Illumi-
nation (CIE). The use of the CIE-L*a*b* simplifies
and strengthens statistical calculations (Vodyanitskii
and Savichev 2017) and the CIE-XYZ is the base of
the transmission to any other color space (Viscarra-
Rossel et al. 2006).
In the CIE-XYZ model, X is the color red, which
varies from 0 to 0.9505, Y is the color green, which
varies from 0 to 1.0, and Z is the color blue, which
varies from 0 to 1.089 (Kirillova et al. 2014). The
redness index was introduced by Barron and Torrent
(1986) to estimate the percentage content of hematite
in soils, but it should simply be called "color index" like
this in general, because it includes all the parameters
of the CIE-L*a*b* color system and therefore can be
associated with the materials and minerals that give
the soil its color (Bautista et al. 2003; 2005),
RI =L(a2+b2)0.51010/bL6
where RI = redness index, L* = luminosity, a*
= coordinates of red/green, and b* = coordinates of
yellow/blue. In this paper we will refer to this as the
organic matter index (OMI).
The relationship between the soil organic
matter (SOM) and the OMI is established under
the assumption of a possible curvilinear behavior
(Schulze et al. 1993), and proposes an adjustment
based on the power regression analysis with the two
SOM =aOMIb+c
where a, b and c are the coefficients that
should be found with a confidence limit of 95%;
however, another adjustment is proposed with a
logarithmic regression analysis (Viscarra-Rossel et al.
SOC =aLn(OMI) + b
where a and b are the coefficients that should
be found for both equations with a confidence limit
of 95% using regression analysis. A cross validation
between the SOM and the equations obtained (Eq.
2, Eq 3) was applied in order to verify the viability to
obtain the percentage of SOM.
E. ISSN: 2007-901X
García-Ruiz et al.
Proxy method to estimate SOM
Ecosist. Recur. Agropec. 9(1): e3189, 2022
Formation of soil sample groups by color
The soil samples were separated into
color groups using color parameters following the
methodology established by Cortes et al. (2015). The
first step was to transform the parameters of CIE-XYZ
system to the RGB system to rescale the XYZ triplets
and subsequently make use of the square matrix 3 x 3
for the standard illuminant D65 at 2°(Viscarra-Rossel
et al. 2006).
3.240579 1.537150 0.498535
0.969256 1.875992 0.041556
0.055648 0.204043 1.057311
The CIE-RGB was used to make the cluster
analysis and separate the soil samples into groups
based on the similarities and differences between the
color parameters with a k-means clustering (Matlab
Based on the study of Vodyanitskii and
Savichev (2017), we use the brightness (L), redness
(a) and yellowness (b) to make a linear regression
analysis for each group obtained.
The parameters of color form a rectangular ma-
trix MATN,4, and the SOMN,1is a vector of the soil
organic matter.
The vector of the coefficients X4,1is deter-
mined by the method of least squares with the Moore-
Penrose pseudo-inverse algorithm.
The present study used the measure of
error referred to as the K-factor (SOM/SOM1/2)
(Vodyanitskii and Savichev 2017). The multiple de-
termination coefficient R2was applied to observe
whether the estimated SOMewas accurate about the
original SOM. The number of the samples should be
quite large (N>8) and the content of the SOM greater
than 0.4% to obtain a strong correlation (Vodyanitskii
and Savichev 2007).
Finally, the SOM and OMI by color group of
soils were compared using the Kruskal-Wallis test, as
it is the best method to compare population in which
there is no gaussian distribution of the data. Kruskal
and Wallis test (1952) evaluates the hypothesis that
the median of each group is equal; it combines the
data of every group and orders them from least to
greatest, and subsequently calculates the average
range for the data of each group.
The organic matter index
Descriptive statistics provided the values of the
variation of the SOM and the OMI (Table 1) in all of the
samples, making it possible to observe the variation
of the SOM in its concentrate between 2.11 ±1.30
and that of the OMI between 18.80 ±18.28 with a
median similar to the mean that indicate that the sam-
ples belong to the same group of soil samples. Other
chemical properties of soils also show a wide range of
variance, such as the CEC ranging from 18.9 to 34.7
cmol kg1(Table 1).
The SOM vs. OMI has two mathematical ad-
just by power and logarithmic fit with a small deviation
that ensured the correlation between both parame-
ters with an R2>0.86 and a R2>0.87 (Table 2, Figure
1) respectively. The relation that exists between SOM
vs SOMI provided an equation to estimate the soil
organic matter (SOMe) and when is made a cross-
validation between the SOM (measured) and the
SOMe was obtained a R2>0.85 and the RMSE 0.50%
which indicates a clear association.
Formation of soil sample groups by color
The full set of samples was organized into five
groups divided by color: Group I with soils of a pinkish
white color, Group II with soils of a brownish grey
color, Group III with soils of a grey color, Group IV
with soils of a greyish brown color, and Group V with
soils of a dark grey color (Table 3, Figure 2). These
soil colors have significative difference for the SOM
and OMI observed in Figure 3 by the box plot.
These five groups were independently related
with their SOM by a multiple linear system. The
correlation of each of the groups is well defined, espe-
cially for Group V, where the system that it conformed
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García-Ruiz et al.
Proxy method to estimate SOM
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Table 1. Chemical properties of soils.
—————- cmol/kg —————- %
X 7.8 0.59 36.1 2.6 0.2 0.4 25.2 2.1 18.8
Sd 0.2 0.13 6.5 0.7 0.1 0.4 4.0 1.3 18.2
Max 8.3 1.19 48.9 4.7 0.4 1.2 34.7 5.1 92.4
Min 7.4 0.39 24.2 1.6 0.0 0.0 18.9 0.0 2.7
EC = electrical conductivity; CEC = cation exchange capacity; SOM = soil or-
ganic matter; SOMI = soil organic matter index.
Table 2. Regression analysis, equation for correlation and cross-validation.
Regression analysis R2SOM vs. SOMeCross-validation (R2, RMSE)
SOM = 9.75(SOMI)0.101 - 10.5 0.86 f(x) = 0.86x + 0.30 0.85, 0.51
SOM = 1.26Ln(SOMI) - 1.05 0.87 f(x) = 0.85x + 0.31 0.85, 0.52
R2= the square correlation between the response values and the predicted response values;
SOM = Soil’s Organic Matter; SOMe= Estimated Soil Organic Matter; SOMI = organic carbon
index RMSE = Root Mean Squared Error.
Figure 1. Fit analysis between the SOM and the SOMI: a) Power regression analysis and cross-validation, b) Logarithmic
adjustment and cross-validation.
was quite outstanding by the mathematical analysis
due to the matrix SOM4×4being quadratic, which en-
sured the solution of the system. The amounts of the
SOM have the major percentage for the group V and
gradually decrease until the group I (Table 4).
The multiple linear correlations between the
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Proxy method to estimate SOM
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Table 3. Descriptive statistics of the soil organic carbon in the five color groups.
Groups/Colors I Pinkish white II Brownish grey III Grey IV Greyish brown V Dark grey
Mean SOM (%) 0.55 1.66 2.36 2.96 4.44
Maximum 1.21 2.22 3.11 3.56 5.12
Minimum 0.16 0.48 0.03 1.83 2.69
Standard deviation. 0.27 0.73 0.79 0.57 1.17
N 13 6 16 11 4
n = sample number.
Figure 2. Color groups of soil samples. L* = luminosity, a* = coordinates of red/green, and b* = coordi-
nates of yellow/blue (CIE 1978).
Figure 3. Box plot for the five groups: a) SOM, b) OMI.
CIE-L*a*b parameters and the SOM provided good
results for each group, especially for Groups I, II,
and V. This further emphasizes the relationship that
exists between the three parameters of color in the
CIE-L*a*b* system with the SOM, providing equations
to estimate the soils organic matter estimated in the
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Table 4. Linear regression between the soil’s organic carbon and parameters of color.
Group SOM (%) SOM = XLL* +Xaa* + Xbb* + X0SOMe(%) R2
I (pinkish white) 0.550.82
0.28 3.41 - 0.043L* - 0.16a* + 0.084b* 0.540.80
0.28 0.92
II (brownish grey) 1.662.39
0.93 12.96 - 0.15L* + 0.83a* - 0.34b* 1.662.31
1.01 0.89
III (grey) 2.363.15
1.57 5.75 + 0.01L* - 0.14a* - 0.36b* 2.563.20
1.92 0.85
IV (greyish brown) 2.963.53
2.39 12.18 - 0.24L* - 6.67a* + 2.14b* 2.793.26
2.32 0.83
V (dark grey) 4.445.61
3.27 50.53 - 0.75L* - 16.7a* + 3.33b* 4.475.65
3.30 1.0
SOM = the soils’ organic matter, SOMe= soils’ organic matter estimated by Group, R2= the
square coefficient of correlation.
Groups (SOMeg).
Groups II and V did not meet the first require-
ments of the number of samples N> 8 for a suitable
adjustment, but despite this, the results present a
substantial correspondence.
The organic matter index
The value of the correlation between SOM
(measured) and SOMe (estimated) in this study was
acceptable compared to that obtained by Stiglitz et
al. (2017). They used the soil color parameters
as a predictor of the SOM developing a prediction
model, using the soil depth, L * and a * as inde-
pendent variables in dry soils obtaining values of R2
= 0.7978 and RMSE = 0.0819. In contrast, in soils
wet R2= 0.7254 and RMSE = 0.1536. These re-
sults suggest that the soil color is efficient for the
rapid determination of SOM. However, they warn that
the high iron contents, carbonates, depth, and the
humidity of the soil are variables that can negatively
affect the model’s predictive capacity. To improve the
accuracy of color measurement with electronic equip-
ment we recommended taking into account: a) the
size of the particle (Sánchez et al. 2004); b) soil mois-
ture (Domínguez et al. 2012); c) particulate organic
A better fit was obtained between SOM and
SOMe because the parameter L (luminosity) plays an
important role due to the colors of the soil samples
vary from white (Calcite) to black (humidified organic
matter) (Figure 2).
The content of exchangeable cations (Ca, Mg,
Na, and K), the pH value, electrical conductivity, and
the cation exchange capacity are typical of soils de-
veloped on limestone (Bautista et al. 2011).
Calcite and, to a lesser extent, quartz are the
minerals that appear in the diffractograms. Once the
Calcite is eliminated with HCl, other minerals appear,
such as Tosudite (white, light yellow, light green),
Hematite (red), Dickite(white), Boehmite (white), and
Goethite (brown) (Figure 4); in addition, the sample
turns darker in color because both the Calcite and the
Quartz are white, which gives the soil sample more
luminosity. As occurs in Leptosols of karst origin of
the Yucatan peninsula (Bautista et al. 2011). The
color of the mineral fraction of the soil must be taken
into account because, in some cases, it is the one that
dominates the color of the soil, mainly iron minerals
(Barron y Torrent, 1986, Schwertmann1993, Schulze
et al. 1993).
Formation of soil sample groups by color
According to Simon et al. (2020), SOM has a
great soil darkening capacity, which even masks the
white colors of minerals. This property can be ade-
quately predicted through color due to the strong re-
lationship between the color and nature of the soil or-
ganic matter. Humic acids with higher carbon rich-
ness had darker colors (Shields et al. 1968), which
explains the dark gray color of group V and the higher
percentage of organic matter.
In the opinion of Chen et al. (2018), the CIE
L * a * b * color system alone can predict the per-
centage of SOM, although multiple linear regression
analysis can marginally improve the prediction; the b
* coordinate correlates negatively with the concentra-
tion of SOM, mainly expresses the yellowish colors,
so it is related to the low concentration of SOM; the
a * coordinate, on the other hand, exhibits a stronger
correlation with brownish colors, while the L * coordi-
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Proxy method to estimate SOM
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Figure 4. Soil minerals with (a) and without calcite (b). Ca = calcite, Q = Quartz, T = Tosudite,
H = Hematite, D = Dickite, B = Boehmite, G = Goethite.
nate shows a low correlation with the concentration of
the SOM.
The five equations obtained (Table 4) for each
group can be used to estimate organic matter in large
collections of soil samples in karst areas; however,
further equations must be generated for soil samples
with other colors such as reds, yellows and browns
that also exist in karstic zones from peninsula of Yu-
catan (Bautista et al 2003, Bautista et al. 2005,
Bautista et al. 2011).
The association between soil color and organic
matter is widely known (Schulze et al. 1993); the
mathematical model proposed between the color
components (L*a*b) and the percentage of organic
matter is relevant in this study. In this same sense,
other mathematical models have been proposed
(Spielvogel et al. 2004, Domínguez et al. 2012,
Stiglitz et al. 2017, Chen et al. 2018); however, they
are very different because the soils are also different
in mineralogy and the type of organic matter and par-
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ticle size.
The SOMI allowed to estimation the soil’s or-
ganic matter using both Power and Logarithmic fit.
The grouping of soil samples by color allowed to
describe a linear relationship between the soil color
and its organic matter percentage, which improved
the efficiency of this proxy technique. The darker or
lighter colors as dark grey and pinkish-white, showed
a higher level of R2, concerning other colors as
brownish grey, grey, and greyish brown. Thus, the
correlation sequence of color groups is V (dark grey)
> I (pinkish white) >II (brownish-grey) >III (grey) >IV
(greyish brown). For the karstic conditions of the Yu-
catan Peninsula, the study of soil color (SOMI and
color parameters) may be considered useful for the
estimation of the organic matter in large collections
of soil samples, even in samples with low SOM con-
tent. Furthermore, this technique is much cheaper
and less time-consuming compared to the traditional
methodology because of its straightforward treatment
allowing fast measurements. Another notable benefit
of this technique for estimating soil organic matter is
that, unlike the conventional method, it produces no
environmentally harmful waste products.
The DGAPA, UNAM provided financial support
for the key project PAPIIT 29-IN209218. This work
was also supported by the CONACYT with project
code 283135. “Los análisis de DRX fueron realizados
en el Laboratorio Nacional de Nano y Biomateriales,
Cinvestav-IPN (proyectos FOMIX-Yucatán 2008-
108160, CONACYT LAB-2009-01-123913, 292692,
294643, 188345 y 204822). Se agradece a la Dra.
Patricia Quintana por el acceso a LANNBIO, al M.C.
Daniel Aguilar Treviño por la obtención de los difrac-
togramas y al Ing. Emilio Corona por mantenimiento
correctivo del difractómetro D-8 Advance”.
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Full-text available
Color is an important soil property and often used to infer soil properties and delineate soil horizons. We in- vestigated the effect of soil texture, carbon, total elements, and particle-size fractions on the color of sandy soils using five color models. In total, 915 soil samples were collected to a maximum depth of 220 cm at 400 locations in the Wisconsin Central Sand Plains, and the soils were sandy throughout. The samples were scanned using a visible-near infrared spectrometer, with soil color models (HSV, RGB, CIE L*a*b*, CIE L*u*v*, and redness index (RI)) extracted from the reflectance spectra. Cubist models were used to predict each of the soil color coordinates from the soil properties. The models showed high prediction accuracy for V, R, SRGB, L*, a*, b*, u*, and v* color coordinates for both calibration and validation. The CIE L*a*b* color model was generally better than other color models. Silt and Fe were used in all of the Cubist models, while sand, clay, carbon, Al, Si, Mn, and Zn were used in most models. The 45–125 μm, 125–250 μm, and 1000–2000 μm fractions affected the soil color as opposed to the 250–500 μm and 500–1000 μm fractions. A clustering analysis on the CIE L*a*b* color model showed that soil lightness was higher with higher sand content, but lower with an increase in silt and clay content, carbon, Fe, Al, Zn, and Mn. The LUCAS dataset has approximately 20,000 soil samples and was used to explore the relationships between soil color and soil properties in sandy soils and to test the robustness of the model established with the Central Sands dataset. The Cubist models developed from either dataset (Central Sands, LUCAS) were not useful to predict CIE L*a*b* color using the other dataset.
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Quantifying soil organic carbon (SOC) is important for soil management, precision agriculture, soil, mapping and carbon dynamics research. Inexpensive sensor technologies offer the potential for rapid quantification of SOC in laboratory samples as well as in the field. The objective of this study was to use a commercially-available color sensor to develop SOC prediction models for both dry and moist soils from the Piedmont region of South Carolina. Thirty-one soil samples were analyzed for lightness to darkness, redness to greenness, and yellowness to blueness (CIEL*a*b*) color using a Nix Pro (TM) color sensor. Soil color was measured under both thy and moist soil conditions and the depth of each soil sample was also recorded. Using L*, a*, b* and soil depth for each sample as initial predictors, regression analyses were conducted to develop SOC prediction models for dry and moist soils. The resulting residual plots, root mean squared errors (RMSE), and coefficients of determination (R-2) were used to assess model fits for predicting the SOC content of soil. Cross validation was conducted to determine the efficiency of the predictive models and the mean squared prediction error (MSPE) was calculated. The final models included soil depth, L*, and a* as independent variables (dry soils R-2 = 0.7978 and MSPE = 0.0819, moist soils R-2 = 0.7254 and MSPE = 0.1536). The results suggest that soil color sensors have potential for rapid SOC determination, and soil depth and color are useful in predicting SOC content in soils.