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Estimation of total iron content infloodplain soils using VNIR spectroscopy– a case study in the Le'an Riverfloodplain, ChinaYaolin Liu a b & Yiyun Chen a ba Department of Resources-Environment and Urban-Rural PlanningManagement , School of Resource and Environmental Science,Wuhan University , Wuhan , 430079 , PR Chinab Key Laboratory of Geographic Information System of the Ministryof Education , Wuhan University , Wuhan , 430079 , PR ChinaPublished online: 02 May 2012.
To cite this article: Yaolin Liu & Yiyun Chen (2012) Estimation of total iron content in floodplainsoils using VNIR spectroscopy – a case study in the Le'an River floodplain, China, InternationalJournal of Remote Sensing, 33:18, 5954-5972, DOI: 10.1080/01431161.2012.676687
To link to this article: http://dx.doi.org/10.1080/01431161.2012.676687
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International Journal of Remote SensingVol. 33, No. 18, 20 September 2012, 5954–5972
Estimation of total iron content in floodplain soils using VNIRspectroscopy – a case study in the Le’an River floodplain, China
YAOLIN LIU*†‡ and YIYUN CHEN†‡†Department of Resources-Environment and Urban-Rural Planning Management,School of Resource and Environmental Science, Wuhan University, Wuhan 430079,
PR China‡Key Laboratory of Geographic Information System of the Ministry of Education,
Wuhan University, Wuhan 430079, PR China
(Received 7 December 2010; in final form 27 February 2012)
Accumulation of heavy metals has become a serious environmental issue inChina, especially in the floodplains downstream from mining and smelting sites.Estimation of total iron (Fe) content at the regional scale becomes particularlyimportant because of the heavy metal sorption of Fe oxide. A novel method forestimating total Fe content is proposed using visible and near-infrared (VNIR)spectroscopy and partial least squares regression (PLSR). Our study focuses on theLe’an River floodplain, Jiangxi Province, China, which houses the largest coppermining corporation in China, as this area has suffered a series of environmental set-backs because of the extraction of non-ferrous metals. Our study employs PLSR tosummarize the relationship between VNIR reflectance spectra and the total Fe con-tents of collected soil samples. More specifically, our study aims to (1) explore thecorrelation between VNIR and total Fe content, (2) assess the relationship betweenVNIR determination of total Fe content and the preprocessing of soil samplesand (3) evaluate the performance of data transformation methods in PLSR. ThePLSR model with transformed total Fe content and continuum removal spectrawas finally chosen for estimating the total Fe content from both pretreated soilsamples (coefficient of determination for prediction, R2
p = 0.66) and soil sam-ples without pretreatment (R2
p = 0.55). Therefore, VNIR spectroscopy could bean alternative method for estimating total Fe content at the regional scale.
1. Introduction
There has been ongoing interest in the application of visible and near-infrared (VNIR)spectra to characterize soil properties (Brown et al. 2006, Viscarra Rossel et al. 2006,Reeves III 2010). The accurate estimation of total iron (Fe) content in soil usingVNIR spectra attracts particular concern not only because of the molecular absorp-tion mechanism of Fe oxide, which provides the physical foundation to estimate totaliron content from the spectra (Sherman and Waite 1985), but also because of the com-plexity of soil diffuse reflectance, which is usually a combination of a variety of soilconstituents (Viscarra Rossel and Behrens 2010). Oxidation of some Fe sulphides canlead to severe environmental degradation (Åström and Corin 2000, Fanning et al.
*Corresponding author. Email: [email protected]
International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online © 2012 Taylor & Francis
http://www.tandfonline.comhttp://dx.doi.org/10.1080/01431161.2012.676687
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VNIR spectroscopy and total iron content in a mining area 5955
2002). Moreover, Fe transformations in soil can greatly affect the cycling of tracemetals and other contaminants (Tack et al. 2006, Burton et al. 2008). Wu et al.(2005) concluded in their case study that the major mechanism for predicting spec-trally featureless heavy metal elements lies in element sorption by Fe oxides, which isspectrally active. Therefore, the fast and accurate estimation of total Fe content couldbe important for monitoring heavy metal contamination in the soil.
The prediction accuracy reported by a number of researchers suggests that VNIRspectroscopy coupled with partial least square regression (PLSR) might replace con-ventional chemical analyses (Viscarra Rossel et al. 2009, Canasveras et al. 2010). Mostprevious studies, however, have been carried out at field or local scales with homoge-nous soil samples in terms of soil type, topography, land use and land cover. Outliershave been eliminated despite their valid descriptions of sample properties. The fol-lowing question arises: is it feasible to estimate total Fe content from soil samplesthat are virtually unique? In other words, can models be accurately constructed usinggeographically diverse and relatively dispersed sample sets?
Total Fe content can vary greatly in samples collected at a regional scale (e.g. froma floodplain area of 5000 km2). Thus, non-normality could be the usual case in datadistribution. Mean centring in the PLSR procedure could normalize the data, but thedata distribution remains the same. Few normality transformations have been appliedto response variables, as it is not essential for the response variable to be normal inPLSR. Logarithmic transformation of the response variable has proved to be usefulfor PLSR (Wold et al. 2001), and it is necessary in the case of skewed distribution(Vasques et al. 2008). A second question arises: if the transformations on the responsevariable could be inverted to be helpful for better performance of the PLSR model,why not try them?
This study, on the floodplain along the Le’an River and its two branches in theJiangxi Province of China, aimed to explore the possibilities of accurate estimation oftotal Fe content concentrations in soil samples using laboratory-based VNIR data.
2. Materials and methods
2.1 Description of the study area
The study area is located between 28.7◦–29.3◦ N and 116.5◦–117.9◦ E in the Le’anRiver floodplain, Jiangxi Province, China (figure 1). A relatively wide range of landuse (e.g. pasture, industrial areas, etc.) and land cover (e.g. orchards and paddy fields)characterizes this area. The elevation is about 500–1600 m in the east and 0–200 min the west. According to the United States Department of Agriculture (USDA) SoilTaxonomy, two soil orders dominate the study area – ultisols and vertisols. In thisregion, the Le’an River runs 279 km from east to west into the Poyang Lake, whichis presently the largest freshwater lake in China (Liu et al. 2003). The drainage areaof the Le’an River is around 8989 km2, and it covers several mining regions suchas the Dexing Copper Mine and the Yishan Lead–Zinc Mine (Zeng et al. 2007). TheDexing Copper Mine is the largest outcrop copper mine in China, and the Dawu Rivercrosses this mine area and drains into the Le’an River (Wu et al. 2009). The YishanLead–Zinc Mine is located downstream of the Jishui River, which is the largest branchof the Le’an River (Zeng et al. 2007).
The mining activities in the Dexing and Yishan mining areas produce large amountsof acid mine drainage, which contains rich heavy metals such as copper (Cu), lead (Pb)and zinc (Zn) (Zeng et al. 2007, Wu et al. 2009). The drainage with high heavy metalconcentrations enters the Le’an River and then the Poyang Lake through the Dawu
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5956 Y. Liu and Y. Chen
117° 30′ 0″ E
117° 30′ 0″ E
117° 0′ 0″ E
117° 0′ 0″ E
29
° 0′ 0
″ N2
8° 3
0′ 0
″ N
29
° 0′ 0
″ N2
8° 3
0′ 0
″ N
River
LakeLand-use type
ForestPasture
River bench
Agricultural land
Poyang Lake
Chang River
Le'an River
Jishui River
1
2Dawu River
Studyarea
N
EW
S
0 5 10 20
km
Figure 1. Map showing Poyang Lake, Le’an River, Jishui River, Dawu River, YishanLead–Zinc Mine (1), Dexing Copper Mine (2) and the land-use type of the sampling sites.
or Jishui rivers, and may induce serious environmental and ecological impacts on thewater, sediments and plants in these regions (He et al. 1998, Liu et al. 2003, Zeng et al.2007).
2.2 Soil samples collection
From 29 October 2009 to 1 November 2009, a total of 71 top layer (0–15 cm) soilsamples were collected in the Le’an River floodplain, including 45 samples from agri-cultural land, 11 from forest land, 7 from pasture and 8 from river benches, ensuringa representative sample set of the Le’an River floodplain. The land-use classificationof the sample sites is based on the field survey and is not absolute. Most of the sam-ple sites are along the Le’an River drainage, within a buffer zone of 50 m. About75% of the samples (53 sample sites) were collected around the middle and upperreaches of the Le’an River, where the Dexing Copper Mine and the Yinshan Lead–Zinc Extraction Facility are located. Four portions of the topsoil samples (0–15 cm)within a 10 m2 plot were collected and mixed at each sample site. Stones and debriswere removed. Approximately 1 kg of sample was stored in a plastic bag, which wasnumbered with a tag. Geographical coordinates were also recorded by a handheld GPSwith a positional error of less than 10 m. All soil samples were taken to the laboratoryon the fourth day of the fieldwork.
2.3 Chemical analyses of soil properties
The soil samples were first air-dried in the laboratory at 20–25◦C for 2 days. The driedsoil samples were then gently crushed in a porcelain mortar to break down large aggre-gates and sieved using a 0.2 mm stainless steel sieve. It was assumed that the differencesbetween the samples with and without pretreatment existed only in soil particle size
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VNIR spectroscopy and total iron content in a mining area 5957
and water content. Total contents of Fe were determined by wavelength-dispersiveX-ray fluorescence (XRF) spectroscopy (X-50TM Mobile XRF Analyzer; Innov-XSystems, Woburn, MA, USA). Before XRF analysis, all samples were homogenized.An elastic plastic collar was placed on a stainless steel plate; about 40 g of the sam-ple was filled in and afterwards compressed into uniform pellets of 45 mm diameterusing a semi-automatic press machine. The known advantage of XRF is the simulta-neous estimation of several metal elements without producing much chemical waste.Soil organic matter (SOM) was determined by the wet oxidation method at 180◦Cwith a mixture of potassium dichromate and sulphuric acid (Agricultural ChemistryCommittee of China 1983). SOM content was investigated for its ability to mask Feoxide absorptions by a broad absorption near 650 nm (Viscarra Rossel et al. 2010).
2.4 VNIR spectral analyses
A ASD FieldSpec3 portable spectral radiometer with a wavelength of 350–2500 nmwas used to measure the spectral reflectance of the soil samples. Its sampling inter-val and spectral resolution are 1.4 and 3 nm for the 350–1000 nm range and 2 and10 nm for the 1000–2500 nm range (http://www.asdi.com). With differently pretreatedsoil samples, two sets of reflectance spectra were collected, namely Ori-01 and Ori-02.Ori-01 was from the fresh soil samples without any pretreatment except removal ofstones and debris. Ori-01 differed a lot in soil particle size and moisture. Ori-02 wascollected afterwards from the samples that had gone through the air-drying, grindingand sieving procedures mentioned in §2.3.
The spectra scanning procedure was carried out in a dark room at night, minimizingthe influence of external light. Three people with dark clothes were involved, withone operating a laptop connected to the FieldSpec 3 spectroradiometer and the othertwo preparing the samples for measurement. A white light source matched with thespectroradiometer was used with a 45◦ incident angle. Two square pieces of blackvelvet cloth with side lengths of about 35 cm were used in turn to hold the soil samples.Soil samples of approximately 500g, spanning diameters of approximately 20 cm, werescanned by the spectroradiometer at a distance of 12 cm from probe to sample surfaceand a zenith angle of 90◦. A brush was used to remove the soil debris when changingthe samples.
The spectral radiance over a standardized white Spectralon® panel (Labsphere,www.labsphere.com) was measured for every ten samples. Then the spectral radianceover the soils was scanned (10 internal scans of 100 ms each). By dividing the radianceover the Spectralon® panel, the reflectance spectrum of each soil sample was automat-ically derived and displayed on the screen of the laptop. Data were exported in ASCIIformat for further analyses.
2.5 Spectral preprocessing
The spectral preprocessing technique is considered to be an integral part ofchemometrics modelling, the suitable application of which can largely eliminate thebaseline shift and non-linearities (Balsam and Deaton 1996, Rinnan et al. 2009).Wold et al. (2001) also suggested that applying transformations on predictors couldbe useful for model calibration. This study applied seven different kinds of spectralpreprocessing and transformation techniques over both Ori-01 and Ori-02: first andsecond derivatives (Savitzky–Golay derivation with 11 points and a second-order poly-nomial), absorbance (log[1/reflectance]), first and second derivatives of absorbance,
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5958 Y. Liu and Y. Chen
standard normal variate (SNV) and continuum removal. The continuum removaltechnique has been widely used in VNIR spectroscopy because of its ability to iso-late particular absorption features in diffuse reflectance spectra (Gomez et al. 2008,Viscarra Rossel et al. 2009). Further details on these spectral transformations canbe found in a review by Rinnan et al. (2009). An Unscrambler®X (http://www.camo.com/) was used to perform these spectral transformations except for the continuumremoval process, which was performed using ENVI Version 4.5 (www.ittvis.com). Thealgorithms of these preprocessing techniques are not listed because of their wide appli-cation and support from a variety of software. Sixteen sets of spectra were finallyavailable for model calibrations, including Ori-01 and Ori-02.
2.6 Statistical analyses
Statistical analyses, including descriptive statistics, histograms, normal probabil-ity plots, box plots, natural logarithmic transformation, Box–Cox transformation,Lilliefors normality test and Spearman rank correlation analysis, were performedusing Matlab® (R2008a, MathWorks, www.mathworks.com).
Box plots are applied to identify outliers (Kerry and Oliver 2007, Zhang et al. 2009).Box–Cox transformation was applied in an attempt to transform the total Fe content(denoted by CFe) into normally distributed forms:
C∗Fe =
{ln(CFe) λ = 0Cλ
Fe−1λ
λ �= 0, (1)
where ∗ denotes the normally distributed form. As the Box–Cox transformation isa family of power transformations, different λ values correspond to different trans-formations. λ is the power of power transformations. The optimal λ value for thetransformation maximizes the log-likelihood function. This study used the ‘boxcox’function in Matlab®, and the optimal λ value was returned by the function. The ‘log’function in Matlab® was used for natural logarithmic transformation of CFe.
The Lilliefors normality test was applied to test the normality of total Fe con-tents before and after transformations. It is known as a kind of Komogorov–Smirnovtest for normality when mean and variance are unknown. Spearman rank correla-tion coefficients (r) were calculated to evaluate the relationships between reflectancewavelengths and total Fe content. Median and median absolute deviation (MAD) arecommonly used in geochemical studies as indicators of central tendency and disper-sion because the arithmetic mean and the standard deviation are parameters affectedby the presence of outliers (Salminen and Tarvainen 1997, Filzmoser et al. 2008).A Matlab® program was written to calculate MAD.
2.7 Modelling and fit assessment
A classic PLSR procedure was adopted (Wold et al. 1983). The maximum number oflatent variables used in the PLSR was set to seven. The optimum number of latentvariables used for regression was determined by comparing the cross-validated rootmean square error (RMSE) obtained with different numbers of latent variables. Thevalidation procedures of the PLSR model were based on the leave-one-out cross-validation method, which means that each sample was estimated by a PLSR modelthat was calibrated using the remaining samples. Details of the validation algorithm
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VNIR spectroscopy and total iron content in a mining area 5959
can be found in the Unscrambler®X (CAMO Software, www.camo.com) help docu-ment. This method has been used in similar studies (e.g. Huang et al. 2004, ReevesIII and Smith 2009). A validation procedure that used a randomly selected test setwas not adopted because of its potential overestimation (Brown et al. 2005). Also, theprediction accuracy could be dependent on the sample selection.
The coefficient of determination for prediction (R2p) was used to determine the best
calibration of the PLSR model. Root mean square error of prediction (RMSEP) wasnot used because the response variable was differently transformed or scaled, makingthe RMSE incomparable in units.
Signal noises were observed at both ends of the spectral curves (figure 3). Therefore,wavelengths of 350–359 nm and 2491–2500 nm were cut, and the remaining wave-lengths (360–2490 nm) were used in model calibrations. The modelling and fitassessment processes were carried out in Unscrambler®X. Calibration models ofPLSR were examined and recalculated using the function ‘plsregress’ in Matlab®
(R2008a) with the optimal latent variable number calculated in Unscrambler®X. Suchan examination ensured the validity of the results and the reproducibility of the workusing other software such as Matlab®. We noticed in our experiment that anotherPLSR function, ‘plsr’ in Matlab® (R2008a), did not ‘mean centre the data itself’, andthe calculation of the calibration model could be different from that of the ‘plsregress’function and Unscrambler®X. Therefore, the ‘plsr’ function was not adopted.
3. Results and discussion
3.1 Sample characterization
Summary statistics for total Fe content are provided in table 1. In the study area, theestimated concentrations of total Fe content ranged from 1.67% to 12.16%. The SOMranged from 3.59% to 43.31%. High values were found in agricultural land, while thelow ones may be from river benches and pasture.
The histogram, normal probability plot and box plot of total Fe content and itstransformations are shown in figure 2. For the total Fe content, the distribution ispositively skewed (skewness = 1.44) with a steep peak (kurtosis = 5.86). The Lillieforsnormality test and normal probability plot show evidence of non-normality. The boxplot suggests that four sample values can be deemed outliers. The null hypothesis ofnormality is not rejected at the confidence interval of 0.05 when the data is Box–Cox-transformed or natural-logarithmic-scaled. Box plots for these two data sets do notsuggest any outliers. Thus, it might be feasible to estimate total Fe content from soilsamples that are virtually unique.
3.2 Spectral transformations
Spectral transformations were plotted on a three-dimensional space (x-axis, wave-lengths; y-axis, total Fe content; z-axis, values of reflectance spectra and theirtransformation) (figure 3). A ‘gap’ was observed in the z-axis from 8% to 10%, dividingthe curves into two groups. This suggests that the curves were from the soil samplesthat varied in total Fe content. Signal noises were observed at both ends of the spectralcurves in most subplots. Such noises were stronger and more obvious in the subplotsfor the first- and second-derivative spectra over reflectance and absorbance spectra.Therefore, the wavelengths of 350–359 nm and 2491–2500 nm were cut, and the rest(360–2490 nm) were used in model calibrations. Comparisons between the reflectance
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5960 Y. Liu and Y. Chen
Tab
le1.
Des
crip
tive
stat
isti
csof
tota
lFe
cont
ent,
SOM
and
Lill
iefo
rsno
rmal
ity
test
resu
lts.
Min
.M
ax.
Mea
nM
edia
nM
AD
Kur
tosi
sSk
ewne
ssL
illie
fors
norm
alit
yte
st∗
Lill
iefo
rsno
rmal
ity
test
∗(B
ox-C
ox-t
rans
form
ed)
Lill
iefo
rsno
rmal
ity
test
∗(l
ogar
ithm
ic-s
cale
d)
Fe
(%)
1.67
12.1
64.
744.
631.
315.
861.
44N
on-n
orm
alit
yN
orm
alit
yN
orm
alit
ySO
M(%
)3.
5943
.31
22.3
023
.41
6.22
2.61
−0.0
4N
orm
alit
y
Not
es:M
AD
,med
ian
abso
lute
devi
atio
n.∗ L
illie
fors
norm
alit
yte
stw
assi
gnifi
cant
ata
confi
denc
ein
terv
alof
0.05
.
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VNIR spectroscopy and total iron content in a mining area 59611
0 8 6 4 2 02
46
Ku
rto
sis
= 5
.85
7
Ske
wn
ess =
1.4
44
4
Lill
iefo
rs t
est p
= 0
.01
52
77
81
01
20 –
3–2
–1
01
23
Sta
ndard
norm
al quantile
s
5
10
Quantiles of input sample
15
Fe
(%
)
24
68
10
12
0.9
90
.98
0.9
50
.90
0.7
5
0.5
0
Probability
0.2
5
0.1
00
.05
0.0
20
.01
(i)
(ii)
(iii
)
3
Sta
ndard
norm
al quantile
s
10
12 8 6 4 2 0
Ku
rto
sis
= 3
.08
96
Ske
wn
ess
= 0
.00
03
30
52
Lill
iefo
rs t
est p
= 0
.08
80
94
2.5
2.0
1.5
Tra
nsfo
rma
tio
n, λ
= 0
.02
53
75
1.0
–3
–2
–1
01
2
3.0
2.5
1.5
Quantiles of input sample
0.5 0
1.0
2.0
0.9
90
.98
0.9
50
.90
0.7
5
0.5
0
Probability
0.2
5
0.1
00
.05
0.5
11
.5
Fe
aft
er
Box–
Cox t
ran
sfo
rma
tio
n
22
.5
0.0
20
.01
(iv)
(v)
(vi)
Ku
rtosis
= 3
.07
13
Ske
wn
ess
= 0
.03
36
08
Lill
iefo
rs t
est p
= 0
.07
42
67
10
12 8 6 4 2 0
21
.51
–2
–1
0
Sta
ndard
norm
al quantile
s
12
3–3
2.5
1.5
Quantiles of input sample
0.5 0
1.02.0
0.5
11
.52
2.5
0.9
90
.98
0.9
50
.90
0.7
5
0.5
0
Probability
0.2
5
0.1
00
.05
0.0
20
.01
(vii
)(v
iii)
(ix)
Fig
ure
2.H
isto
gram
s,no
rmal
prob
abili
typl
ots
and
box
plot
sof
tota
lF
eco
nten
t(fi
rst
row
):(i
)hi
stog
ram
ofF
e(%
);(i
i)no
rmal
prob
abili
typl
ot;
(iii)
plot
ofsa
mpl
eda
tave
rsus
stan
dard
norm
al;B
ox–C
ox-t
rans
form
edw
ith
λ=
0.02
5(s
econ
dro
w);
(iv)
hist
ogra
mof
Fe
afte
rB
ox–C
oxtr
ansf
or-
mat
ion
atλ
=0.
0253
75;(
v)no
rmal
prob
abili
typl
ot;(
vi)
plot
ofsa
mpl
eda
tave
rsus
stan
dard
norm
al;a
ndna
tura
l-lo
gari
thm
ic-s
cale
d(t
hird
row
):(v
ii)hi
stog
ram
ofF
eaf
ter
log
tran
sfor
mat
ion;
(viii
)no
rmal
prob
abili
typl
ot;(
ix)
plot
ofsa
mpl
eda
tave
rsus
stan
dard
norm
al.
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5962 Y. Liu and Y. Chen
(a)
(i) (ii) (iii)
(iv) (v) (vi)
(vii) (viii)(b)
(i) (ii) (iii)
(iv) (v) (vi)
(vii) (viii)
Figure 3. Spectral transformations from soil samples. (a) Without pretreatment: (i) nopretreatment, (ii) first derivative, (iii) second derivative, (iv) absorbance, (v) first-derivativeabsorbance, (vi) second-derivative absorbance, (vii) continuum removal, (viii) standard normalvariate. (b) After air-drying, grinding and 0.02 mm sieving: (i) no pretreatment, (ii) first deriva-tive, (iii) second derivative, (iv) absorbance, (v) first-derivative absorbance, (vi) second-derivativeabsorbance, (vii) continuum removal, (viii) standard normal variate.
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VNIR spectroscopy and total iron content in a mining area 5963
spectra in figures 3(a) and (b) show that the reflectance values from the soil sampleswithout pretreatment were much lower than those of pretreated samples. The verti-cal span (values in y-axis) of reflectance curves in figure 3(a) vary significantly fromsample to sample. SNV transformation, however, showed a good ability to eliminatesuch differences, normalizing the reflectance spectra. The differences in the verticalspan of the reflectance curves from figure 3(b) are less significant in comparison. Thissuggests that air-drying, grinding and 0.2 mm sieving processes make the samplesmore homogeneous and, thus, their reflectance too. In the absence of soil samplepretreatment, SNV transformation can homogenize the vertical span of the curves.Absorption features around 650, 1400 and 1900 nm can be identified from the contin-uum removal subplots. Absorption depths in these three regions are similar in figure3(a). Nevertheless, the absorption depth around 650 nm stands out from the othertwo, which are known as water-related absorption features as shown in figure 3(b).This suggests that in this case, the spectral feature around 650 nm was more easilyinfluenced by the water content and particle size. This finding could be a complementto some previous studies (Van der Meer 1995).
3.3 Correlation analyses
The Spearman correlation between differently transformed spectra and total Fe con-tent was explored. The results are plotted in figure 4. The blue correlation coefficientcurve shows the soil sample without pretreatment while the black curve shows thesoil sample after air-drying, grinding and 0.02 mm sieving. Those bands that were insignificant correlation with total Fe content are marked in red (p < 0.05).
The ‘reflectance’ subplot suggests that only in the part of the visible region did thewavelengths significantly correlate with total Fe content – 350–450 nm for the soilsample without pretreatment and 350–550 nm for pretreated soil sample. The dif-ferences between these two curves mainly lie in the 350–600 nm region. They aremostly close to each other at wavelengths from 600 to 2500 nm. Moreover, curvesfrom the subplot ‘absorbance’ seem to be of mirror symmetry with those from the‘reflectance’ subplot. Their p-values of Spearman correlation turned out to be exactlythe same. Although the absorbance is a mirror replica of reflectance in this sense, thedistributional characteristics of the wavelength variable may vary. With wavelengthvariables at 1450 and 1900 nm as examples, histograms in figure 5 show such differ-ences. Moreover, the models from reflectance and absorbance may perform differently.Thus, analyses of these two might be interesting. Curves from the first-derivative-basedsubplots fluctuate more and severely. Bands around but not limited to 600, 800 and1200 nm are significantly correlated with total Fe content. Curves from the second-derivative-based subplots fluctuate so severely that little information can be extractedgraphically. In the continuum removal subplot, the curve from the samples withoutpretreatment has a curve shape similar to that from pretreated samples in the regionof 350–1200 nm. In the regions of 1350–1600 nm and 1800–2200 nm, the curve fromthe samples without pretreatment is flat and close to zero, while the black curves fluc-tuate significantly. This result suggests that in these two regions, there may be somesignificant correlations between total Fe content and spectral wavelengths (e.g. –OHvibration based), which are possibly masked due to water content and particle sizeeffect. In the ‘SNV’ subplot, significant wavelengths were found in the 600–800 and2200–2400 nm wavelength regions for samples without pretreatment, and in 400–550,
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5964 Y. Liu and Y. Chen
Correction coefficient
0.6
0.4
0.2
–0
.2
–0
.4
–0
.6
50
01
00
01
50
0
Wave
len
gth
(n
m)
20
00
25
00
0
(b)
Correction coefficient
0.6
0.4
0.2
–0
.2
–0
.4
–0
.6
50
01
00
01
50
0
Wave
len
gth
(n
m)
20
00
25
00
0
(c)
Correction coefficient
0.6
0.4
0.2
–0
.2
–0
.4
–0
.6
50
01
00
01
50
0
Wave
len
gth
(n
m)
20
00
25
00
0
(h)
Correction coefficient
0.6
0.4
0.2
–0
.2
–0
.4
–0
.6
50
01
00
01
50
0
Wave
len
gth
(n
m)
20
00
25
00
0
(f)
50
01
00
0
Correction coefficient
15
00
Wave
len
gth
(n
m)
20
00
25
00
0.6
(a)
0.4
0.2
–0
.2
–0
.4
–0
.60
Correction coefficient
0.6
0.4
0.2
–0
.2
–0
.4
–0
.6
50
01
00
01
50
0
Wave
len
gth
(n
m)
20
00
25
00
0
(g)Correction coefficient
50
01
00
01
50
0
Wave
len
gth
(n
m)
20
00
25
00
(d)
0.6
0.4
0.2
–0
.2
–0
.4
–0
.60
Correction coefficient5
00
10
00
15
00
Wave
len
gth
(n
m)
20
00
25
00
(e)
0.6
0.4
0.2
–0
.2
–0
.4
–0
.60
Un
tre
ate
d s
am
ple
s
Sig
nific
an
t b
an
ds (
p <
0.0
5)
Pre
tre
ate
d s
am
ple
s
Sig
nific
an
t b
an
ds (
p <
0.0
5)
Fig
ure
4.Sp
earm
anco
rrel
atio
nbe
twee
ndi
ffer
entl
ytr
ansf
orm
edsp
ectr
aan
dto
talF
eco
nten
t:(a
)re
flect
ance
,(b)
first
deri
vati
ve,(
c)se
cond
deri
vati
ve,
(d)
abso
rban
ce,(
e)fir
st-d
eriv
ativ
eab
sorb
ance
,(f)
seco
nd-d
eriv
ativ
eab
sorb
ance
,(g)
cont
inuu
mre
mov
alan
d(h
)st
anda
rdno
rmal
vari
ate.
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VNIR spectroscopy and total iron content in a mining area 5965
Reflecta
nce
20
10
k = 3.42s = –0.16
00 0.5 1
20
10
k = 3.42s = –0.35
00 0.5 1
20
10
k = 2.82s = 0.56
00 0.5
20
10
k = 2.85s = 0.62
00 0.40.2
Untreated soil samples
Pretreated soil samples
(a)
(i) (ii)
(iii) (iv)
1
2
Absorb
ance
k = 4.39s = 0.92
0
10
20
30
40
0 0.5 1
k = 4.52s = 1.06
0
10
20
30
40
0 0.5
20
10
k = 2.7s = 0.37
00 1 2
20
10
k = 2.69s = 0.37
00 1
Untreated soil samples
Pretreated soil samples
(i) (ii)
(iii) (iv)
(b)
Figure 5. Histograms of wavelength variables at 1450 and 1900 nm from: (a) reflectance at(i) 1450 nm, (ii) 1900 nm, (iii) 1450 nm, (iv) 1900 nm; and (b) absorbance at (i) 1450 nm, (ii)1900 nm, (iii) 1450 nm, (iv) 1900 nm with different soil sample preparations (kurtosis is denotedas k, skewness is denoted as s).
600–800, 1250–1600 and 2200–2500 nm wavelength regions for the pretreatedsamples.
The summaries of Spearman correlation analyses of total Fe content and VNIRspectra are shown in table 2. Wavelengths from 350 to 2500 nm were all used in thecalculation. Both the highest positive and lowest negative Spearman correlation coef-ficients (r) are listed with their corresponding wavelengths. The number of wavelengthsthat significantly correlated with total Fe content increased after sample pretreatmentexcept for the second derivative of spectra (which decreased slightly from 118 to 112).No correlation coefficients were larger than 0.80 or smaller than –0.80. Therefore, asingle-wavelength-based model might not be suitable for this case. Both the highestpositive r = 0.49 at the wavelength of 833 nm and lowest negative r = –0.64 at thewavelength of 531 nm were from the first derivative of absorbance (soil samples with-out pretreatment). For pretreated soil samples, the highest positive r = 0.54 at thewavelength of 361 nm and lowest negative r = –0.72 at the wavelength of 509 nm were,respectively, found from absorbance and first derivative of absorbance. The numberof significant wavelengths (p < 0.05) from reflectance was the same as those fromabsorbance.
3.4 Validation results and important wavelengths
The results presented in table 3 show that for the soil sample set without pretreatments,the PLSR model with continuum removal spectra as predictors and transformed (bothBox–Cox and natural logarithmic transformation) total Fe content as response vari-able achieved the highest prediction accuracy (R2
p = 0.55). The model explained 70%of the variation in total Fe content. For the soil sample set without pretreatments,the result was much better. R2
p reached 0.66, and 84% of the variation in total Fecontents could be explained by the model. Natural logarithmic transformation-basedmodels performed slightly better than those based on the Box–Cox transformation inboth pretreated soil samples and those without pretreatment. Thus, these two models
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5966 Y. Liu and Y. Chen
Tab
le2.
Sum
mar
ies
ofSp
earm
anco
rrel
atio
nan
alys
esof
tota
lFe
cont
ent
and
VN
IRsp
ectr
a.
Soil
sam
ples
wit
hout
pret
reat
men
tP
retr
eate
dso
ilsa
mpl
es
Spec
tral
tran
sfor
mat
ion
Max
rW
avel
engt
h(n
m)
Min
rW
avel
engt
h(n
m)
SigN
ums
Max
rW
avel
engt
h(n
m)
Min
rW
avel
engt
h(n
m)
SigN
ums
Refl
ecta
nce
0.04
610
−0.3
535
986
0.05
609
−0.5
436
117
0F
irst
deri
vati
ve0.
4255
0−0
.49
833
264
0.51
541
−0.4
985
040
5Se
cond
deri
vati
ve0.
4149
9−0
.41
501
118
0.36
2143
−0.3
411
5711
2A
bsor
banc
e0.
3535
9−0
.04
610
860.
5436
1−0
.05
609
170
Abs
FD
0.49
833
−0.6
453
131
90.
4882
1−0
.72
509
519
Abs
SD0.
3850
6−0
.39
2488
103
0.36
569
−0.4
014
1611
4C
onti
nuum
rem
oval
0.42
616
−0.6
298
977
90.
5022
82−0
.64
376
909
Stan
dard
norm
alva
riat
e0.
3613
96−0
.41
2211
543
0.48
1460
−0.5
123
8310
73
Not
e:A
bsF
D,fi
rst
deri
vati
veof
abso
rban
ce;A
bsSD
,sec
ond
deri
vati
veof
abso
rban
ce;M
axS r
,max
imum
ofS r
;Min
S r,m
inim
umof
S r;S
igN
ums,
num
ber
ofsi
gnifi
cant
wav
elen
gths
(p<
0.05
).
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VNIR spectroscopy and total iron content in a mining area 5967
Tab
le3.
Sum
mar
ies
ofth
eP
LSR
mod
el.
Soil
sam
ples
wit
hout
pret
reat
men
tSo
ilsa
mpl
eaf
ter
air-
dryi
ng,g
rind
ing
and
0.2
mm
siev
ing
Tota
lFe
cont
ent
Box
–Cox
-tr
ansf
orm
edL
ogar
ithm
ic-s
cale
dTo
talF
eco
nten
tB
ox–C
ox-
tran
sfor
med
Log
arit
hmic
-sca
led
Spec
tral
tran
sfor
mat
ion
No.
ofLV
sR
2 cR
2 p
No.
ofLV
sR
2 cR
2 p
No.
ofLV
sR
2 cR
2 p
No.
ofLV
sR
2 cR
2 p
No.
ofLV
sR
2 cR
2 p
No.
ofLV
sR
2 cR
2 p
Refl
ecta
nce
40.
510.
384
0.48
0.37
40.
480.
375
0.58
0.45
50.
620.
525
0.62
0.52
Fir
stde
riva
tive
30.
850.
071
0.48
10.
483
0.84
0.07
30.
820.
113
0.82
0.11
Seco
ndde
riva
tive
10.
371
0.37
0.01
10.
370.
011
0.38
10.
371
0.37
Abs
orba
nce
20.
300.
182
0.26
0.15
20.
260.
157
0.71
0.54
70.
720.
607
0.72
0.60
Abs
FD
10.
131
0.09
10.
095
0.75
0.19
50.
760.
215
0.76
0.21
Abs
SD1
0.12
10.
081
0.07
10.
201
0.18
10.
18C
onti
nuum
rem
oval
60.
720.
536
0.70
0.55
60.
700.
557
0.84
0.60
70.
840.
667
0.84
0.66
Stan
dard
norm
alva
riat
e
40.
590.
414
0.58
0.44
40.
580.
443
0.52
0.38
30.
510.
343
0.51
0.34
Not
es:B
lank
cells
indi
cate
the
failu
reof
the
PL
SRm
odel
for
pred
icti
ngth
ene
wda
tase
t;th
enu
mbe
rof
LVs
give
sth
enu
mbe
rof
opti
mum
late
ntva
riab
les
acco
rdin
gto
leav
e-on
e-ou
tcr
oss-
valid
atio
n.A
bsF
D,fi
rst
deri
vati
veof
abso
rban
ce;A
bsSD
,sec
ond
deri
vati
veof
abso
rban
ce.
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5968 Y. Liu and Y. Chen
were selected as final models. Both natural logarithmic transformation and Box–Coxtransformation improved the model performance. Continuum removal-based modelsoutperformed others. The models that utilized first- and second-derivative spectra aspredictors mostly failed in predicting a new data set. The first and second deriva-tives over absorbance spectra also performed poorly. The poor performance mightbe attributed to the magnification of noise by derivative processing not only at bothends of the spectra but also in the wavelengths that could be important for totalFe content estimation. It was noticed that some of the models had high R2
c (coeffi-cient of determination for calibration) but low R2
p, e.g. first-derivative-based models.The highest R2
c reached was 0.85 while the R2p was only 0.07, suggesting poor predic-
tion ability for a new data set. Therefore, the calibrated model was doubted and notadopted.
The predicted versus observed plots and the regression coefficients of the final mod-els are shown in figures 6 and 7. Regression coefficients summarize the relationshipbetween all spectral wavelengths and Fe content. Spectral wavelengths with a largeregression coefficient usually play an important role in the regression model. Thep-values for regression coefficients are calculated to test the null hypothesis (H0);H0 = the coefficient is equal to 0. Wavelengths that have corresponding p-values(p < 0.01) are selected as important wavelengths. All the important wavelengths weremarked with crosses. Figure 6 shows that wavelengths around 600 and 650 nm wereimportant wavelengths. Figure 7 shows that wavelengths around 500 and 600 nm wereimportant wavelengths. No wavelengths from the near-infrared region were found tobe important.
Fe oxides in the soil might make the wavelength around 650 and 500 nm impor-tant in the final models. The wavelength at 649 nm is known as the absorption bandsof Goethite, Lepidocrocite and Hematite from 6A1→ 4T1. Wavelengths at around
12
CalibrationCross-validationPredicted = observed
10
8
Pre
dic
ted
Fe
(%
)
6
4
2
00 2 4 6 8 10 12
Observed Fe (%)
0.2
0.15
0.1
0.05
Regression coefficient curve
Important wavelength (p < 0.01)
0
–0.05
Re
gre
ssio
n c
oe
ffic
ien
t
–0.1
–0.15
–0.2400 600 800 1000 1200 1400
Wavelength (nm)
1600 1800 2000 2200 2400
R2 = 0.55p
(a) (b)
Figure 6. Plot of calibration and validation results and important wavelengths from the finalmodel of soil samples without pretreatment. (a) Scatter plots of predicted versus measured totalFe content (natural-logarithmic-transformed) based on calibration set (cross) and validation set(circle); (b) important wavelengths.
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VNIR spectroscopy and total iron content in a mining area 5969
12
10
8
Pre
dic
ted
Fe
(%
)
6
4
2
00 2 4 6 8 10 12
Observed Fe (%)
Calibration
Cross-validation
Predicted = observed
R2 = 0.66p
(a)
Regression coefficient curve
Important wavelength (p < 0.01)
0.2
0.4
0.6
0
Re
gre
ssio
n c
oe
ffic
ien
t
–0.4
–0.2
–0.6
400 600 800 1000 1200 1400
Wavelength (nm)
1600 1800 2000 2200 2400
(b)
Figure 7. Plots of calibration and validation results and important wavelengths from the finalmodel of soil samples with pretreatment. (a) Scatter plots of predicted versus measured total Fecontent (natural-logarithmic-transformed) based on calibration set (cross) and validation set(circle); (b) important wavelengths.
500 nm may be related to a variety of transitions from different Fe oxides (Cornelland Schwertmann 2003). The importance of the wavelength at around 600 nm is lessclear.
4. Conclusions
In this study, we explored the feasibility of estimating total Fe concentrations in soilsusing laboratory-based VNIR data at a regional scale, with the Le’an River floodplain,China, as a case study. The principal result obtained is that estimation of total Fecontents from the samples collected in the floodplain soil with different land use andland cover is possible with VNIR spectroscopy (350–2500 nm).
Logarithmic and Box–Cox transformations have proved to be useful to transformthe non-normal data of total Fe content into normal. These transformations also‘eliminate’ four outliers, making the PLSR with leave-one-out cross-validation reliablein this case.
Spearman correlation analyses between the total Fe content and spectra with dif-ferent transformations reveal that the significant wavelength differs significantly inregions. For the individual spectral transformation, spectra from pretreated soil sam-ples generally have more significant wavelengths and higher absolute values of theSpearman correlation coefficients. Therefore, it could be concluded that the air-drying,grinding and 0.2-mm sieving processes are helpful to highlight the spectral feature oftotal Fe content in the VNIR region.
The final models from soil samples after air-drying, grinding and 0.2 mm sievingperform better than that from samples without pretreatment. Models from total Fecontent after natural logarithmic transformation and Box–Cox transformation per-form similarly but slightly better than the untransformed models. Wavelengths around500, 600 and 650 nm were considered to be particularly useful for total Fe estimationin this case.
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5970 Y. Liu and Y. Chen
We conclude that VNIR spectroscopy coupled with PLSR and data transforma-tions can be a potential technique for estimating total Fe content in the soil at aregional scale. Research that follows should focus on improvement of the modelaccuracy.
AcknowledgementThis research was supported by the Fundamental Research Funds for the CentralUniversities (No. 121010), the National High Technology Research and DevelopmentProgramme of China (2009AA122001) and the National Key Technology R&DProgramme of China (Grant No. 2011BAB01B06). The authors thank Dr Zhuo Luoand Dr Wu Jian for their participation in the fieldwork, and Xiao Mei and Zhang Linfor their guidance and assistance in chemical analyses.
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