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Europ. J. Agronomy 52 (2014) 198–209

Contents lists available at ScienceDirect

European Journal of Agronomy

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Reflectance estimation of canopy nitrogen content in winter wheatusing optimised hyperspectral spectral indices and partial leastsquares regression

Fei Li a,b, Bodo Misteleb, Yuncai Hub, Xinping Chenc, Urs Schmidhalterb,∗

a College of Ecology & Environmental Science, Inner Mongolia Agricultural University, 010019 Hohhot, Chinab Chair of Plant Nutrition, Department of Plant Sciences, Technische Universität München, Emil-Ramann-Str. 2, D-85350 Freising-Weihenstephan, Germanyc College of Resources & Environmental Sciences, China Agricultural University, 100094 Beijing, China

a r t i c l e i n f o

Article history:Received 22 November 2012Received in revised form 27 August 2013Accepted 4 September 2013

Keywords:Winter wheatCanopy N contentPLSRSpectral indices

a b s t r a c t

Many spectral indices have been proposed to derive plant nitrogen (N) nutrient indicators based ondifferent algorithms. However, the relationships between selected spectral indices and the canopy Ncontent of crops are often inconsistent. The goals of this study were to test the performance of spectralindices and partial least square regression (PLSR) and to compare their use for predicting canopy N contentof winter wheat. The study was conducted in cool and wet southeastern Germany and the hot and dryNorth China Plain for three winter wheat growing seasons. The canopy N content of winter wheat variedfrom 0.54% to 5.55% in German cultivars and from 0.57% to 4.84% in Chinese cultivars across growthstages and years. The best performing spectral indices and their band combinations varied across growthstages, cultivars, sites and years. Compared with the best performing spectral indices, the average valueof the R2 for the PLSR models increased by 76.8% and 75.5% in the calibration and validation datasets,respectively. The results indicate that PLSR is a potentially useful approach to derive canopy N contentof winter wheat across growth stages, cultivars, sites and years under field conditions when a broadset of canopy reflectance data are included in the calibration models. PLSR will be useful for real-timeestimation of N status of winter wheat in the fields and for guiding farmers in the accurate applicationof their N fertilisation strategies.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Canopy N content is one of the important N nutrient diagno-sis indicators of plants, governing canopy carbon assimilation; itis often positively associated with leaf and canopy chlorophyllcontent and canopy photosynthetic capacity (Smith et al., 2003;Green et al., 2003; Oppelt and Mauser, 2004; Ollinger et al., 2008;Stroppiana et al., 2009). Timely detection of the canopy N con-tent of crops on a regional scale is important not only to obtainan overview of the N distribution, but also to gain knowledge ofcanopy energy exchange in agro-ecosystems. Therefore, up-scalingbeyond discontinuous field-based small sampling points is neces-sary for regional N budget estimation as well as for carbon cyclingsevaluations (Ollinger et al., 2008).

One effective and timely approach used is to remotely esti-mate canopy N content using calibrated relationships betweencrop canopy reflectance parameters and lab-based wet chemical

∗ Corresponding author. Tel.: +49 8161 713390; fax: +49 8161 714500.E-mail address: [email protected] (U. Schmidhalter).

analysis data (Mistele and Schmidhalter, 2008). As plant N concen-tration is linked to the amount of chlorophyll, many studies havefocused on estimating crop leaf chlorophyll concentration, whichgive an indirect assessment of canopy- or leaf-based N status ofcrops (Haboudane et al., 2008). The most common method of deriv-ing canopy N content using remote sensing is to derive spectralindices by incorporating two or more characteristic wavebands intoa simple ratio or into a more complicated formula based on algo-rithms and N-related plant physiological significance (Pinter et al.,2003; Hatfield et al., 2008; Ollinger, 2011). However, unlike above-ground biomass production and canopy N uptake, canopy N contentdecreases with the progression of growth stages and produce “dilu-tion effects” as described by Lemaire et al. (2008). For example, theN content of plants is highest at early growth stages and decreasescontinually up to the stage of senescence because the N uptakeper unit of above-ground biomass accumulated decreases as theleaf area per unit crop mass decreases. In the vegetative growthperiod in particular, an increase in the rate of biomass productioncompared to that of canopy N uptake results in a rapid decreasein canopy N content. The variation in above-ground biomass andcanopy structure dominates the canopy spectral reflectance. Thus,the “dilution effect” and the variation in canopy structure probably

1161-0301/$ – see front matter © 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.eja.2013.09.006


F. Li et al. / Europ. J. Agronomy 52 (2014) 198–209 199

Fig. 1. The reflectance in different winter wheat cultivars, growth stages and sites.

affect the selection of sensitive bands for spectral indices. Using thealgorithm of all possible two band combinations at 400–1000 nm,Hansen and Schjoerring (2003) found that the combination of R440with R573 was the best performing NDVI-like index for derivingthe canopy N content of winter wheat. Li et al. (2010), however,suggested that (R410 − R365)/(R410 + R365) could better estimatecanopy N content of winter wheat compared with other publishedspectral indices. Similarly, there were inconsistencies observedin the selection of the sensitive bands as reported by Zhu et al.(2007), Stroppiana et al. (2009) and Tian et al. (2011) for rice. Theseinconsistencies may result from an indirect estimation of plant Nconcentraton because nitrogen does not directly absorb radiationin the VIS-NIR region. Delegido et al. (2010, 2011) proposed an area-based index, the Normalised Area Over Reflectance Curve (NAOC),that successfully derived the canopy chlorophyll content of differ-ent crops under heterogeneous conditions. However, there is littleknowledge available relating to the derivation of canopy N contentbased on mass using the NAOC.

Spectral indices with simple ratios or combined formulas focusmostly on 2–3 bands only, which make it difficult to construct aunified index to remotely estimate canopy N content across differ-ent growth stages, cultivars, sites and years due to the influencesof the “dilution effect” and the variation in the canopy structure ofthe crops. Optimum multiple narrow band reflectance using steplinear regression analysis has been commonly used to identify thecharacteristic bands related to the crop biophysical and biochem-ical parameters of interest (Thenkabail et al., 2000; Serrano et al.,2002). However, this method suffers from “over fitting”, becausethe number of spectral bands exceeds the number of experimen-tal samples (Thenkabail et al., 2000; Nguyen and Lee, 2006). Incontrast, although most of the waveband reflectances have beenused to estimate plant biochemical concentrations, partial leastsquare regression (PLSR) overcomes the problems of collinearityand “over-fitting” compared to step linear regression analysis ifoptimally choosing a suitable number of principal components anddeleting the noise bands (Herrmann et al., 2011). However, thesmall number of sampling may limit the number of latent vari-ables in the PLSR model and reduce the calibration accuracy (VanDer Heijden et al., 2007). The PLSR has been widely used to derivethe chemical compositions in reagents and dry samples (Wold et al.,2001; Gislum et al., 2004) and to assess N related indicators of cropsin homogeneous areas (Nguyen and Lee, 2006; Soderstrom et al.,2010). Limited research has been conducted to estimate canopyN content in heterogeneous fields with different growth stages,cultivars, sites and years under contrasting climatic conditions. Fur-thermore, there is limited knowledge on how these factors affectthe performance of PLSR in evaluating mass-related canopy N con-tent of winter wheat.

To date, many studies have been performed that attempt toderive biophysical and biochemical parameters of interest witha relatively homogeneous medium in both the field and the lab(Mutanga and Skidmore, 2004; Cho and Skidmore, 2006, Cho

et al., 2008). Most of these studies were conducted in a homo-geneous medium with the same ecological area under controlledconditions. Limited experiments were performed to address theeffects of canopy structure or the “dilution effect” on remoteevaluation of the canopy N content of winter wheat under het-erogeneous field conditions Therefore, the main objectives of thecurrent study were as follows: (1) to address how the dilutioneffect, growth stage, cultivar, site and year influence the relation-ships between spectral parameters and the canopy N content ofwinter wheat; and (2) to compare the performance of spectralindices and PLSR for estimating the canopy N content in winterwheat.

2. Materials and methods

2.1. Field experiments and design

All experiments were conducted at the Dürnast Research Stationof the Technische Universität München (TUM) in Freising, insoutheast Germany, and at the experimental station of the ChinaAgricultural University (CAU) in Quzhou County in the NorthChina Plain during the winter wheat growing seasons of 2009through 2011. As illustrated in Fig. 1, Freising is characterisedby a typical oceanic climate with mild cloudy winters and wet,cool summers, while Quzhou County lies in the warm-temperatesubhumid-continental monsoon zone and is cold in winter anddry and hot in summer. Hence, no irrigation is applied in Freis-ing, whereas the farmers in Quzhou irrigate their winter wheat3–4 times during the season using flood irrigation with water fromwells.

An experiment at Freising in 2008/2009 was performed usingeight N rates (0, 60, 120, 180, 240, 300, 360 and 420 kg N ha−1)with three replications, three German winter wheat cultivars(Solitär, Ellvis and Tommi) and one Chinese cultivar (Nongda318).At Quzhou, two experiments were carried out in 2009/2010 and2010/2011. One German cultivar (Tommi) and two local cultivars(Heng4399 and Kenong9204) were used in Experiment 1 withseven N rates (0, 60, 120, 180, 240, 300, 360 kg N ha−1) based onthe residual soil mineral N previously assessed using a quick-testmethod (Schmidhalter, 2005). One Chinese wheat cultivar, Liangx-ing99, was used in Experiment 2; the five N treatments used inthis experiment were the control (no N applied), 50% of the opti-mum (Opt) N rate, Opt rate, 150% of the Opt and conventional(Con) N rate. The Opt was based on the above-ground N require-ment and the soil N supply for the two growing periods (sowingto shooting, shooting to harvest) (Chen et al., 2006). The conven-tional N treatment represents the local farmers’ practice, in which150 kg N ha−1 was applied before sowing and 150 kg N ha−1 wasapplied as top-dressed fertiliser at the shooting stage. In addition,some farmers’ fields near the Quzhou experimental station wereselected in 2009–2011; these fields were managed by the farm-ers.


200 F. Li et al. / Europ. J. Agronomy 52 (2014) 198–209

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Fig. 2. Monthly rainfall and average temperature in (a) Freising from 1999 to 2008 and (b) Quzhou from 2007 to 2011.

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Fig. 3. Relationship between the above-ground biomass and the canopy N content indicating the “dilution effect” for (a) German and (b) Chinese cultivars.

Table 1Algorithms corresponding to the hyperspectral indices used in this study.

Spectral index Formula References

Ratio and normalised based algorithmsNormalised difference vegetation index (NDVI1) (R780 − R670)/(R780 + R670) Rouse et al. (1974)Normalised difference spectral index 1 (NDVI2) (R573 − R440)/(R573 + R440) Hansen and Schjoerring (2003)Normalised difference spectral index 2 (NDVI3) (R410 − R365)/(R410 + R365) Li et al. (2010)Normalised difference spectral index 2 (NDVI4) (R503 − R483)/(R503 + R483) Stroppiana et al. (2009)Ratio vegetation index (RVI1) R780/R670 Pearson and Miller (1972)Ratio vegetation index (RVI2) R787/R765 Fava et al. (2009)NIR/NIR R780/R740 Mistele and Schmidhalter (2010)Red edge position (REIP) 700 + 40 × [(R670 + R780)/2 − R700]/(R740 − R700) Guyot et al. (1988)Optimised vegetation index 2 (VIopt2) R760/R730 Jasper et al. (2009)Zarco-Tejada & Miller (ZTM) R750/R710 Zarco-Tejada et al. (2001)Normalised difference red edge index (NDRE) (R790 − R720)/(R790 + R720) Barnes et al. (2000)The MERIS terrestrial chlorophyll index (MTCI) (R750 − R710)/(R710 − R680) Dash and Curran (2004)Red-edge model index (R-M) (R750/R720) − 1 Gitelson et al. (2005)Green model index (G-M) (R750/R550) − 1 Gitelson et al. (2005)Chlorophyll absorption in reflectance index (CARI) (R700 − R670) − 0.2 × (R700 + R550) Kim et al. (1994)Transformed chlorophyll absorption in reflectance index (TCARI) 3 × [(R700 − R670) − 0.2 × (R700 − R550)(R700/R670)] Haboudane et al. (2002)Modified chlorophyll absorption in reflectance index (MCARI) [(R700 − R670) − 0.2 × (R700 − R550)](R700/R670)) Daughtry et al. (2000)TCARI/OSAVI TCARI/OSAVI Haboudane et al. (2002)Canopy chlorophyll content index (CCCI) (NDRE − NDREMIN)/(NDREMAX − NDREMIN) Barnes et al. (2000)Normalised difference spectral index (NDSI)# (R�1 − R�2)/(R�1 + R�2), R�1 > R�2 This study

Chlorophyll absorption area based algorithmsTriangle vegetation index (TVI) 0.5 × [120 × (R750 − R550) − 200 × (R670 − R550)] Broge and Leblanc (2000)Modified triangular vegetation index 1 (MTVI1) 1.2 × [1.2 × (R800 − R550) − 2.5 × (R670 − R550)] Haboudane et al. (2004)Modified triangular vegetation index 2 (MTVI2) 1.5×[1.2×(R800−R550)−2.5×(R670−R550)]√

(2×R800+1)2−(6×R800−5×√

R670−0.5)

Haboudane et al. (2004)

Normalised area over reflectance curve (NAOC)& 1 −∫ b

a�d�

�max(b−a) Delegido et al. (2010)

# �1 and �2 stand for the wavelength in 300–1150 nm and R�1 and R�2 stand for the reflectance wavelength �1 and �2. The bands combination (�1, �2, R�1 and R�2) wasoptimised using a Matlab program at 9 datasets.

& � is the reflectance, � the wavelength, �max is the maximum far-red reflectance, corresponding to reflectance at the wavelength “b”, and “a” and “b” are the integrationlimits.



2.2. Canopy spectral measurements

Spectral reflectance data at the canopy level were collectedusing a passive spectrometer (tec5, Oberursel, Germany). The spec-tral reflectance of different cultivars, growth stages and sites isshown in Fig. 2. The measuring head of this device consists of twooptics: the upper optic is used to quantify the incoming light asa reference, and the lower optic records the reflectance from thevegetation and the ground (Erdle et al., 2011; Winterhalter et al.,2011; Li et al., 2012). The sensors have a bandwidth of 3.3 nm andcan measure 256 bands, with a spectral detection range from 300to 1150 nm. Depending on the length of the plots, we measuredthe reflectance in the winter wheat by holding the sensor approxi-mately 0.8–1.0 m above the canopy and walking at constant speedalong the plots. The sensor path was parallel to the sowing rowsand the sensing was performed before biomass sampling in all thewheat plots.

2.3. Biomass sampling

The above-ground biomass was destructively sampled byrandomly cutting five 1 m consecutive rows in each plot orfarmer’s field within the scanned areas immediately after thereflectance measurements. All of the plant samples were ovendried at 70 ◦C to a constant weight and then weighed andground for subsequent chemical analysis. A subsample was takenfrom the ground samples for canopy N content determina-tion.

2.4. Data analysis

All data, consisting of 878 observations from all experiments,were pooled in a calculation spreadsheet. The dataset was ran-domly separated into two databases: 75% for the calibration setand 25% for the validation set. To address the influences of the“dilution effect”, growth stage, cultivar, site and year on the per-formances of spectral indices and PLSR method in deriving thecanopy N content of winter wheat, we organised the datasets into 9dataset formations with different cultivars, sites and years, in addi-tion to organising data combinations into calibration and validationdatasets.

To identify the best performing algorithms and indices, twotypes of spectral indices (Table 1) were selected and comparedbased on their relationships with the canopy N content in the fieldmeasurements using the calibration datasets. Then, the best per-forming relationships were validated using the validation datasets.First, the most widely used spectral indices, the RVI and NDVI,proposed by Jordan (1969) and Rouse et al. (1974) that noware regarded as kind of a benchmark for researchers develop-ing new spectral indices. Thus, one type of indices are RVI- andNDVI-like indices based on ratio and normalised algorithms. Weselected the commonly used algorithms of the two-band combi-nation ratio, such as NDVI-like indices (NDVI, NDRE, MTCI, R-M,G-M CARI, TCARI, MCARI, TCARI/OSAVI and CCCI) and RVI-likeindices (RVI, NIR/NIR, VIopt2 and ZTM) (Table 1). For this algo-rithm, we also tested all possible two-band combinations from 300through 1150 nm to relate these combinations to the canopy N con-tent and to identify the optimised band combinations. Secondly,the chlorophyll absorption area-based indices, including the tri-angle vegetation index (TVI), MTVI1, MTVI2 and NAOC (Table 1),were tested. TVI is calculated as the area of the triangle definedby the green peak (550 nm), the chlorophyll absorption minimum(670 nm), and the NIR shoulder (750 nm) in the spectral range.Haboudane et al. (2004) replaced 750 nm by the 800 nm wave-length and incorporated a soil adjustment factor, and then theMTVI1 and MTVI2 were proposed. Similarly, Delegido et al. (2010) Ta

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Fig. 4. Contour diagrams showing the coefficient of determination (R2) for the relationships between the canopy N content and the narrow band NDSI calculated from allpossible two-band combinations in the range of 300–1150 nm with 9 data formations. The letters a, b, c, d, e, f, g, h and i, indicate different data set formations: (a) Chinesecultivar, (b) German cultivar, (c) before flowering, (d) after flowering, (e) site for Quzhou, (f) site for Dürnast 2009, (g) 2010, (h) 2011, (i) all data combinations.

developed NAOC based on the chlorophyll absorption area. Thealgorithms of the optimising band combinations for the spec-tral indices and the regression analyses were created using aself-developed computer program of MATLAB 7.0 software (TheMathWorks, Inc., Natick, MA).

PLSR is a method that specifies a linear relationship betweena set of independent and response variables. In this study, PLSRwas used to model the correlation between canopy reflectancespectra (predictor variables) and canopy N content (response vari-able). The PLSR modelling was performed using software developedby Viscarra Rossel (2008). All calibration spectral data used forbuilding the PLSR models were corrected for light scattering usingStandard Normal Variate Transformation (SNV) techniques. Beforeanalysis, we deleted the noise bands of less than 350 nm and morethan 1050 nm, and then used a second order Savitzky–Golay filterto smooth spectra and the spectral data sets were further centred orstandardised (mean of zero and standard deviation of one) to maketheir distribution fairly symmetrical (Wold et al., 2001; ViscarraRossel, 2008).

The performance of the model was estimated by compar-ing the differences in prediction abilities using the coefficientof determination (R2), the root mean square error of cross-validation/prediction (RMSECV/RMSEP) and relative error (RE, %).The higher the R2 and the lower the RMSECV/RMSEP and RE, thehigher the precision and accuracy of the model to predict thecanopy N content.

3. Results

3.1. Variation in canopy N content

The seasonal variation of the investigated canopy N contentwas influenced by the phenological development of winter wheat.As illustrated in Fig. 3, the average canopy N content of Germancultivars decreased from 3.9% at shooting stage and 2.8% at thebooting stage to 1.6% after flowering. For Chinese cultivars, theaverage canopy N content declined from 3.3% to 1.7%. These resultsshow that the canopy N content of German cultivars was gener-ally higher in the shooting stage compared to that of the Chinesecultivars, while no obvious difference was observed after flower-ing. However, compared with Chinese cultivars, the response ofGerman cultivars to N fertiliser was more sensitive. The varia-tion in canopy N content was greater at the given above-groundbiomass (Fig. 3). The results show that the canopy N content andabove-ground biomass or leaf area index (LAI) should be remotelyestimated separately.

3.2. Evaluation of optimised spectral indices

To evaluate the stability of spectral indices in deriving canopy Ncontent, we established the relationships between representativelypublished spectral indices and canopy N content with 9 dataset for-mations using calibration datasets. As illustrated in Table 2, most



Fig. 5. Contour diagrams showing the coefficient of determination (R2) for the relationships between the canopy N content and the narrow band NAOC calculated from allpossible two-band combinations in the range of 600–800 nm with 9 data formations. The letters a, b, c, d, e, f, g, h and i, indicate different data set formations: (a) Chinesecultivar, (b) German cultivar, (c) before flowering, (d) after flowering, (e) site for Quzhou, (f) site for Dürnast 2009, (g) 2010, (h) 2011, (i) all data combinations.

spectral indices had only weak relationships with canopy N con-tent. With the exception of the optimised NDSI and NAOC, none ofthe spectral indices showed a consistent performance in estimat-ing the canopy N content across 9 calibration dataset formations.Spectral indices that are composed of the red edge and the shoulderof NIR bands were found to be more competent predictors than redlight based indices after the heading stage. In addition, all spectralindices showed a poor predictive ability for the calibration datasetsduring the period before flowering. This may be due to the influenceof variation of the above-ground biomass and canopy structure ofwinter wheat.

Optimum bands significantly increase the predictive power ofspectral indices. Compared with spectral indices with fixed bandcombinations, optimised ratio-based NDSI and area-based NAOChave the highest R2. However, the band combinations for opti-mising NDSI and NAOC varied among the 9 calibration datasets(Figs. 4 and 5). For NDSI, the best performing bands have a greatervariation than do those of NAOC (Table 3). To further check therobustness of the NDSI and NAOC, nine corresponding validationdatasets were used to validate the best performing relationshipbetween NDSI, NAOC and canopy N content. The results indicatethat with the exception of a low R2 for the dataset corresponding

Table 3Validation results for the relationships established using the best performing spectral vegetation indices with validation datasets.

Data formations n Range (N%) Validation for NDSI Validation for NAOC

�1/�2 R2 RMSEP (N%) RE (%) �1/�2 R2 RMSEP (N%) RE (%)

Chinese cultivar 120 0.57–4.35 664/680 0.54 0.64 26.4 666/676 0.48 0.67 27.8German cultivar 98 1.02–5.09 380/408 0.56 0.69 22.6 640/682 0.43 0.78 25.4Before flowering 118 1.23–5.09 390/398 0.28 0.71 21.5 642/684 0.26 0.72 21.7After flowering 100 0.57–4.14 746/794 0.52 0.43 24.9 744/766 0.56 0.43 24.4Quzhou, NCP 133 0.57–4.93 664/676 0.55 0.67 24.7 660/676 0.54 0.68 24.9Dürnast, TUM, 2009 85 1.02–5.09 978/1098 0.54 0.75 27.7 642/684 0.45 0.82 30.32010 86 1.17–4.93 662/674 0.61 0.61 20.7 656/674 0.62 0.61 20.62011 47 0.57–3.87 302/694 0.63 0.56 24.2 664/678 0.54 0.62 27.1All data 218 0.57–5.09 662/682 0.41 0.80 29.4 648/682 0.40 0.80 29.6



Table 4Calibration and validation statistics of PLSR models on the entire measuring spectra (300–1150 nm) for determination of canopy N content in winter wheat applying SNVscatter corrections.

Data formations Calibration datasets Validation datasets

n Range (N%) PCs R2 RMSECV (N%) RE (%) n Range (N%) R2 RMSEP (N%) RE (%)

Chinese cultivar 386 0.68–4.84 12 0.81 0.38 16.1 120 0.57–4.35 0.86 0.35 14.4German cultivar 274 0.75–5.55 13 0.82 0.44 14.7 98 1.02–5.09 0.82 0.44 14.3Before flowering 395 1.05–5.55 13 0.75 0.43 13.4 118 1.23–5.09 0.79 0.38 11.5After flowering 265 0.68–3.43 12 0.75 0.27 15.0 100 0.57–4.14 0.81 0.27 14.7Quzhou, NCP 405 0.68–5.55 10 0.86 0.37 14.4 133 0.57–4.93 0.88 0.35 12.7Dürnast, TUM, 2009 255 0.75–5.30 8 0.86 0.39 14.2 85 1.02–5.09 0.86 0.41 15.12010 240 1.05–5.55 7 0.87 0.37 13.6 86 1.17–4.93 0.90 0.31 10.52011 165 0.68–4.17 9 0.85 0.33 13.9 47 0.57–3.87 0.90 0.29 12.7All data 660 0.68–5.55 13 0.81 0.44 16.7 218 0.57–5.09 0.84 0.42 15.4

PCs, Number of latent variables.

to the periods before flowering, the performance of the spectralindices in the other 8 datasets is acceptable under field conditions.Cultivar, site and year greatly affected the performance of NDSI andNAOC and their band combinations (Table 3).

3.3. Evaluation of PLSR method

Through the selection of 2–3 sensitive bands incorporating dif-ferent formula, the method of spectral indices was widely used to

derive the agronomic parameters of interest. PLSR searches the sen-sitive information from whole continuous spectra and then usesthe leave-one-out-cross-validation procedure to calculate the cal-ibration PLSR model. Application of the PLSR method to the ninecalibration data formations produced nine calibration models; thedescriptive statistics for the model performance parameters arepresented in Table 4. Weighing the RMSECV, Akaike informationcriterion (AIC) values and the performance of the PLSR calibrationmodel, we determined the optimal number of latent variables used

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Fig. 6. The variation of root mean square error (RMSE) and Akaike information criterion (AIC) in predictive models with the loading of number of latent variables for 9calibration datasets. (a) Chinese cultivar, (b) German cultivar, (c) before flowering (d) after flowering, (e) site for Quzhou, (f) site for Dürnast 2009, (g) 2010, (h) 2011, (i) alldata combinations.



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y = 0.87x + 0.34R2 = 0.86

y = 0.86x + 0.41R2 = 0.82 y = 0.76x + 0.76

R2 = 0.79

y = 0.80x + 0.41R2 = 0.81

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Fig. 7. Relationship between the predicted and observed canopy N content for the validation datasets. (a) Chinese cultivar, (b) German cultivar, (c) before flowering (d) afterflowering, (e) site for Quzhou, (f) site for Dürnast 2009, (g) 2010, (h) 2011, (i) all data combinations.

for canopy N content estimation (Fig. 6). A good calibration modelcould be obtained using 13 potential variables from all data combi-nations with an R2 of 0.81; a RMSECV of 0.44% N, and a RE of 16.7%.Across all calibration data set formations, the R2 ranged from 0.75before flowering to 0.87 in the 2010 dataset, the RMSECV variedbetween 0.27% N and 0.44% N and the RE, % varied from 13.4% to16.7%. Compared with the method of spectral indices, PLSR greatlyincreased the precision and accuracy of prediction (Tables 3 and 4).

To further test the performance of the developed PLSR model,the corresponding validation datasets were used to calculate thecanopy N content of winter wheat in different data formations. TheR2 in the validation sets are higher than the R2 in the calibrationsets, whereas the RMSECV and RE, % are somewhat lower com-pared to the statistical parameters of the calibration (Table 4 andFig. 7).

4. Discussion

Remote estimation of the canopy N content of winter wheat,rice, cotton and grass have been comprehensively discussed by

many studies (Tarpley et al., 2000; Gislum et al., 2004; Nguyen andLee, 2006; Fava et al., 2009; Stroppiana et al., 2009; Wang et al.,2012). Most of the studies addressed in these papers are basedon leaf-level N concentrations at several growth stages. Further-more, these experiments were conducted in the same ecologicalregion under controlled conditions. The results found in these stud-ies showed that the spectral parameters used were closely relatedto the canopy N content of plants. In contrast, our experiments wereconducted over an extensive period covering the entire growthperiod, among different cultivars and years, and in contrasting eco-logical and climatic sites, characterised by a cool and wet season insouth-eastern Germany and a dry and hot season in the North ChinaPlain. The results of the present study revealed that the spectralindices that were reported in the literature to have performed welldid not appear to work, indicating that the “dilution effect”, growthstage, cultivar, year and ecological conditions greatly influence therelationship between the parameters and the canopy N contentof winter wheat. Compared with the spectral index method, thePLSR has great potential for effectively deriving canopy N contentof winter wheat.



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Fig. 8. Validation of the model using contrasting datasets. (a) Using a validation dataset of German cultivar to validate the model established using a calibration dataset ofChinese cultivar, (b) using a validation dataset after flowering to validate the model established using a calibration dataset of the period before flowering, (c) using a validationdataset of Dürnast to validate the model established using a calibration dataset of Quzhou, (d) using a validation dataset of 2011 to validate the model established using acalibration dataset of 2010.

Most of the published spectral indices performed poorly atderiving the canopy N content in this study. For the “before flow-ering” dataset, none of the spectral indices with fixed wavebandswere positively related to the canopy N content of winter wheat.This was most likely due to the “dilution effect” mentioned byJustes et al. (1994). The rate of the above-ground biomass pro-duction exceeds the rate of N uptake by plants before floweringwhen the amount of biomass dominates the canopy reflectance. Incontrast, the leaf and stem biomass is no longer increased and the“dilution effect” is over after flowering when plant N dominatesthe canopy reflectance. Thus, the canopy N content is relativelyeasily evaluated using spectral indices during this period, particu-larly for red edge based spectral indices that react more sensitivelyto plant N than red light based spectral indices (Table 2). Hansenand Schjoerring (2003), Li et al. (2010) and Stroppiana et al. (2009)used the algorithm of two band combinations to extract optimumNDVI-like spectral indices in winter wheat and rice, respectively,which significantly improved the predictive power compared tothe selected published spectral indices. Similarly, the results of thecurrent study show that the optimised bands algorithm greatlyincreased the performance of NDSI and NAOC at deriving thecanopy N content of winter wheat compared to all other selectedpublished spectral indices. However, the band combinations for

the optimum spectral indices varied with the variation of calibra-tion datasets (Table 3). Compared with the variation observed inratio-based algorithms, the variation observed using chlorophyllabsorption area-based algorithms was relatively small, and theoptimum bands mainly focused on the red light area (Figs. 4 and 5).The optimum spectral indices derived from the literature (Hansenand Schjoerring, 2003; Li et al., 2010) are only significantly relatedto canopy N content in datasets 1–3 of 9. The issues addressed abovemay suggest that the relationship between spectral indices and thecanopy N content of winter wheat is specific to the cultivar, growthstage, site and year. Overall, it is difficult to develop a unified spec-tral index to derive the canopy N content and the magnitude ofthe relationship was never observed to be sufficient to develop aappropriate methodology using the method of spectral indices.

Many spectral indices and the corresponding formulas that arebased on plant physiology have been developed to evaluate theN status-related parameters of crops. However, many bands thatare sensitive to canopy structure rather than plant photosyntheticpigment have been positively related to the canopy N content(Ollinger, 2011). This may be the reason why the spectral indicesinvolving 2–3 wavebands were difficult to use to derive the canopyN content. The PLSR would be a better choice because the PLSRprovides a regression model in which the entire spectral dataset is



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Fig. 9. Relationship between the estimated and observed canopy N content for the data from validation datasets, using the established unified model with all calibrationdatasets of all data combinations at different (a) cultivars, (b) growth stages, (c) sites and (d) years.

taken into account in a weighted viewpoint. The loading weightsof the main latent variables show that the reflectance at variouswavebands was loaded in our study. The high loading values werefocused on the wavebands of blue, green, red, and red edge, at theshoulder of the NIR and at approximately 1000 nm in the threemain latent variables of all nine PLSR models predicting canopyN content (Fig. 10). This further confirms that the method of PLSRshould include more sensitive wavebands compared to the methodof spectral indices. Although limited multivariate calibration meth-ods were used to remotely estimate the aerial N indicators in theagricultural fields, the PLSR models performed better than the bestof the selected spectral indices in 9 calibration datasets based on lin-ear curve fitting (Tables 2–4). The average R2 for the PLSR increasedby 76.8%, with a range of 26.1–200.0%, compared to the R2 forthe relationships between the best performing spectral indices andthe canopy N content in the calibration datasets. Similarly, in thevalidation datasets, the method of PLSR enhanced the average R2

by 75.5% and decreased the RMSE by 89.6% compared with themethod based on spectral indices, indicating that PLSR is indeed apotentially robust method to derive the canopy N content of winterwheat. In agreement with this study, the R2 for the best performingspectral indices related to the canopy N content presented in theliterature was generally lower than 0.65 across the growth stages,sites and years (Hansen and Schjoerring, 2003; Fava et al., 2009;

Stroppiana et al., 2009; Li et al., 2010; Rodriguez-Moreno and Llera-Cid, 2011). The findings of Wang et al. (2012) are an exception.These results suggest that the R2 for the optimum three-band spec-tral index is related to the canopy N content of winter wheat andachieved a value of 0.86 across the growth stages, experiments andyears. The explanation for the result may be that the authors relatedthe spectral indices to the leaf N concentration rather than to thewhole plant canopy concentration; in addition, their experimentswere conducted in similar ecological regions and under relativelycontrolled conditions.

The robustness of the PLSR models strongly depends on whetherspanning those spectral variations as much as possible in calibra-tion datasets used. Prediction errors can be observed if insufficientspectral variation information of calibration datasets is used to cal-ibrate the model. When contrasting datasets with different growthstages, cultivars, sites and years were used to calibrate and validateeach other, the accuracy and precision of the prediction stronglydecreased and the predictive values deviated substantially fromthe 1:1 line (Fig. 8). In contrast, if the dataset of “all data combina-tions” was used to calibrate the model and/or if any one of the ninevalidation datasets was used to validate the model, the predictiveperformance was significantly increased and the prediction valuesalmost coincided with the 1:1 line (Fig. 9). Thus, a global modelfor N estimation in winter wheat for all conditions is probably



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Fig. 10. Comparison of the loading weights for each PLSR model depending on themain latent variables.

developed when the spectra and the canopy N content samplingfrom other cultivars, ecological areas and years are involved.

5. Conclusions

Although the bands were optimised using band optimum algo-rithms, the method of spectral indices did not deliver satisfactoryresults for the derivation of the canopy N content of winter wheatgrown under contrasting field conditions; this is most likely due tothe influences of the “dilution effect”, cultivar, site and year. ThePLSR is a potentially useful method to evaluate the canopy N con-tent in the field in a timely manner compared with the methodof spectral indices. Particularly, when more spectral and field

measurements in ecological regions and years are included, arobust model can be proposed and may possibly extend the modelto on-line estimating N status for winter wheat globally.

Acknowledgements

This research was financially supported by the German Fed-eral Ministry of Education and Research (BMBF) (Project No. FKZ0330800A) and the International Bureau of the German FederalMinistry of Education and Research (Project No: CHN11/052).

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author's personal copy...of winter wheat. the study was conducted in cool and wet southeastern...

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