Geoderma 161 (2011) 202–211

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Comparison of soil reflectance spectra and calibration models obtained usingmultiple spectrometers

Yufeng Ge a,1, Cristine L.S. Morgan b,⁎, Sabine Grunwald c,2, David J. Brown d,3, Deoyani V. Sarkhot e

a Biological and Agricultural Engineering Department, MS 2117, Texas A&M University, College Station, TX 77843-2117, USAb Soil and Crop Sciences Department, Texas A&M University, 370 Olsen Boulevard, College Station, TX 77843-2474, USAc Soil and Water Science Department, University of Florida Gainesville, 2169 McCarty Hall, Gainesville, FL 32611-0290, USAd Department of Crop and Soil Sciences, Washington State University, 249 Johnson Hall, Pullman, WA 99164-6420, USAe School of Natural Sciences, University of California, Merced, P.O. Box 2039, Merced, CA 95344, USA

Abbreviations: VNIR-DRS, visible and near infrared diOC, organic soil carbon; QU, Quemado; TX data set,inorganic soil carbon; TC, total soil carbon; RMSEcv, croserror; RPD, RMSE divided by the standard deviation of⁎ Corresponding author. Soil and Crop Sciences Depa

College Station, TX 77843-2474, USA. Tel.: +1 979 845E-mail addresses: yg36@tamu.edu (Y. Ge), cmorgan

sabgru@ufl.edu (S. Grunwald), david_brown@wsu.edu (dsarkhot@gmail.com (D.V. Sarkhot).

1 Tel.: +1 979 845 2440.2 Tel.: +1 352 392 1951x204.3 Tel.: +1 509 335 1859; fax: +1 509 335 8674.

0016-7061/$ – see front matter © 2010 Elsevier B.V. Adoi:10.1016/j.geoderma.2010.12.020

a b s t r a c t

a r t i c l e i n f o

Article history:Received 24 June 2010Received in revised form 8 December 2010Accepted 26 December 2010Available online 5 February 2011

Keywords:Visible-near infrared diffuse reflectancespectroscopyCalibration transferSoil VNIRSoilSpectral libraries

In the literature of visible and near infrared diffuse reflectance spectroscopy (VNIR-DRS) for soil characterization,the effects of instruments and scanning environments on reflectance spectra and calibration models have notbeenwell documented. Tofill this knowledge gap, the goal of this study is to compare soil reflectance spectra andcalibrationmodels obtainedwith different spectrometers operated under different lab environments. Two sets ofsoil samples were used in this study. The first set (containing 180 samples collected from Quemado, Texas) wasscanned by three spectrometers and no effort was provided to control the scanning protocol. The second set(containing 264 samples from Central Texas) was scanned by two spectrometers, and efforts were providedintentionally to control the scanning protocol. Partial least squares regressionwas applied to develop calibrationmodels for soil organic carbon (OC) content using the first derivative spectra. Three calibration transfer methods(namely, slope and bias correction, direct standardization, and piecewise direct standardization) were used totransfer calibration models from one instrument to another. In the experiment where no scanning control wasprovided, significant differences were seen in mean soil spectra by different spectrometers. But cross-validationindicated that all three models can predict OC accurately. However, the OC models are quite dissimilar to eachother in terms of the regression coefficients at eachwavelength, and their application to the spectrameasured byother instruments generally yielded poor results. All three calibration transfer methods provided a satisfactoryapplication of the OC model calibrated on the primary instrument to secondary instruments. In the experimentwhere some controls were provided, mean soil spectra by different spectrometers matched each other well. Thetwo OC models were quite similar, and model application to the spectra measured by the other instrumentyielded satisfactory predictions. When scanning control was provided; however, model transfer methodsimproved the calibrationmodel onlymarginally. All results indicate that VNIR-DRS calibrationmodels are highlyinstrument/scanning environment dependent, and their extent of applicability could be highly limited. Provisionof controls over the scanning protocol has the potential to remove a great deal of spectral variations that arerelated to extraneous effects due to multiple instruments/scanning environment. The results of this study haveimportant implications on the future use of VNIR-DRS as a routine method for soil characterization, such ascomparisons among VNIR prediction models derived from different soil labs and a global soil spectral library,where multiple instruments have to be involved.

ffuse reflectance spectroscopy;CT, Central Texas dataset; IC,s-validated root mean squaredmeasurements.rtment, 370 Olsen Boulevard,3603; fax: +1 979 845 0456.@ag.tamu.edu (C.L.S. Morgan),D.J. Brown),

ll rights reserved.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Visible and near infrared diffuse reflectance spectroscopy (VNIR-DRS) has been successfully applied to characterize many soilproperties including texture, macro- and micro-nutrient levels, cationexchange capacity, organic and inorganic carbon, pH, and claymineralogy (Stoner and Baumgardner, 1981; Dalal and Henry, 1986;Sudduth and Hummel, 1991; Ben-Dor and Banin, 1995; Chang et al.,2001; Sørensen and Dalsgaard, 2005; Brown et al., 2006; Ge andThomasson, 2006; Vasques et al., 2008; Vasques et al., 2009).Compared to traditional laboratory analytical procedures, VNIR-DRSis fast, cost-effective, non-destructive, requires minimal sample

203Y. Ge et al. / Geoderma 161 (2011) 202–211

preparation and no hazardous chemical reagents, and most attrac-tively, several soil properties can be derived from a single scan (Brownet al., 2006; Viscarra Rossel et al., 2006). Early studies of VNIR-DRS forsoil analysis were primarily research oriented, and now there is agrowing interest to use this technique routinely to expand spatialresolution and extent of known soil properties for mapping efforts.For instance, several researchers (Daniel et al., 2003; Waiser et al.,2007; Morgan et al., 2009) investigated its potential for in situcharacterization where soils were scanned field moist and intact.Prototype sensors based on VNIR-DRS have also been developed andcan be attached to mobile machinery (e.g., tractors and probe trucks)for real time soil characterization (Sudduth and Hummel, 1993;Hummel et al., 2001; Adamchuk et al., 2004; Ben-Dor et al., 2008;Christy, 2008; Bricklemyer and Brown, 2010). More recently, researchefforts are under way to develop large soil spectral libraries (Brownet al., 2005; Brown, 2007; Viscarra Rossel et al., 2008; Global Soil Map,2009) that would support various research fields including proximalsoil sensing, digital soil mapping, land resource management, soil andwater quality modeling, and precision agriculture.

At the heart of VNIR-DRS is the calibration model that relatesreflectance spectra (or their derivations) to soil properties measured bythe reference methods. In VNIR-DRS applications other than soil (suchas in chemical, pharmaceutical, and food industries), it is well knownthat many factors will have profound effects on measured reflectancespectra and subsequently the calibration models (Bouveresse andCampbell, 2001; Fearn, 2001; Feudale et al., 2002; van den Berg andRinnan, 2008). These factors include inherent characteristics ofdetectors and circuitry employed by different instruments, scanningenvironment (particularly ambient temperature and humidity), radia-tion source–sample–detector geometry (or light path), sample prepa-ration (for soil, 2-mm grinding vs. pulverization), packing density,length and bending of fiber optics (cause loss in transmission ofradiation energy), equipment aging and repair, routine scanningprotocols by different labs, and operators’ scanning habits. In thispaper, we use the term instruments/scanning conditions to refer tothese factors collectively.

Multivariate methods such as direct standardization, piecewisedirect standardization, orthogonal signal correction, reverse stan-dardization, piecewise reverse standardization, and slope and biascorrection have been put forward to address model transferability inVNIR spectroscopy (Bouveresse and Campbell, 2001; Chu and Yuan,2001; Fearn, 2001; Greensill et al., 2001; Feudale et al., 2002). Spectralpreprocessing techniques such as standard normal variate andmultiplicative scatter (or signal) correction have also been investi-gated to reduce the spectral difference among instruments andimprove the robustness of VNIR models (Geladi et al., 1999; Krameret al., 2008). However, most of these efforts have appeared in thespectroscopy literature. In the soil community, almost all studiespreviously reported in the literature used a single VNIR spectrometer,and the issue of instruments/scanning conditions has not raised muchattention among researchers. On the other hand, VNIR-DRS is clearly acost-effective method for soil analysis, suggesting that this techniquemight be broadly applied in the near future. In this context, multipleinstruments can almost certainly be employed, making cross-calibration an increasingly salient issue.

Establishment of a soil spectral library with a robust calibration ona particular instrument requires scanning of more than severalthousands of soil samples. Obviously such an effort cannot berepeated for each instrument. Several questions emerge: If aninstrument is replaced by a new one, can the spectral library of theprevious instrument still be used to infer soils scanned by the newinstrument? In the context of a global soil spectral library, a size of~5.2×109 spectra was estimated by Brown et al. (2006) to have theglobal coverage. A library of this size is apparentlymuch larger than allexisting soil spectral libraries reported in the literature (Shepherd andWalsh, 2002; Brown et al., 2006; Viscarra Rossel et al., 2008). A global

soil VNIR library will also be several orders larger than spectrallibraries in use for other products such as grains and forages(Bouveresse and Campbell, 2001). A worldwide collaboration isneeded and it is inevitable that spectra derived from different labs/instruments should be gathered and merged into a common library.The question is: How useful would such a global library be if itscompositional spectra are measured bymany different instruments orthe same instrument but quite different scanning conditions?

The objectives of this study are to: (1) compare soil spectrameasured with different spectrometers under different scanningconditions, (2) compare the performance of resultant calibrationmodels, and (3) assess the transferability of spectral modelsdeveloped with a given instrument a/o under specific conditions topredict OC. Among the factors that contribute to spectral variabilitymentioned above, some can be controlled by certain means (e.g.,sample preparation) but some are very difficult to control (e.g.,operators scanning habits). Therefore we are not providing athorough investigation on the effect of each individual factor on soilreflectance spectra and calibration models. Rather, we are interestedin examining if their general effects could be mitigated by controllingsome of these factors. It is our hope, through this paper, to raiseawareness of issues pertaining to the spectral fusion of data fromdifferent instruments under varying scanning conditions that willneed to be addressed when constructing global spectral libraries forthe prediction of various soil properties. We also hope this study canprovide some rudimentary, but important, insights on the futureutility of VNIR-DRS in soils where spectral data collected withdifferent instruments are merged to build larger soil spectral libraries.

2. Materials and methods

2.1. Soil samples

Two sets of soil samples, both collected and lab-analyzed in theDepartment of Soil and Crop Sciences, Texas A&MUniversity, were usedin this study. The first set, referred to as the QU set, contained 180 soilsamples from 45 cores collected from a natural giant reed (ArundoDonax L.) and Coastal Bermuda grass (Cynodon Dactylon) landscape inQuemado, TX. These cores had a depth of 50 cm or to a restrictivehorizon. The soils belong to the Rio Grande series (coarse-silty, mixed,active, calcareous, hyperthermic Aridic Ustifluvents). The second set,referred to as the CT set, contained 264 soil samples from 72 soil corescollected from six fields in Erath and Comanche Counties in CentralTexas. The coring depth was 105 cm or to a restrictive horizon. Therewere 21 soil series, five soil orders, and multiple parent materialspresent in these soil samples. Readers are referred to Waiser et al.(2007) for detailed information regarding this sample set. Both setswere subject to the same lab analytical procedures for reference soilproperty measurement. Each sample was oven dried at 60 °C for 24 h,ground and homogenized until the whole sample passed through a2-mm sieve. We chose organic carbon content (OC) as the targetproperty for two reasons. First, OC has been successfully quantified byVNIR-DRS in many previous studies as summarized in the reviewarticles by Viscarra Rossel et al. (2006) and Grunwald (2009). Secondly,soil OCmeasurementsmay be required for a vast number of samples forthe purpose of quantifying soil carbon sequestration and carbon credits(Reeves III et al., 2002; Brown et al., 2005; Reeves III, 2010). Total carboncontent (TC) was determined by the dry combustion method describedby Nelson and Sommers (1996). Inorganic carbon content (IC) wasdetermined by themodified pressure calcimetermethod (Sherrod et al.,2002) and OC was then calculated as TC less IC.

2.2. Spectrometers and sample scanning

Four spectrometers were used in this study, and each of them ishoused in and maintained by a different academic research lab. For

Table 1Maker, model, and spectral characteristics of the four spectrometers used in this study.

Abbreviation Maker† Model Spectralrange (nm)

Samplinginterval (nm)

Output

SP1 ASD AgriSpec 350–2500 1 ReflectanceSP2 Foss NIRSystems

6500400–2498 2 Absorbance

SP3 ASD LabSpec 5000 350–2500 1 ReflectanceSP4 ASD FieldSpec Pro 350–2500 1 Reflectance

†ASD: Analytical Spectral Devices Inc., Boulder, Colorado, USA; Foss: Foss NIRSystems,Inc., Denmark.

Table 2Summary statistics of soil organic carbon content in calibration and validation for bothsample sets in this study.

N Min. Median Max. Mean SD CV (%)

Calibration†

QU (g kg­1) 120 1.4 8.2 41.5 10.7 7.5 70CT (g kg­1) 188 0.0 7.5 55.9 10.5 10.4 98

ValidationQU (g kg­1) 60 1.3 7.6 37.2 10.0 8.2 82CT (g kg­1) 76 0.0 6.5 49.5 10.1 10.4 104

†QU means the sample set was collected from Quemado, Texas; and CT means thesample set was collected from Erath and Comanche Counties in Central Texas.SD = standard deviation; CV = coefficient of variation.

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convenience, they are referred to as SP1, SP2, SP3, and SP4 in theremainder of this paper. Table 1 gives the information about theirmaker, model, and spectral characteristics.

Two scanning experiments were conducted. In the first experi-ment, the QU set was scanned by SP1, SP2, and SP3. In thisexperiment, all instruments were operated under the routineconditions of their respective labs (namely, no intentional controlwas provided). For SP1, soil samples were placed in a borosilicatebottom petri dish and scanned from underneath with SP1's mug lamp(an accessory that contains a high-intensity illumination source andfiber optics for collection and transmission of reflected signal to thespectrometer). Two composite scans (each is an average of 15 internalscans) were obtained for each sample by rotating the petri dish 90°between. The final spectrum was obtained by averaging the twocomposite scans. For SP2, samples were placed in Foss sample cupsand clamped on SP2's spinning drawer attachment and pushedagainst its scanning window for spectral measurement. A total of 32internal scans were averaged as final spectral measurement. For SP3,samples were placed in petri dishes and scanned from above usingSP3's mug lamp. Four composite scans (each is an average of 100internal scans) were obtained for each sample from the fourquadrants of the petri dish by rotating it 90°. The final spectrumwas obtained by averaging the four composite scans. In contrast toSP1 and SP2 where sample holders were in direct contact with theirscanning window, SP3's scanning window was placed around 5 cmabove soil sample holders. For the analytical spectral devices (ASD)instruments (SP1 and SP3, Table 1), 2-h warming was allowed beforedaily scanning to stabilize temperature and account for dark currentdrift. White reference scans (with a Spectralon panel) were takenevery 12 (SP1) and 25 samples (SP3). For the Foss instrument (SP2),an instrument internal check had to be passed before daily scanning,and white referencing was taken between every sample scan.

In the second experiment, the CT set was scanned by SP1 and SP4.In this experiment, we controlled some of the scanning conditionswith our best effort by keeping the radiation source–sample–detectorconfiguration, scanning procedures, and white referencing the samefor both instruments (as described above for SP1 in the firstexperiment). We are interested in examining if these measures canremove extraneous variations and thus improve the consistency ofsoil spectra and calibration model between the instruments. As westated earlier, it is almost impractical to control all extraneous factors.In this experiment, two important factors that were not controlledincluded the inherent characteristics of the instruments and theoperators.

2.3. Spectral preprocessing, model calibration and validation

The following steps were used to preprocess the spectra: (1)remove the heading and trailing portion of the SP1, SP3, and SP4spectra so that all five spectra sets have the same range from 400 to2498 nm; (2) convert the absorption spectra of SP2 to reflectancespectra using equation R=10­A (where R is reflectance and A isabsorbance); (3) average spectra by every 10 nm; (4) convert the 10-nm-averaged reflectance spectra to the first derivative. After these

steps, five sets of first derivative reflectance spectra were attained.Each spectrum contained 209 data points, with their nominal centralwavelengths at 410, 420, … , and 2490 nm.

Each soil sample set was randomly split into a calibration set(containing 2/3 of samples) and a validation set (1/3 of samples).Samples belonging to the same core were allowed to be in bothcalibration and validation sets. Table 2 gives the summary statistics ofsoil OC in calibration and validation for both sample sets. Partial leastsquares regression (PLSR) with random 10-segments cross-validationwas used for model calibration. Models having as many as 20 latentfactors were considered, and the optimal model was determined bychoosing the number of factors that gave the first local minimum incross-validated root mean squared error (RMSEcv). Coefficient ofdetermination (R2) between lab-measured and predicted values, andratio of standard deviation of lab-measured values in calibrationmodeto RMSEcv (RPD) was also reported for the optimal model.

In each soil sample set, the model calibrated on one instrumentwas applied to all sets of validation spectra to evaluate the modelperformance. This comparative study evaluates how well a modeldeveloped on one instrument can be applied to the spectra measuredby another instrument with or without control over scanningprotocol. Model performance was evaluated using R2, bias, rootmean squared error for validation (RMSE) and ratio of standarddeviation of lab-measured values in validation mode to RMSE (RPD).

To mimic a soil spectral library whose soil spectra are likelyscanned by different spectrometers, we formed a new dataset bysequentially drawing 60 spectra scanned by SP1, 60 by SP2, and 60 bySP3 in the QU set, and 60 by SP1 and 60 by SP4 in the CT set. This newdataset (referred to as the composite set, 300 samples) was alsorandomly split into a calibration (180 samples) and validation set(120 samples). A calibration model for soil OC was developed (usingthe same PLSR method as described above), and its performance wasevaluated against the validation set with the same statistics: R2, bias,RMSE, and RPD.

2.4. Calibration transfer

Three widely used methods, namely, slope and bias correction,direct standardization, and piecewise direct standardization wereattempted to transfer calibration models from one instrument toanother. Details on the mathematical treatment of these methods canbe found in Fearn (2001) and Feudale et al. (2002). A five data-pointwindow (50 nm) was used in piecewise direct standardization. Forthe QU set, SP3 was regarded as the primary instrument and SP1 andSP2 secondary instruments. For the CT set, SP4 was regarded as theprimary instrument and SP1 secondary instrument. Because allsamples were scanned by both primary and secondary instruments,it allowed us to use the previously selected calibration set (containing2/3 of samples) as the transfer set (Table 2). The samples in validationwere used for assessing the performance of different calibrationtransfer methods from primary instrument to secondary and also no

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transfer. To compare the transfer, methods to each other and to the notransfer method, root mean squared error and bias were calculatedusing validation data.

Data analysiswasperformed inR statistical software (RDevelopmentCore Team, 2008), and PLSRmodeling and calibration transfer was donewith R's chemometrics contributed package (Filzmoser and Varmuza,2008).

3. Results and discussion

3.1. Soil reflectance spectra

The mean reflectance spectra of the QU set (n=180) scanned bythe three different spectrometers are shown in Fig. 1A. The differenceswere substantial, because no effort was provided to control the effectsof instrument/scanning environments for this set (however, all scanswere completed by strictly following the respective lab protocols).Most strikingly, the overall reflectance of SP3 was much lower thanthat of SP1 and SP2. This was caused by the fact that the Spectralonpanel was in direct contact with SP3's mug lamp during whitereference, yielding much stronger signals (raw digital numbers)against which soil signals were ratioed. The absorption features ataround 1400, 1900, and 2200 nm were clearly seen in all meanspectra, though these features were very much damped in SP3. Meanspectra of SP1 and SP3 shared general similarity with each other, asboth instruments are ASD products. The discontinuities at 1000 and1800 nm were evident in SP1 and SP3. These wavelengths areapproximately where the ASD instruments change the diffractiongratings and optical detectors. For SP2, the discontinuity at 1100 nmcorresponds to where Foss changes its detector. Mean spectra of SP2generally follow the same profile, but there is distinctiveness in thevisible bands (400–700 nm). Conversion to the first derivativealleviates the overall reflectance problem in SP3, as all three meanspectra of the first derivative were brought to a comparable scale

Fig. 1. Mean reflectance and the first derivative spectra of the Quemado sample set (A and B(C and D) scanned by different spectrometers. SP1 is ASD AgriSpec; SP2 is Foss NIRSystems

(Figure 1B). However, SP3 still had much lower absolute values atalmost all wavelengths. It also becomes obvious now that SP2introduced several absorption features in the visible range (400–700 nm) that were absent from the first derivative spectra of SP1 andSP3. While the cause of these features is not known (possibly becauseof inherent characteristics of SP2's electro-optical system, differentenergy path, and a different packing density), it demonstrates howdistinctive measured soil spectra can be by different instrumentmakers and models.

The mean reflectance spectra of the CT set (n=264) scanned bythe two different spectrometers are shown in Fig. 1C. There are stillapparent discrepancies in the mean reflectance, even though weattempted our best to control the scanning environments in thisexperiment. It is not totally unexpected, nonetheless, as there areother factors that cannot be controlled and they contribute toextraneous variations in soil spectra. Conversion to the first derivativemade the two spectral sets match remarkably well to each other(Figure 1D), particularly in terms of the main absorption features.

As stated earlier, the samples in the QU set belong to a single seriesand those in the CT set belong to 21 different series. Therefore the CTset is compositionally more diverse than the QU set, which leads tothe wider range and higher CV found in OC for both calibration andvalidation of the CT set (Table 2). Fig. 2 is a side-by-side scatter plot,which shows the first three principal component (PC) scores of thefirst derivative spectra scanned by SP1 for both QU and CT sets. Clearlythe CT set shows greater spectral variation for all three PCs and ismuch more scattered in the PC space, whereas the QU set showssmaller variation and its data cloud is compact. The higher spectraldiversity exhibited by the CT set also indicates that it is morecompositionally diverse than the QU set.

Fig. 3A and B shows side-by-side scatter plots of the first three PCsof the first derivative spectra by different instruments for the QU andCT sets, respectively. For the QU set, spectra by the three spectro-meters were well separated on all three PCs. For the CT set, spectra by

) and mean reflectance and the first derivative spectra of the Central Texas sample set6500; SP3 is ASD LabSpec 5000; and SP4 is ASD FieldSpec Pro.

Fig. 2. Side-by-side scatter plots of the first three principal components (PC) of the firstderivative spectra of both sample sets scanned by the same spectrometer (ASDAgriSpec). Black are the Quemado set and red are the Central Texas set. To increase thevisibility of the data clouds, axes are not drawn to the same scale. Numbers inparentheses show the portion of variance each PC accounts for.

Fig. 3. Side-by-side scatter plots of the first three principal components of the firstderivative spectra of the Quemado set scanned by three different spectrometers(A: black are scanned by SP1; red are scanned by SP2; green are scanned by SP3) andthe Central Texas set scanned by two different spectrometers (B: black are scanned bySP1; red are scanned by SP4). To increase the visibility of the data clouds, axes are notdrawn to the same scale. Numbers in parentheses show the portion of variance each PCaccounted for.

206 Y. Ge et al. / Geoderma 161 (2011) 202–211

the two spectrometers were almost inseparable on PC1 and PC2, butclear separation appeared on PC3. Because PC3 only accounted for asmaller fraction of total variance comparing to PC1 and PC2, these twoplots showed that control of scanning protocol made soil spectramorecomparable to each other.

3.2. Calibration and validation

Table 3 summarizes the calibration results of OC using the firstderivative spectra. For the QU set, models calibrated on all threespectrometers can predict OC quite successfully, with R2 greater than0.75 and RPD greater than 2.0. SP2 has the highest predictionaccuracy, whereas SP3 has the lowest accuracy. SP2 implementedwhite referencing between every sample scan; therefore, its higherpredictive power is not surprising (e.g., light source output due to ACvoltage fluctuation and dark current due to temperature change) canbe better accounted for. Because each spectrummeasured by SP3 wasan average of 400 sensor internal scans, higher signal to noise ratioshould be achieved by SP3 than SP1. Its lower predictive power is,therefore, not expected, but likely caused by stray light that wasattributable to the space between petri dishes and the mug lamp. TheRPD values for all models are greater than 2.0, suggesting satisfactoryand effective calibrations of OC using the first derivative spectra byeach spectrometer. For the CT set, the models calibrated on the twospectrometers showed somewhat less accurate results, with R2 valuesbetween 0.6 and 0.7 and RPD between 1.6 and 1.7. As we establishedearlier, the CT set is both compositionally and spectrally moreheterogeneous than the QU set. It is, therefore, not surprising theprediction on the CT set is not as good as the QU set.

Fig. 4 compares the OC PLSR calibration models (in terms of theregression coefficients of each waveband) given in Table 3. For the QUset, the three models show noticeable differences among them interms of both relativemagnitude and sign of the regression coefficientat each waveband. The magnitude of regression coefficients in theQU3 is generally larger. This is apparently associatedwith damped soilreflectance spectra (and the first derivative) measured by SP3(Figure 1). There are some significant wavebands roughly between750 and 1350 nm for model QU1. However there are few significant

wavebands in this region for models QU2 and QU3. All three modelsshow a concentration of significant wavebands from 2000 to 2300 nm.This is the region, according to the literature, where the spectralinformation concerning OC abounds (e.g., overtones or harmonics ofthe stretching and vibration of C-H, N-H, and C=Obonds, Malley et al.,2002). Compared to the QU models, the two CT models are verysimilar to each other, with respect to not only the relative magnitudeand sign of regression coefficients, but also the locations of significantwavebands (such as a collection of significant bands between 700 and800 nm). A collection of significant wavebands from 2000 to 2300 nmwas also present in these two CT models.

Fig. 5 shows the result of applying each QUmodel to all three sets ofspectra in the validation set. The results were organized in a matrix

Table 3Calibration results of soil organic carbon content using the first derivative reflectancespectra from different spectrometers.

Model† n No. of factors R2 RMSEcv (g kg­1) RPD

QU1 120 10 0.81 3.3 2.29QU2 120 9 0.86 2.8 2.66QU3 120 6 0.75 3.7 2.02CT1 176 10 0.60 6.5 1.60CT4 176 8 0.66 6.0 1.72Composite 200 11 0.55 5.4 1.49

†Models QU1, QU2, and QU3 were calibrated from the spectra of the Quemado sampleset scanned by SP1 (ASD AgriSpec), SP2 (Foss NIRSystems 6500), and SP3 (ASD LabSpec5000), respectively; models CT1 and CT4were calibrated from the spectra of the CentralTexas sample set scanned by SP1 and SP4 (ASD FieldSpec Pro), respectively; and modelcomposite was calibrated with the composite dataset.RMSEcv is cross-validated root mean squared error.RPD is ratio of standard deviation of lab-measured value in calibration to RMSEcv.

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representation: diagonal subplots indicated calibration models andvalidation spectra were from the same instrument and off-diagonalsubplots indicated they were not. Apparently, validationwas successfulwhen the calibration model and validation spectra were from the sameinstrument (subplots along the diagonal of Figure 5), as indicated byhigh R2 and RPD values but low bias. The regression lines between lab-measured and predicted values were all close to the 1:1 line. In caseswhere the calibration model and validation spectra were not from thesame spectrometers (off-diagonal subplots), poor performanceresulted. The RMSE values were generally high and RPD values werelow. The regression lines deviated substantially from the 1:1 line (largebias associated with non-zero intercept and/or non-unity slope).

Likewise, Fig. 6 shows the result of applying each CT model to bothsets of spectra in the validation set. The results were also organized ina matrix representation. It can be seen that both diagonal and off-diagonal subplots show comparable validation results. Unlike our firstexperiment, both CT models can be applied to the other set of

Fig. 4. Regression coefficients of the six soil organic carbon calibration models as given in Tathe length of the bar. Significant wavebands (pb0.05) as indicated by Tukey's jackknife var

validation spectra without substantially decreasing its predictionaccuracy. R2, bias and RPD values for the diagonal and off-diagonalsubplots are essentially comparable.

Calibration–validation resultswith the composite dataset (Table 3 andFigure 7) revealed some very interesting results. The OCmodel calibratedon this dataset is poorer compared to all other models that are calibratedon a single instrument. In the calibration, the R2 value is 0.53 and RPD is1.49. In the validation, the R2 value is 0.63 and RPD is 1.62. The lowerprediction accuracy of this model is expected, because by forming thiscomposite dataset,we introduced a lotmore spectral variations thatwererelated to extraneous effects. Before this experiment, we had expectedthat somedataset–instrument combination groupswouldbe consistentlyover-predicted and some under-predicted. Fig. 7 indicates that all fivegroups arewell distributed around the regression line,withno systematicover-prediction or under-prediction for a certain group. Although it isdifficult to explain these patterns, it is reasoned that PLSR has thecapability inmodeling (or accommodating) spectral variations associatedwith not only soil chemical compositions but also extraneous effects(both randomand systematic). The net effect is a larger PLSRmodel (withadditional latent factors to account for extraneous effects) that is moreinclusive to various situations. This leads to opposite views in using VNIR-DRS: a global calibration model that includes all sources of possiblevariation leading to extended applicability of the model at the cost ofdecreasing prediction accuracy; or local calibration models that areaccurate but with limited applicability (e.g., valid for a single instrumentor soil samples belonging to similar parental materials). Somemay arguethat the R2 value and RPD in the second experiment (Figure 7)might stillbe in acceptable ranges for some applications, thus, would favor globalcalibration.However, it shouldbenoted thatwearedealingwithonlyfivedataset–instrument combinations. In real life, a global soil spectral librarywill contain compositionally very diverse soils and they may be scannedby many spectrometers (tens or even hundreds). The variationsassociatedwith extraneous effectswouldbecome so large that it becomesdoubtful if such a global calibration model could be constructed and stillprovides acceptable prediction accuracies.

ble 3. The magnitude of the regression coefficient at each wavelength is proportional toiance estimate procedure are shown as thick, red bars.

Fig. 5. Lab-measured versus VNIR-DRS predicted soil organic carbon content for the validation set (n=60) in the Quemado sample set. The validation was done by applying eachcalibration model to validation spectra by different spectrometers. Models QU1, QU2, and QU3 are calibrated on SP1, SP2, and SP3, respectively, and validation spectra 1, 2, and 3 aremeasured by SP1, SP2, and SP3, respectively.

208 Y. Ge et al. / Geoderma 161 (2011) 202–211

Table 4 summaries the results of calibration transfer for both datasets.In case of the QU set where no control was provided during spectralscanning, all three methods provided a satisfactory application of theVNIR model calibrated on the primary instrument (SP3) to secondaryinstruments (SP1 and SP2). For the CT set, calibration transfer improvedthe prediction accuracy very slightly (Table 4). When no control overscanning protocol was provided, calibration transfer was necessary tomake calibration models applicable to another instrument/scanningenvironment. When scanning control was provided, however, modeltransfer improved the calibrationmodel marginally because themajorityof the extraneous variation in reflectance spectra was being controlledduring scanning.

3.3. Implications

Our study demonstrated that instruments/scanning environmentscan have profound effects on soil reflectance spectra and the resultantcalibration models. In the first scanning experiment (the QU set), both

reflectance and the first derivative spectra showed great inconsistencyamong different spectrometers. Despite the high calibration accuracyfor each model (Table 3), their application to the soil spectra measuredby other spectrometers resulted in significant bias and low predictionaccuracy (Figure 5). On the other hand, the second scanning experiment(the CT set) indicates that consistency of soil spectra and calibrationmodels among different instruments could be improved by theprovision of control over scanning protocol. Specifically, Fig. 1D showsthe twosets of thefirst derivative spectramatch remarkablywell to eachother and Fig. 4 indicates very similar regression coefficients for the twoCT models. Most importantly, Fig. 6 shows that the calibration modelscan be successfully applied to other instruments without significantdecrease in prediction accuracy, thus extending the applicability of theVNIR-DRS model to other instruments.

Off-diagonal subplots in Fig. 5 suggest that, even though theprediction results are poor (high RMSE, high bias, and low RPD),strong linear relationships between the lab-measured and predictedvalues still exist (indicated by high R2 values in all cases). The bias is

Fig. 6. Lab-measured versus VNIR-DRS predicted soil organic carbon content for the validation set (n=88) in the Central Texas sample set. The validation is done by applying eachcalibration model to validation spectra by different spectrometers. Model CT1 and CT4 are calibrated on SP1 and SP4, respectively, and validation spectra 1 and 4 were measured bySP1 and SP4, respectively.

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largely systematic (non-unity slopes and non-zero intercepts) and theincrease of scatter (or randomness) around regression lines isrelatively small. This further suggests that the extraneous variationsin soil spectra introduced by instruments/scanning environments aremainly systematic. Random noise can largely be removed byaveraging many internal scans. Realizing this effect, other NIRcommunities (e.g., agricultural products such as grain and forage)usually maintain/develop a set of “standard” samples whenevercalibration models need to be transferred from one instrument/

Fig. 7. Lab-measured versus VNIR-DRS predicted soil organic carbon content for thevalidation set (n=100) of the composite dataset. The dataset is composed of,approximately, 1/5 samples from the QU set and scanned by SP1 (◊), 1/5 samplesfrom the QU set and scanned by SP2 (○), 1/5 sample from the QU set and scanned bySP3 (□), 1/5 samples from the CT set and scanned by SP1 (▲), and 1/5 samples from theCT set and scanned by SP4 (●).

scanning environments to another (Bouveresse and Campbell, 2001;Fearn, 2001; Shenk et al., 2001). In the soil community, this issue hasnot yet been adequately investigated. Nevertheless, the result in thisstudy (Table 4) provides preliminary evidence that calibrationtransfer will be very important in soil spectroscopy with a multipleinstrument/scanning environment.

Although VNIR-DRS has already been widely researched for soilcharacterization, the majority of research efforts was carried out aslocal, ad-hoc studies by individual research groups. In most of thesestudies, calibration and validation (or prediction) samples werescanned by a single spectrometer, and considerations on the issue of

Table 4Summary of root mean squared error and bias (in parentheses) for the selected modeltransfer methods. For the QU set, SP3 is considered as the primary instrument, and SP1and SP2 as the secondary instrument; for the CT set, SP3 is considered as the primaryinstrument, and SP1 as the secondary instrument.

Primary Secondary

Organic carbon, g kg­1

QU setCalibration transfer method SP3 SP1 SP2

No transfer 3.0 (0.9) 17.3 (10.5) 56.6 (55.7)Slope and bias correction 4.2 (0.1) 3.9 (0.1)Direct standardization 3.0 (0.2) 3.8 (0.8)Piecewise direct standardization 2.3 (0.3) 2.4 (0.5)

CT setCalibration transfer method SP4 SP1

No transfer 4.9 (­0.6) 5.9 (­0.4)Slope and bias correction 5.6 (­1.0)Direct standardization 5.7 (­0.8)Piecewise direct standardization 5.7 (­0.9)

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instruments/scanning conditions were more or less irrelevant. On theother hand, VNIR-DRS is insofar the most cost-effective method forsoil characterization and its applications on the regional, national, andglobal scales have been proposed and actively pursued (e.g.,establishment of a dedicated soil characterization lab based onVNIR-DRS and development of a global soil reflectance spectra library).The results of this study provided some important implications on thesefuture applications.

Development of a global soil spectral library is more challengingthan other products such as grain or forage in several aspects. Soils areinherently far more diverse than any other traditional NIR products,both compositionally and spectrally. To build an all-inclusive library,Brown et al. (2006) estimated ~5.2×109 samples, which is 5–6 orderslarger than existing NIR libraries (Fearn, 2001; Shenk et al., 2001).This is apparently not possible without a global collaborationinvolving hundreds (if not more) of labs and instruments. “Standardsamples” for calibration transfer is also difficult, because identifying~30 representative samples (Fearn, 2001; Shenk et al., 2001;Bouveresse and Campbell, 2001; Feudale et al., 2002) that span a lotof variation with respect to many soil properties and spectra is not aneasy task. In this study we focused only on soil OC, however, other soilproperties may show different variability in attribute space whichmay confound findings further.

For these reasons, we think that provision of prudent measuresduring scanning to limit the effects of instruments/scanning environ-ments is of paramount importance for the quality, success, andsustainability of the soil VNIR library approach will be critical in thefuture. Practically, the planners could establish a set of standardscanning protocols (that specifies, for example, sample preparation,lab temperature and humidity, radiation source–sample–detectorconfiguration, length and bending of fiber optics, dark currentcorrection and white referencing, and so on), and only samples thatare strictly scanned following these standards can be included in a“global” library. Careful planning may benefit users by (1) improvingthe performance of a VNIR library and (2) minimizing the need forpainstaking calibration transfer on some occasions (as demonstratedin Table 4 and Figure 6).

To develop broad spectral libraries accommodating multiple soilproperties and diverse geographic soil regions, one ideal methodwould be to designate a network of qualified soil labs around theworld for spectral scanning. Training, education, and quality controlcan be provided to ensure that all participating labs have comparableequipment and follow the standard scanning protocols. Althoughthese practices will cause extra efforts in obtaining soil spectra andcannot completely remove all extraneous effects, we believe they arewell justified by the potential benefits of improved predictionaccuracy and extended applicability for its intended purposes. Asdemonstrated in our composite dataset, including everything indis-tinguishably into the spectral library and relying on the black-boxedPLSR to handle the rest will certainly affect its prediction performance(if it will work at all) and jeopardize its reliability and credibilityamong the end users.

4. Conclusion

Two sets of soil samples (one collected from Quemado Texas andthe other from Central Texas) were scanned by different VNIRspectrometers to compare soil reflectance spectra and the calibrationmodels for soil organic carbon content. We found that (1) instru-ments/scanning environments have significant effects on soil reflec-tance spectra and therefore the calibration models; (2) directapplication of models calibrated on one particular instrument/scanning environment to other instrument/scanning environmentgenerally yields poor prediction results; and (3) the influence ofinstrument/scanning environment could be greatly mitigated byprovision of control measures during soil scanning along with

standardized spectral preprocessing techniques (e.g., thefirst derivative),which might make the calibration models more robust and extend thescope of their applicability. The transfer of calibrationmodels, which hasnot been adequately investigated in the soil VNIR community, also bearspromise in addressing the changing instrument/scanning environmentissue. As more and more research focuses have been attracted to thebroader applications of VNIR-DRS in various soil disciplines, it is time toraise the awareness of this issue among pedometricians and otherinterested groups. The issue needs to be properly and sufficientlyaddressed before VNIR-DRS can be effectively put into wide-scope soilapplications.

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