I
·. IGZ
Organized under the auspices of the International Union of Soil Sciences
Working Group on Proximal Soil Sensing
Bornimer Agrartechnische Berichte
Heft 82
Potsdam-Bornim 2013
3rd Global Workshop on
Proximal Soil Sensing 2013
26 – 29 May 2013
Potsdam, Germany
International Union of Soil Sciences
Working Group on Proximal Soil Sensing
Bornimer Agrartechnische Berichte
Heft 82
Potsdam-Bornim 2013
Editors:
Dr. Robin Gebbers
Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V.
Dr. Erika Lück
Universität Potsdam
Dr. Jörg Rühlmann
Leibniz-Institut für Gemüse- und Zierpflanzenbau
Layout and typesetting:
Dipl.-Ing. (FH) Katrin Witzke
Publisher:
Published by the Leibniz-Institute for Agricultural Engineering Potsdam-Bornim (Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V., ATB) with support by the German Federal Ministry of Food, Agriculture and
Consumer Protection (Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz, BMELV)
and the Ministry of Science, Research and Culture of the State of Brandenburg (Ministerium für Wissenschaft, Forschung und Kultur, MWFK).
Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V.
Max-Eyth-Allee 100
14469 Potsdam-Bornim
:
+49 (0)331-5699-0
Fax:
+49 (0)331-849
E-mail:
[email protected]
Internet:
http://www.atb-potsdam.de
The authors are solely responsible for the content of the proceedings. The proceedings do not necessarily reflect the official position of the ABT, and its printing and distribution does not constitute an
endorsement of views which may be expressed.
All rights reserved. Reproduction of this work in whole or part requires written permission of the Publisher.
Herausgegeben vom Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. (ATB) mit Förderung durch
den Bund (Bundesministerium für Ernährung, Landwirtschaft und Verbraucherschutz BMELV), das Land
Brandenburg (Ministerium für Wissenschaft, Forschung und Kultur MWFK).
Für den Inhalt der Beiträge zeichnen die Autoren verantwortlich.
Eine Weiterveröffentlichung von Teilen ist unter Quellenangabe und mit Zustimmung des Leibniz-Instituts
für Agrartechnik Potsdam-Bornim e.V. möglich.
ISSN
0947-7314
© Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V., 2013
Buttafuoco, Guagliardi, Bastone, Ricca, Conforti
236
Using Reflectance Spectroscopy for the assessment of soil
potentially toxic elements (PTEs) in agricultural soils: an
application in southern Italy
G. Buttafuoco*, I. Guagliardi, L. Bastone, N. Ricca, M. Conforti
Institute for Agricultural and Forest Systems in the Mediterranean, National Research Council of
Italy, Via Cavour 4-6, 87036, Rende CS, Italy
*
E-mail: [email protected] Abstract
Assessing soil potentially toxic elements (PTEs) contamination is an important objective, as high levels of some trace elements may be ecotoxicologically relevant and
harmful when passing the food chain. This study focused on agricultural soil in southern
Italy. The study examined the feasibility of predicting some PTEs concentration in soil
samples using reflectance spectroscopy. Partial least squares regression was used to
establish the relationship between reflectance spectra in the visible-near-infrared (VisNIR) region and PTEs. Among the PTEs analysed, only for Pb and V have been obtained successful results.
Keywords: potentially toxic elements (PTEs), visible-near infrared spectroscopy, PLSR.
1
Introduction
Soil monitoring for food safety and for general environmental control requires soil sampling and chemical analyses for determining the concentration of potentially toxic elements (PTEs). High concentrations of PTEs in soil pose a threat for human health because they can be taken up into the trophic chain via assimilation by plants. Reflectance
spectroscopy within the visible-near infrared (Vis-NIR) region has been widely used to
predict spectrally soil properties quantitatively (Ben-Dor et al., 1999; Brown et al., 2006;
Viscarra Rossel et al., 2006). Compared to conventional laboratory analysis, Vis-NIR
spectroscopy techniques are rapid, relatively inexpensive, require minimal sample
preparation, are non-destructive, require no hazardous chemicals and several soil properties can be measured from a single scan. Quantification and relationships between
spectral reflectance and soil properties, such as soil organic matter content, iron oxides,
calcium carbonate content, soil heavy-metal (Cu, Pb, and Zn), etc. have been established in many studies by means of statistical models (Kemper and Sommer, 2002; Viscarra Rossel et al., 2006). The main aim of this study was to examine the feasibility of
predicting some PTEs concentration in soil samples using reflectance spectroscopy.
Bornimer Agrartechnische Berichte  Heft 82
ISSN 0947-7314
Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. (ATB)
3rd Global Workshop Proximal Soil Sensing
2
237
Materials and methods
The experimental area (100 m x 100 m) was an olive orchard located in southern Italy
(Calabria) where, at 100 locations, topsoil (0-0.20 m) samples were collected (Figure 1).
Soil samples were ground, dried, weighed, sieved (<2 mm), and then split into two subsamples: one was used for spectroscopic measurements, while the other for conventional laboratory analysis. The samples for conventional laboratory analysis were digested in aqua regia and analyzed for cadmium (Cd), chromium (Cr), copper (Cu), lead
(Pb), thallium (Tl), vanadium (V), and zinc (Zn) content using Inductively Coupled Plasma Mass Spectrometry (ICP-MS).
Figure 1. Study area and sampling point (black cross) locations.
To minimize the noise level, every measurement was recorded as the average of four
consecutively acquired spectra. Finally, conversion to spectral reflectance was
performed by means of dividing the radiance of spectra of the samples by the radiance
spectrum of the standard white reference. Prior to statistical analysis, the spectral
reflectance curves were re-sampled at 10 nm interval, reducing the number of
wavelength from 2151 to 216, which smoothed the spectra and reduced the problem of
over-fitting (Kemper and Sommer, 2002). Partial Least Squares Regression (PLSR)
analysis (Geladi and Kowalski, 1986) was applied to establish the relationships between
spectral reflectance and measured soil PTEs. In this study, leave-one-out crossvalidation (Efron and Tibshirani 1994) was used to determine the number of factors to
be retained in the calibration models. 30 bilinear factors (latent variables) were tested.
The prediction performance was evaluated on predicted and measured tracer values,
2
using the adjusted coefficient of determination ( Radj
) and the root mean square error
(RMSE) of cross-validation. RMSE of predictions was used to select the optimal crossvalidated calibration model. In addition, to evaluate the performance of prediction
models, the residual predictive deviation (RPD) was used. RPD is defined as the ratio of
standard deviation of measured values of the soil properties to the RMSE (Williams,
2001).
PLSR analysis was performed using the PArLeS vs3.1 software developed by Viscarra
Rossel (2008). Finally, using Ordinary Kriging (Webster and Oliver, 2007), the values of
the spectrally predicted PTEs were interpolated and mapped.
Bornimer Agrartechnische Berichte  Heft 82
ISSN 0947-7314
Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. (ATB)
Buttafuoco, Guagliardi, Bastone, Ricca, Conforti
238
3
Results and discussion
Descriptive statistics of the PTEs data are summarised in Table 1, which shows that
zinc is the most abundant PTE (mean content 83.30 mg kg-1), while cadmium is the less
abundant (mean content 0.40 mg kg-1). The PTEs concentrations are less than the ones
for urban and peri-urban soils, but there are no PTEs data for agricultural soils in the in
the neighbourhood of the study area. Table 2 reports the results of the PLSR: the latent
factors in the PLSR models used for prediction ranged from 4 to 17. Results of the level
of agreement between measured and predicted values show that better results have
2
2
 0.74 ; RMSE=1.86; RPD=2.0) and V ( Radj
 0.69 ;
been obtained for Pb ( Radj
RMSE=5.27; RPD=2.0). Estimates of moderate quality of the prediction models have
2
been obtained for Cr and Tl with Radj
of 0.64 and 0.62, respectively. The remaining elements (Cu, Cd and Zn) have been poorly predicted (Table 2); therefore, these results
can be considered to be generally used for an approximate screening.
Table 1. Basic statistics of PTEs concentrations (mg kg-1 dry weight).
PTE
Min
Max
Mean
Median
S. D.
CV
Skewness
Kurtosis
Cd
0.08
0.40
0.18
0.17
0.07
0.38
0.83
3.33
Cr
20.79
103.90
39.68
37.88
10.22
0.26
2.60
16.60
Cu
9.60
33.37
19.31
18.98
3.88
0.20
0.87
4.56
Pb
0.22
21.19
13.06
12.51
3.83
0.29
0.11
3.01
Tl
0.18
16.85
0.62
0.33
1.69
2.74
8.93
84.84
V
27.44
91.81
51.34
49.46
10.21
0.20
0.86
4.64
Zn
33.46
172.36
83.30
77.52
25.58
0.31
1.42
5.48
S.D. Standard deviation; CV Coefficient of variation
2
Table 2. Results of PLSR spectral analysis. NF: number of factors; Radj
: coefficient of
determination; RMSE: root mean square error; RPD: residual predictive deviation.
PTE
NF
2
Radj
RMSE
RPD
Pb
8
0.74
1.86
2.0
V
7
0.69
5.27
2.0
Cr
4
0.64
4.81
1.7
Ti
6
0.62
92.21
1.8
Cu
10
0.57
2.57
1.5
Cd
5
0.45
0.05
1.4
Zn
17
0.44
19.97
1.3
Bornimer Agrartechnische Berichte  Heft 82
ISSN 0947-7314
Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. (ATB)
3rd Global Workshop Proximal Soil Sensing
239
Among the PTEs only the predicted values of Pb and V, which have obtained the better
results, have been interpolated and mapped. In the variographic analysis, in order to
check the behaviour of Pb and V data in terms of anisotropy, two variogram maps (not
shown) were calculated revealing no significant difference as a function of direction for
V and an anisotropic behaviour for Pb. Then, a bounded isotropic variogram model was
assumed for V and the variogram fitted to the experimental values of semivariance,
included a nugget effect [sill=12 (mg kg-1)2] and an spherical model (Webster and
Oliver, 2007) with a range of about 74 m and a sill equal to 81 (mg kg-1)2. For Pb, the
fitted variogram in the direction N75E included a nugget effect [sill=1 (mg kg-1)2]) and a
K-Bessel model (Webster and Oliver, 2007) with a range of about 25 m and a sill of 16
(mg kg-1)2, while in the direction N165E the fitted variogram included a cubic model with
a range of about 80 m and a sill equal to 4.34 (mg kg-1)2. The above variogram models
were used with ordinary kriging to produce the maps of spectrally predicted Pb and V.
4
Conclusions
The study allowed to examine the feasibility of predicting some PTEs concentration in
soil samples using reflectance spectroscopy. Only for Pb and V have been obtained
successful results. The difficulty in predicting spectrally PTEs could be due to the fact
that pure PTEs do not exhibit characteristic absorption in the Vis-NIR region and that
they are detected indirectly taking into account the binding reaction of the PTEs onto the
mineral surface. The study was the first step to in-field spectroscopy for PTEs.
Figure 2. Maps of spectrally predicted Pb and V obtained using ordinary kriging.
Acknowledgements
This project was funded by the ACTION 2 - Public research laboratory mission oriented,
APQ – Scientific Research and Technological Innovation in Calabria Region. Laboratory
for Food Quality, Safety and Origin (QUASIORA).
Bornimer Agrartechnische Berichte  Heft 82
ISSN 0947-7314
Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. (ATB)
240
Buttafuoco, Guagliardi, Bastone, Ricca, Conforti
References
BEN-DOR, E., IRONS, J.R., EPEMA, G.F. 1999. Soil reflectance. In: Rencz, A.N. (Ed.), Remote
Sensing for the Earth Sciences. Manual of Remote sensing, vol. 3. Wiley & Sons, New York,
pp. 111–188.
BROWN, D.J., SHEPHERD, K.D., WALSH, M.G., DEWAYNE MAYS, M., REINSCH, T.G. 2006. Global
soil characterization with VNIR diffuse reflectance spectroscopy. Geoderma 132, 273–290.
EFRON, B., TIBSHIRANI, R. 1994. An introduction to the bootstrap. Monographs on statistics and
applied probability. Vol. 57, Chapman and Hall, London, UK, 436pp.
GELADI, P., KOWALSKI, B.R. 1986. Partial Least-Squares regression: a tutorial. Analytica
Chimica Acta 185, 1-17.
KEMPER, T., SOMMER, S. 2002. Estimate of heavy metal contamination in soils after a mining
accident using reflectance spectroscopy. Environmental Science & Technology 36, 2742–
2747.
VISCARRA ROSSEL, R.A., WALVOORT, D.J.J., MCBRATNEY, A.B., JANIK, L.J., SKJEMSTAD, J.O.
2006. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for
simultaneous assessment of various soil properties. Geoderma 131, 59–75.
VISCARRA ROSSEL, R.A. 2008. ParLeS: Software for chemometric analysis of spectroscopic
data. Chemometrics and Intelligent Laboratory Systems 90, 72-83.
WEBSTER, R., OLIVER, M. A. 2007. Geostatistics for environmental scientists. Wiley, Chichester,
UK.
WILLIAMS, P.C. 2001. Implementation of near-infrared technology. In: Williams, P.C., Norris, K.
(Eds.). Near-Infrared Technology in the Agricultural and Food Industries, second ed.
American Association of Cereal Chemists Inc., St. Paul, Minnesota, USA, pp. 145-169.
Bornimer Agrartechnische Berichte  Heft 82
ISSN 0947-7314
Leibniz-Institut für Agrartechnik Potsdam-Bornim e.V. (ATB)