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Prediction of total nitrogen in cropland soil atdifferent levels of soil moisture with Vis/NIRspectroscopyYaolin Liua, Qinghu Jianga, Tiezhu Shia, Teng Feia, Junjie Wanga, Guilin Liub & YiyunChena

a School of Resource and Environmental Science, Wuhan University, Wuhan, Chinab Department of Environmental Remote Sensing and Geoinformatics, University of Trier,Trier, GermanyPublished online: 16 Apr 2014.

To cite this article: Yaolin Liu, Qinghu Jiang, Tiezhu Shi, Teng Fei, Junjie Wang, Guilin Liu & Yiyun Chen (2014) Predictionof total nitrogen in cropland soil at different levels of soil moisture with Vis/NIR spectroscopy, Acta AgriculturaeScandinavica, Section B — Soil & Plant Science, 64:3, 267-281, DOI: 10.1080/09064710.2014.906644

To link to this article: http://dx.doi.org/10.1080/09064710.2014.906644

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ORIGINAL ARTICLE

Prediction of total nitrogen in cropland soil at different levels of soilmoisture with Vis/NIR spectroscopy

Yaolin Liua, Qinghu Jianga, Tiezhu Shia, Teng Feia, Junjie Wanga, Guilin Liub and Yiyun Chena*

aSchool of Resource and Environmental Science, Wuhan University, Wuhan, China; bDepartment of EnvironmentalRemote Sensing and Geoinformatics, University of Trier, Trier, Germany

(Received 6 January 2014; accepted 17 March 2014)

Visible/near-infrared (Vis/NIR) spectroscopy has been proven to be an effective technique for soil total nitrogen(TN) content estimation in the laboratory conditions. However, the transferability of this technique fromlaboratory study to field application is complicated by soil moisture effects. This study aims to compare theperformance of four spectral transformation strategies, namely, Savitzky–Golay (SG) smoothing, SG smoothingfollowed by first derivative (FD), orthogonal signal correction (OSC), and generalized least squares weighting(GLSW), in the removal of soil moisture effects on TN estimation. The spectral transformations were appliedon 8 sets of spectral reflectance measured from 62 soil samples at 8 moisture levels. The air-dried set was usedfor partial least squares regression (PLSR) calibration, whereas the other seven sets with moisture gradientswere used for external validations. Results show that the SG-PLSR model cannot be transferred from the air-dried samples to the samples with moisture gradients. The FD-PLSR model showed fair TN predictionperformance, with five out of seven residual prediction deviations (RPD) that are greater than 1.4. Both OSC-PLSR and GLSW-PLSR had good transferability to the moist samples. More specifically, the GLSW-PLSRmodel (mean of R2

pre¼ 0:718, root mean square error for prediction [RMSEP] = 0.262, and RPD = 1.885)outperformed the OSC-PLSR model (mean of R2

pre¼ 0:695, RMSEP = 0.277, and RPD = 1.780). The resultsdemonstrate the value of OSC and GLSW in eliminating the effects of moisture on TN estimation, and theGLSW-PLSR is recommended for a better Vis/NIR estimation of TN content under different soil moistureconditions.

Keywords: cropland soil; total nitrogen; visible/near-infrared spectroscopy; orthogonal signal correction;generalized least squares weighting; soil moisture effects

Introduction

Nitrogen, which is one of the primary nutrientscritical for the survival of all living organisms, andits cycle is significantly affected by human activities(e.g., agricultural practice) in the local and globalecosystems (Bernhard 2010). The rational use ofnitrogenous fertilizers could not only improve soilfertility and promote the sustainable development ofagriculture but also ensure food and nutrition safety(Yakovchenko et al. 1996; Olfs et al. 2005). Con-versely, unreasonable management of nitrogenousfertilizers may lead to the decline of soil fertility,resulting in the decrease of crop yield and proteincontent. Moreover, this situation will cause a seriesof environmental problems, such as soil erosion and

water eutrophication (Elmi et al. 2003; Erhart et al.2007), which will threaten human health and sur-vival. Soil total nitrogen (TN) is an important part ofsoil, and the level of its content is one of theimportant indicators for soil quality evaluation (Shiet al. 2013). The importance of nitrogen in terrestrialecosystems underlies the significance of enhancingthe efficiency of obtaining soil TN content to guidethe precise fertilizer and the assessment of soilquality.In recent years, visible/near-infrared (Vis/NIR)

spectroscopy has been increasingly used to measurevarious soil properties, including the TN content(Chang & Laird 2002; Viscarra Rossel et al. 2006;Michel et al. 2009; Soriano-Disla et al. 2013).

*Corresponding author. Email: kellypcyy@126.com

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Compared with traditional biological and chemicalmethods for determining TN content (Keeney 1982;Felipó & Garau 1987), Vis/NIR spectroscopy has theadvantages of fast, efficient, and nondestructivemeasurements (Demattê et al. 2004; Terhoeven‐Urselmans et al. 2006). In the laboratory, soilreflectance can be acquired under controlled condi-tions. However, in the field, spatial variation of“external parameters,” such as moisture and particlesize, may influence soil reflectance and furtherdegrade its ability for TN estimation. The effects ofsoil moisture on the Vis/NIR reflectance spectra havebeen extensively studied (Galvão & Vitorello 1998;Reeves III 2010). The relationship between thenormalized soil reflectance and moisture is nonlinear(Idso et al. 1975). Such nonlinear relationship couldexplain why homogeneous moisture in soils is pre-ferable for the Vis/NIR estimation of soil properties.The influence of soil moisture on the Vis/NIRestimation of soil properties, such as TN, has beenexplored in various studies. For example, Chang andLaird (2002) minimized the influence of moisturecontent on the spectra by using dried samples.Nevertheless, one of the most challenging issues ishow to improve the transferability of Vis/NIR modelamong different moisture conditions. Moreover,with the development of a global reflectance spectrallibrary from legacy soil samples (Viscarra Rosselet al. 2008), which are usually air-dried, transferringthe Vis/NIR model calibrated from dry samples tothe samples with moisture gradients would bedesirable.Chemo-metric strategies were used in line mon-

itoring of petrochemical, food, and pharmaceuticalfield for preventing influence factors, which canoccur in industrial conditions (Preys et al. 2008),such as multiplicative scatter correction (Isaksson &Næs 1988), standard normal variate transformationsand de-trending methods (Barnes et al. 1989),smoothing (Savitzky & Golay 1964), orthogonalsignal correction (OSC; Wold et al. 1998), externalparameter orthogonalization (EPO; Roger et al.2003), and generalized least squares weighting(GLSW, Zorzetti et al. 2011). In soil science,current efforts have been devoted to the removal ofthe influence of moisture on the Vis/NIR estimationof soil organic carbon (SOC) in view of somechemo-metric strategies. For example, Wu et al.(2009) noted that the first derivative (FD) of specificbands (e.g., 800–1400, 1600–1700, and 2100–2200nm) is moisture-independent and proposed to usethese spectra to estimate SOC content under fieldcondition. Marco et al. (2013) classified the spectraaccording to the normalized soil moisture indexproposed by Haubrock et al. (2008) and establisheda series of models to predict SOC content at

different moisture levels. Minasny et al. (2011)used EPO to remove the effects of soil moisturefrom Vis/NIR spectra in the prediction of SOCcontent. Despite these efforts, further examinationshould be conducted on chemo-metric strategiesthat hold the potential to remove moisture effectson the Vis/NIR estimation of TN.We investigated the influence of soil moisture on

the Vis/NIR spectra and the prediction of TN. Thisstudy mainly aims to remove the soil moisture effectson the Vis/NIR estimation of soil TN. More specif-ically, we aim to (1) explore the transferability ofpartial least squares regression (PLSR) model cali-brated among eight moisture gradients and to (2)compare the performance of four spectral trans-formation strategies, namely, Savitzky–Golay (SG)smoothing, SG smoothing followed by FD, OSC,and GLSW, in the removal of soil moisture effectson the Vis/NIR estimation of TN content.

Materials and methods

Study area

The study area is located in Honghu City (HubeiProvince), Jianghan Plain, which is known for itsriparian landscape as well as its dramatic land useand land cover changes since the 1950s (Fang et al.2005). The landform of the study area is flat with anelevation not exceeding 50 m above mean sea level.Inceptisols dominate the study area according to theUS Department of Agriculture Soil Taxonomy.Water body, lacustrine vegetation, floodplain, crop-land, and open land are the major land cover types(Zhao et al. 2003). The field survey in December2011 reported that artificial forest and meadow alongthe dams are now essential parts of land cover typesin this region. Paddy field and irrigated cropland arethe major land-use types comprising the cropland.The humid climate and diverse land-use types resultin the spatial–temporal dynamics of soil moisture.Therefore, the Honghu region was selected asstudy area.In December 2011, a total of 62 topsoil samples

were collected in study area. According to the USDepartment of Agriculture classification system, soiltexture of all samples was classified into fourclassifications. Descriptive statistics showing thetexture, TN, and SOC content of the samples havebeen provided in Table 1. We can see that with theorder of clay, loam, and sandy, the SOC and TNcontent gradually reduced. The geographical coor-dinates of all sampling sites were recorded using ahandheld global positioning system with a positionalerror of less than 5 m. Figure 1 shows the spatialdistribution of the sampling sites, with an Enhanced

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Thematic Mapper Plus (ETM+) image (panchro-matic band) showing the landscape of the study area.Spatial resolution of the panchromatic band is 15 mand the spectral range is 0.52–0.90 µm. Thus, thecropland shows bright while the water body (e.g.,breeding ponds) appears to be dark. It is noted thatthe total set of soil samples were from cropland andsome were much closer to the breeding pond area.At each sampling site, about 1.0–1.5 kg of surfacesoil from a depth of 0–10 cm was collected in fivesampling campaigns after wiping off plant material,plant residues, roots, and stones (Shi et al. 2013).That is to say, five samples were collected on the

center position and four corners of a 5 × 5 m grid foreach sampling site, and composed them as one soilsample. This method can reduce random error andensure the representativeness of the sample. Thecollected soil samples were then kept in a sealedpackage for chemical analyses and spectral measure-ment in the laboratory.

Laboratory analyses and measurements

In the laboratory, the soil samples were air-dried,ground, and sieved (less than 2 mm). Each samplewas thereafter placed on a petri dish with diameter

Figure 1. Maps showing the geographical location of the study area, distribution of sampling sites, and landscape of thestudy area, indicated by a LANDSAT-7 ETM+ image with band 8 (panchromatic band).Note: DN refers to digital number.

Table 1. Basic properties and statistical descriptions of the soil samples on Honghu region, China.

SOC (g kg−1) TN (g kg−1)

Texture Sample number Crop Min Max Mean Min Max Mean

Clay 36 Winter rape, grass, idle field 15.09 26.23 20.59 1.23 2.18 1.69Clay loam 7 Wheat, winter rape, sesame 11.71 13.74 12.36 0.87 1.6 1.24Loam 14 Wheat, winter rape, sesame 7.45 10.25 8.60 0.61 1.03 0.78Sandy loam 5 Peanut, winter rape 2.35 6.12 4.35 0.43 0.64 0.55

Min, minimum; Max, maximum. SOC, soil organic carbon; TN, total nitrogen.

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and thickness of 12 and 1 cm, respectively. Allsamples were oven-dried at 105°C for six hoursand weighted on an analytical scale (with accuracy of0.01 g) to calculate the weight of oven-dried soilsamples. A small part of each sample was analyzedfor soil TN content by using the semimicro Kjeldahlmethod (Nelson & Sommers 1980). Subsequently,the oven-dried soil samples were rewetted to over-saturation, with the moisture content falling withinthe range of 350–400 g kg−1. For the rewettingprocess, predetermined volume of distilled water wasmeasured using a glass cylinder, and gently added tothe petri dish. Each of the petri dish was covered forabout two hours until all the samples were saturated.The samples were thereafter air-dried for seven daysat room temperature ranging from 18 to 22 °C(Ciarlo et al. 2007). The lid of the petri dish wasused to control soil evaporation so that the moistureof each sample could fall within the ranges of300–350 g kg−1, 250–300 g kg−1, 200–250 g kg−1,150–200 g kg−1, 100–150 g kg−1, 50–100 g kg−1, and0–50 g kg−1 (air-dried) for each of the seven days,respectively. The soil samples at these eight differentmoisture levels (including the oversaturated sam-ples) were weighted shortly before spectral measure-ment. Subtracting the weights of the oven-dried soilsamples facilitates the calculation of the moisture ofeach sample at each moisture level. With box plots,Figure 2 shows the statistical characteristics of themoisture content of the eight data-sets. It can beseen that for each of the seven moist data-sets, the

moisture content of the 62 samples takes an approxi-mately normal distribution. We assumed that suchstatistical distribution of moisture content could be agood representative of operational situation.An ASD FieldSpec3 portable spectral radiometer

with wavelength of 350–2500 nm was used tomeasure the soil spectra for each set. The samplinginterval and spectral resolution were 1.4 nm and3 nm for the 350–1000 nm range, respectively, and2 nm and 10 nm for the 1000–2500 nm range,respectively (http://www.asdi.com). A standardizedwhite Spectralon® panel was used for reflectancecalibration. A white light source matched with thespectroradiometer was used with a 45° incidentangle. The spectra of the soil samples were measuredby the spectroradiometer with a distance of 15 cmfrom the probe to the sample surface and a zenithangle of 90°. The entire scanning procedure wasperformed in a dark room at night to minimize theinfluence of external light (Choe et al. 2008).

Spectral preprocessing

Only the reflectance of the air-dried samples (0–50g kg−1) in the region of 400–2350 nm was used formodel calibration because of the presence of noiseat both edges. Four spectral preprocessing strat-egies were implemented and compared in terms oftheir performance in removing soil moisture effectson TN prediction. The reflectance was smoothedby using the SG algorithm with a window size of 15

Figure 2. Box plots of soil moisture content (SMC) which are divided into eight levels: 0–50 g kg−1 (air-dried), 50–100 gkg−1, 100–150 g kg−1, 150–200 g kg−1, 200–250 g kg−1, 250–300 g kg−1, 300–350 g kg−1, and 350–400 g kg−1.

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and polynomial order of 2 (Savitzky & Golay1964). The smoothed reflectance was thereafterpreprocessed with FD (Rinnan et al. 2009), OSC,and GLSW. Thus, for each of the eight data-sets,four sets of spectra were involved: (1) the reflec-tance with SG smoothing, (2) the SG smoothedreflectance followed by FD, (3) the SG smoothedreflectance coupled with OSC, and (4) the SGsmoothed reflectance followed by GLSW.The OSC effectively removes all orthogonal varia-

tions in independent variables (X-block) that areunrelated or orthogonal to dependent variables(Y-block), thus simplifying the calibration model(Lin & Kuo 2013). The algorithm starts by identify-ing the first principal component of the X-block.Afterward, the loading is rotated to make the scoresorthogonal to the Y-block. Once the rotation iscomplete, a partial least squares (PLS) model isestimated, which can predict these orthogonal scoresfrom the X-block. The number of components (c) inthe PLS model is adjusted to achieve a specified levelof captured variance for the orthogonal scores.Finally, the weights, loadings, and predicted scoresare used to remove the specified orthogonal com-ponent. This entire process can be repeated on theX-block, from which the previously identified ortho-gonal component was removed, for any given num-ber of components. Each cycle results in additionalPLS weights and loadings being added to the totaland will be used when applied to new data (Wiseet al. 2007).The GLSW was used to down weigh the differ-

ences between replicate measurements (Zorzetti et al.2011). The underlying principle of GLSW is a filtercalculated from the differences (usually caused byexternal factors) between samples, which shouldotherwise be similar. These differences are consid-ered interferences, which the filter attempts to downweigh. In this study, GLSW was applied to correctsoil moisture effects on the Vis/NIR estimation ofTN. GLSW helps down weigh the differences andmake the spectra of the same sample at different soilmoisture gradients appear more similar. Ten sam-ples were selected from the air-dried set according tothe spectral Euclidean distance (DISTSLCT func-tion in PLS toolbox version 7.3.1, EigenvectorResearch, Manson, WA). Then, the same sampleswere selected from the other seven data-sets withdifferent moisture levels. Thereafter, a GLSW wasestimated by using the SG smoothed reflectance ofselected samples from each of the eight moisturelevels. Finally, the filtered air-dried set was used forthe PLSR calibration (Fu et al. 2012). The mostcommonly used algorithm for GLSW has a singleadjustable parameter, α, which defines how stronglythe GLSW down weight the interferences. Adjusting

α toward larger values (typically above 0.001)decreases the effect of the filter. Smaller α values(typically 0.001 and below) apply more filtering.

Building the data-sets

The reflectance spectra and the TN content of all 62soil samples were denoted as (Xi, Y), where Xidenotes the spectral matrix and Y represents thecontent of TN. The lower case character i representsthe soil moisture level, where i = 1 means soilmoisture content (SMC) = 0–50 g kg−1 (the air-dried data-set), i = 2 means SMC = 50–100 g kg−1,i = 3 means SMC = 100–150 g kg−1, i = 4 meansSMC = 150–200 g kg−1, i = 5 means SMC =200–250 g kg−1, i = 6 means SMC = 250–300 gkg−1, i = 7 means SMC = 300–350 g kg−1, and i = 8means SMC = 350–400 g kg−1. With the fourspectral transformations mentioned above, 32 data-

sets, namely, (X ðiÞSG, Y), (X ðiÞ

FG, Y), (X ðiÞOSC, Y), and

(X ðiÞGSLW, Y), i = 1, …, 8, were built to study the

influence of soil moisture on the prediction of TNcontent.

Model calibration and validation

PLSR modeling

PLSR coupled with leave-one-out cross validationare used for model calibration by relating thespectral data-sets Xi with TN content data-sets Y.This method was developed by Wold et al. (2001)and has become a routine modeling technique forthe quantitative analysis of Vis/NIR spectra (ViscarraRossel & Behrens 2010). The advantages of PLSRare that it handles multicollinearity. It is robust interms of data noise and missing values (ViscarraRossel et al. 2006). Particularly, it is useful whendealing with highly correlated predictor variables,whose number is much greater than that of samples(Wold et al. 2001; Viscarra Rossel et al. 2006).According to the four spectral pretreatment strat-egies, the SG-PLSR, FD-PLSR, OSC-PLSR, andGLSW-PLSR models were calibrated by using the(X ðiÞ

SG, Y), (XðiÞFG, Y), (X

ðiÞOSC, Y), and (X ðiÞ

GSLW, Y) air-dried data-sets, respectively. To find the better c andα for the OSC-PLSR and GLSW-PLSR models,respectively, different values of c (1, 2, 3, and 4) andα (0.01, 0.001, and 0.0001) were used in the modelcalibrations. Leave-one-out cross validation (Gomezet al. 2008) was used to determine the optimalnumber of factors in the PLSR models. PLS toolboxversion 7.3.1 (Eigenvector Technologies, Manson,Washington, USA) was applied for the model calib-ration and validation.

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Evaluation of the calibration models

The performances of the calibration models wereassessed by the determination coefficient for crossvalidation and validation (R2

cv and R2pre), the root

mean square error for prediction (RMSEP), and theresidual prediction deviation (RPD; Saeys et al.2005; Mouazen et al. 2010; Wang et al. 2014). R2

indicates the variance percentage in the Y variablethat can be captured by the X variables. The RPD,defined as the ratio of the standard deviation of the Yvariable in the validation set to the RMSEP, demon-strates the predictive performance of the calibrationmodels. We adopted the five level interpretations ofR2 given by Williams (2004) and the RPD men-tioned by Viscarra Rossel et al. (2006): excellentpredictions (R2 ≥ 0.90, RPD ≥ 2.5), good predic-tions (0.81 ≤ R2 < 0.90, 2.0 ≤ RPD < 2.5),approximate quantitative predictions (0.66 ≤ R2 <0.81, 1.8 ≤ RPD < 2.0), possibility to distinguishbetween high and low values (0.50 ≤ R2 < 0.66,1.4 ≤ RPD < 1.8), and unsuccessful predictions(R2 < 0.50, RPD < 1.40).

Transferability verification

The transferability verification is two-fold. First,eight PLSR models were calibrated by using one ofthe data-sets (X ðiÞ

SG, Y) with a specific i (i = 1, …, 8).The other seven data-sets were used for validations.Such processes were designed to examine whetherthe PLSR models calibrated from the soil samples ata specific moisture level could be successfullyapplied to estimate the TN content when themoisture of the samples varied. No other spectraltransformation strategies were used in this verifica-tion process. The second verification procedureinvolved all four spectral transformation strategiesfor the purpose of comparison. The (X ð1Þ

SG , Y), (Xð1ÞFG,

Y), (X ð1ÞOSC, Y), and (X ð1Þ

GSLW, Y) data-sets were usedfor model calibration. Thereafter, coupled with theother data-sets at different moisture gradients, fourcalibration models, namely, SG-PLSR, FD-PLSR,OSC-PLSR, and GLSW-PLSR, were used for TNprediction and transferability verification. In bothverification processes, the transferability of the cal-ibration models was assessed by using R2

pre and RPD.

Flowchart of the study

Figure 3 shows a flowchart that describes theprocesses of data collection and preparation, modelcalibration, validation, and evaluation. For the com-parisons of spectral transformations in the removal ofsoil moisture effects, only the air-dried data-setswere used in the model calibration, and the model

transferability was examined with the moisture data-sets as test sets. This measure is performed not onlybecause most previous studies have used the spectraof air-dried samples (e.g., Shi et al. 2013) but alsofor future use of soil spectral library in the estimationof soil properties in the field.

Results and analysis

Effect of moisture content on soil spectra

Figure 4(a) shows the SG smoothed spectra. Thedifferences in soil moisture result in the variations ofspectra in the entire spectral range (400–2350 nm).The reflectance decreased with increased soil mois-ture. The variation is more pronounced for longerwavelengths, particularly in the water absorptionbands (1450 and 1940 nm; Weidong et al. 2002).For low moisture levels, a small increase in soilmoisture induces a typical decrease in soil reflec-tance. However, in the cases of higher soil moisturelevels (i.e., SMC > 250 g kg−1), the reflectancedecreases slightly with increased moisture. Theseconclusions are consistent with those of Neema et al.(1987) and Zhu et al. (1984). Moreover, thewavelengths around 1450, 1850, and 2250 nmwere identified as the important wavelengths forPLSR modeling in TN content evaluation (Shi et al.2013). Thus, moisture may mask these features,resulting in poor Vis/NIR calibration for TN.Figure 4(b) shows the eight curves of the FD

spectra. The water peak at 1400 and 1900 nm isexaggerated. The eight spectral curves appear moresimilar to one another compared with those inFigure 4(a). In Figure 4(c), the OSC applied in thisstudy effectively reduced the influence of moistureand furthered the similarity of the spectral curvesfrom the eight moisture levels. This finding suggeststhat the OSC may be used to remove the soilmoisture effects on the Vis/NIR prediction of soilTN content. Figure 4(d) reveals that the eightspectral curves with GLSW transformation fluctuatesignificantly. However, similar trends of these curvescan still be observed in the region of 400–2350 nm.

Effect of moisture content on the transferabilityof SG-PLSR models

The transferability of the eight SG-PLSR modelswas assessed by using R2

pre and RPD (Figure 5).When R2

pre ≥ 0.5 and RPD ≥ 1.4, models could betransferred among different moisture levels, and thelarger, the better. Models calibrated from the moistsamples can be transferred to samples with moisturedifferences of approximately 50 g kg−1 (the adjacentmoisture gradient). However, these models failed to

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predict the TN content when the differences ofmoisture content were greater than 100 g kg−1.The model calibrated by using the air-dried samples(0–50 g kg−1) exhibited poor transferability inestimating TN with the moist samples. Thus,without additional spectral transformations, theSG-PLSR models need to be calibrated at each ofthe moisture gradients for accurate TN estimations.

Effect of spectral transformations on thecorrelations between soil spectra and TN

The Pearson correlations between differently pre-treated spectra and soil TN at all eight moisture

levels were explored. The correlation curves wereline-plotted in Figure 6. The spectral wavelengthswith significant correlation at the confidence intervalof 0.01 were highlighted with black cross symbols.The significant wavelength variables may vary withdifferent spectral pretreatments. No evident trendwas found for all eight correlation coefficient curvesfrom the spectra with SG smoothing [Figure 6(a)].This result indicates that the soil moisture signifi-cantly interfered with the correlation between reflec-tance and TN content. The FD-based correlationcurves fluctuated in the entire spectral region. Theshapes of the eight curves were similar in the visibleregion but slightly different in the NIR region of

Figure 3. Flowchart of the processes of data collection and preparation, model calibration, validation, and evaluation.Note: Xi (i = 1, …, 8) denotes preprocessed spectral data, i from 1 to 8 are for the soil at gravimetric moisturecontent of 0–50, 50–100, 100–150, 150–200, 200–250, 250–300, 300–350, and 350–400 g kg−1, respectively.

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1400–2350 nm [Figure 6(b)]. sure 6(c) shows thatthe shapes of the eight correlation curves for theOSC-pretreated spectra and TN content were rela-tively similar. The same phenomenon was found inthe GLSW-pretreated spectra [Figure 6(d)]. These

results suggest that the latter two spectral preproces-sing techniques could be useful in determining thecommon wavelengths, which are important for TNestimations at different moisture levels. Thus, themodel calibrated from these common wavelengths

Figure 4. The average of soil spectra from different data-sets with four spectral transformations: (a) SG smoothing, (b) SGsmoothing followed by FD, (c) SG smoothing followed by OSC, and (d) SG smoothing followed by GLSW.

Figure 5. Transferability of SG-PLSR models for total nitrogen (TN) estimation with soil samples of eight moisturegradients as assessed by (a) determination coefficient for validation (R2

pre) and (b) RPD. It should be noted that the diagonalsare (a) determination coefficient for calibration (R2

cal) and (b) standard deviation of prediction set divided by root meansquare error of calibration.

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could be transferred in the soil samples with differentmoisture contents.

Parameters optimization for the OSC-PLSRand GLSW-PLSR models

The parameters that need to be optimized for OSC-PLSR and GLSW-PLSR are c, the number of OSCfilter dimensions and α, the weights of GLSW filterintensity. Figure 7 shows the RMSE values of theTN prediction with different c and α. Results showthat the lowest RMSE values were obtained whenc = 3 (for the OSC-PLSR model) and α = 0.001 (forthe GLSW-PLSR model). Therefore, we chose c = 3and α = 0.001 for OSC and GLSW filters,respectively.

Performances of the four PLSR models

Table 2 presents the results of the four PLSR modelsfor estimating TN content. The optimal number oflatent variables (NLV) determined by leave-one-outcross validation for the SG-PLSR, FD-PLSR, OSC-PLSR, and GLSW-PLSR models were 4, 5, 4, and3, respectively. In terms of model calibration byusing the (X ð1Þ

SG , Y), (X ð1ÞFG, Y), (X ð1Þ

OSC, Y), and(X ð1Þ

GSLW, Y) data-sets, the determination coefficientfor calibration (R2

cal) values were between 0.686 and0.897, with the R2

cv between 0.619 and 0.699. The

results indicate successful model calibrations. Spe-cifically, the OSC-PLSR model performs better thanthe SG-PLSR and the FD-PLSR models do, but notas good as the GLSW-PLSR model in terms of NLV

and R2cv.

The four PLSR models were used to predict theTN content in soil samples with varying moisturecontents (data-sets (X ðiÞ

SG, Y), (X ðiÞFG, Y), (X ðiÞ

OSC, Y),and (X ðiÞ

GSLW, Y), i = 2, …, 8). Thus, for each of thePLSR models, seven validation sets corresponding tothe seven moisture gradients were employed. R2

pre,RMSEP, and RPD were calculated in each of thevalidation processes. Their mean and standard devi-ation are shown in Table 2. Figure 8 details theprediction performance of the four PLSR models foreach of the seven soil moisture levels.Poor predictions were obtained for the SG-PLSR

model when the model calibrated with the air-driedsamples was applied to predict the TN content ofmoist samples. The mean values of R2

pre, RMSEP,and RPD were 0.468, 0.357, and 1.382, respectively.The R2

pre (0.331–0.589), RMSEP (0.315–0.402),and RPD (1.226–1.563) for the seven validationsets varied dramatically, indicating unstable predic-tion ability. For the FD-PLSR model, the predic-tions were less affected by SMC. The mean values ofR2pre increased to 0.557 and the mean values of RPD

increased to 1.493, whereas those of the RMSEPreduced to 0.330, which were better compared with

Figure 6. Pearson’s correlation coefficient between total nitrogen (TN) content and differently pretreated spectra: (a)spectra with Savitzky–Golay (SG) smoothing, (b) spectra with SG smoothing and FD, (c) spectra with SG smoothing andOSC, and (d) spectra with SG smoothing and GLSW.

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the predictions of the SG-PLSR model. However,two out of the seven values of R2

pre and RPD werebelow 0.5 and 1.4, respectively. This finding denotesthe limitations of this model. Besides, the moistureeffects on the TN estimation remained because R2

prewas considerably less than R2

cv.Therefore, the OSC-PLSR and GLSW-PLSR

models were calibrated to remove the soil moistureeffects on TN predictions. The mean values of R2

prefor these two models significantly increased andapproached their R2

cv. All of the seven RPDs aregreater than 1.4, and the RMSEP decreased fromabove to below 0.29. These results show that theprediction accuracy of the soil with moisture gradi-ents is comparable to the prediction of air-driedsoils. Moreover, less fluctuation was observed inR2pre, RMSEP, and RPD curves for the OSC-PLSR

and GLSW-PLSR models. This finding indicatesthat the predictions for all seven sets of moist

samples were similar in spite of variations in mois-ture contents. These results proved the efficiency ofthe OSC-PLSR and GLSW-PLSR in the removal ofsoil moisture effects on the Vis/NIR estimation ofTN. The GLSW-PLSR model (mean of R2

pre =0.718, mean of RMSEP = 0.262, and mean ofRPD = 1.885) performed better than the OSC-PLSR model did (mean of R2

pre = 0.695, mean ofRMSEP = 0.277, and mean of RPD = 1.780). Thisfinding confirms that the quantitative prediction ofsoil TN from the soil samples with varied SMCs ispossible.

Discussion

Diffuse reflectance spectroscopy has been reported tohave potential as a rapid screening method to charac-terize soil chemical properties (Udelhoven et al.2003). Unfortunately, field spectroscopy performed

Table 2. Prediction of soil total nitrogen (TN) content using Vis/NIR spectroscopy with different preprocessing methods.

Mean Standard deviation

Model NLV R2cal R2

cv R2pre RMSEP RPD R2

pre RMSEP RPD

SG-PLSR 4 0.686 0.619 0.468 0.357 1.382 0.091 0.031 0.122FD-PLSR 5 0.897 0.684 0.557 0.33 1.493 0.067 0.028 0.131OSC-PLSR 4 0.75 0.696 0.695 0.277 1.78 0.033 0.017 0.119GLSW-PLSR 3 0.842 0.699 0.718 0.262 1.885 0.024 0.012 0.086

Figure 7. RMSEP of total nitrogen (TN) prediction models with the seven moist data-sets as validation sets: (a) OSC-PLSR models with different c values (the number of OSC filter dimensions); (b) GLSW-PLSR models with different αvalues (the weights of GLSW filter intensity).

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Figure 8. Performances of the four PLSR models: SG-PLSR, FD-PLSR, OSC-PLSR, and GLSW-PLSR. The air-dried sets were used for model calibration, whereas theother seven sets with moisture gradients were used for validations.

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not as well as laboratory spectroscopy probably due tostrong interferences of environmental factors, such assoil moisture variations. Prediction accuracy dependsupon sample preparation and spectral pretreatmentselected before estimating a statistical model (Chang& Laird 2002). In this study, four modeling strategieswere used to examine their ability of removing the soilmoisture effects on the Vis/NIR estimation ofsoil TN.Considering all the indicators used to assess the

quality of model predictions, the performance of thefour models can be ranked as GLSW-PLSR > OSC-PLSR > FD-PLSR > SG-PLSR. Such a result maybe explained by the fact that the SG- or FD-PLSRmodel is just to deal with spectral data itself, anddoes not consider the spectral differences betweenthe air-dried and the moist soil sample sets. Suchprocess may miss some useful information andcannot eliminate the soil moisture effect entirely,which will further affect the quality of modeling. Inaddition, the FD algorithm can remove the baselinedrift and background interferences (Rinnan et al.2009), which were partially caused by soil moisture.The interferences caused by soil moisture are non-linear. Thus, the FD cannot eliminate the influenceof moisture completely. By contrast, the OSC- andGLSW-based models introduced in this studyshowed promising results and could effectively elim-inate the effects of soil moisture on the Vis/NIRpredictions of TN. However, it should be pointedout that, through too few OSC components, theOSC can only remove parts of the signals that areunrelated (orthogonal) to Y. In contrast, removingtoo many OSC components may result in erosion ofthe useful signal (Boulet et al. 2007). These defectsdecrease the prediction accuracy of OSC-PLSRmodels. Thus, appropriate number of OSC compo-nents is important for OSC-PLSR in effectivelyeliminating the effects of soil moisture on the Vis/NIR prediction of TN. GLSW uses the eigenvectorsand eigenvalues of a covariance matrix to downweight signal identified as being from known inter-ferences or differences between samples whichshould otherwise be the same (Zorzetti et al. 2011).Results indicated that GLSW-PLSR model obtaineda better performance than the other models.In addition, we compared the important wave-

lengths for estimating the soil TN between themodels calibrated using the air-dried and moist soilsample sets (with SMC = 150–200 g kg−1 as anexample). Thereafter, from the perspective ofimportant wavelengths, we further evaluated thepotentials of the four spectral pretreatments inremoving the interferences of soil moisture. For aspecific spectral pretreatment, if the important wave-lengths of the air-dried and the moist samples-based

models are similar in their distribution, it mayindicate that this pretreatment functions effectivelyin removing the soil moisture effect on the TNestimation. The variable importance in the projec-tion (VIP) was used to identify the importantwavelengths used in the PLSR calibrations (Chongand Jun 2005). If the VIP value for a specificwavelength is greater than 1, this spectral wavelengthis then considered to be important (Gomez et al.2008). The VIPs for predicting soil TN from thefour preprocessed reflectance spectra are presentedin Figure 9. We can see that the VIP scores of fourPLSR models now display a marked difference.According to the order of Figure 9(a)–(d), with theincreasing number of important wavelengths, thedistribution of the bands is becoming more andmore wide. This suggests that the prediction abilitiesof models are gradually improved. From the per-spective of moisture content, the similarity of VIPcurves of four models are improving step by step.The similar patterns suggest that both data-setsestimate very similar regression models to predictsoil TN, which were less affected by water content.This further proved the performance of the fourmodels illustrated as GLSW-PLSR > OSC-PLSR >FD-PLSR > SG-PLSR.Despite these differences, we found some com-

mon important bands of the four models. Accordingto the VIP scores, the wavelengths near 1410, 1850,2210, and 2350 nm were identified as the importantwavelengths for TN estimation. Approximate wave-length regions around 1446, 1826, 2176, and 2366nm were also identified by Morra et al. (1991).Similarly, the wavelengths 1450, 1850, 2250, and2330 nm were reported by Shi et al. (2013) for TNcontent estimation. Specially, in our study, we foundthat the TN content showed high correlations withthe wavelengths in the visible region. This could beexplained by previous studies which reported visiblewavelengths 600–700 nm (Vinogradov 1981); and564.4 and 623.6 nm (Krishnan et al. 1980) wereimportant for organic matter prediction. Because ofthe inherent link between total carbon and TN(Fystro 2002), soil TN content might be indirectlyrelated to these wavelengths through soil total car-bon (Shi et al. 2013).Soil moisture is one of the major factors affecting

soil reflectance in the Vis/NIR region. Someresearchers (e.g., Hummel et al. 2001) estimatedsoil chemical properties by developing a calibrationdata-set with samples covering a wide range ofmoisture contents. While our study demonstratedthe applications of OSC and GLSW in the removalof soil moisture effects on the Vis/NIR prediction ofTN content in soil. For operational application inthe field situation, the moisture of soil samples may

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vary spatially. In order to calibrate a TN estimationmodel which is less influenced by or free of soilmoisture effect, the GLSW and OSC may be appliedto the Vis/NIR spectra of samples with a wide rangeof moisture contents. Thereafter, the PLSRapproach can be used for model calibrations. Itshould be noted that the spatial variations of otherexternal parameters besides the moisture mightweaken the predictive ability of the TN estimationmodel. Further investigations are needed to deter-mine whether the effects of other factors, such as soilparticle size, can be removed by algorithms, such asOSC and GLSW. In our future studies, the modelscalibrated in our study need to be validated in otherstudy areas, and the formulation of “moisture-free”or “other-external-factors-free” model for the Vis/NIR estimation of soil chemical properties is encour-aged to transfer this technique from laboratory studyto field application. Besides, airborne and satellitehyperspectral images are useful data sources inmapping TN contents over large areas (Shi et al.2013). Several studies have showed that the outdoorand airborne/satellite imaging spectroscopy havepotentials for the estimation of soil properties(Stevens et al. 2010; Viscarra Rossel & Behrens2010). This opens further perspectives to airbornehyperspectral sensors, such as the HyMap andAVIRIS sensors, which characterize soil reflectancein the Vis/NIR spectral region (400–2500 nm) with a

relatively high spectral and spatial resolution(Udelhoven et al. 2003). Therefore, the proposedstrategies for removing moisture effects on the Vis/NIR estimation of TN based on outdoor andairborne/satellite data should be explored to provideaccurate information for precision agriculture.

AcknowledgmentsThe authors are thankful to Prof. Yanfang Liu for hersupport in the laboratory analyses and Prof. Guofeng Wufor his valuable comments. We would also like to thank MrKai Guo, Mr Pujiang Huang, and Ms JingQian for theirhelpful advices on the methodology.

FundingThis work is supported by the National 12th Five-YearsKey Technology R&D Program of China [grant number2011BAB01B06].

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