Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression

Europ. J. Agronomy 52 (2014) 198–209

Contents lists available at ScienceDirect

European Journal of Agronomy journal homepage: www.elsevier.com/locate/eja

Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression Fei Li a,b , Bodo Mistele b , Yuncai Hu b , Xinping Chen c , Urs Schmidhalter b,∗ a

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

a r t i c l e

i n f o

Article history: Received 22 November 2012 Received in revised form 27 August 2013 Accepted 4 September 2013 Keywords: Winter wheat Canopy N content PLSR Spectral indices

a b s t r a c t Many spectral indices have been proposed to derive plant nitrogen (N) nutrient indicators based on different algorithms. However, the relationships between selected spectral indices and the canopy N content of crops are often inconsistent. The goals of this study were to test the performance of spectral indices and partial least square regression (PLSR) and to compare their use for predicting canopy N content of winter wheat. The study was conducted in cool and wet southeastern Germany and the hot and dry North China Plain for three winter wheat growing seasons. The canopy N content of winter wheat varied from 0.54% to 5.55% in German cultivars and from 0.57% to 4.84% in Chinese cultivars across growth stages and years. The best performing spectral indices and their band combinations varied across growth stages, cultivars, sites and years. Compared with the best performing spectral indices, the average value of 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 content of winter wheat across growth stages, cultivars, sites and years under field conditions when a broad set of canopy reflectance data are included in the calibration models. PLSR will be useful for real-time estimation of N status of winter wheat in the fields and for guiding farmers in the accurate application of their N fertilisation strategies. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Canopy N content is one of the important N nutrient diagnosis indicators of plants, governing canopy carbon assimilation; it is often positively associated with leaf and canopy chlorophyll content 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 content of crops on a regional scale is important not only to obtain an overview of the N distribution, but also to gain knowledge of canopy energy exchange in agro-ecosystems. Therefore, up-scaling beyond discontinuous field-based small sampling points is necessary for regional N budget estimation as well as for carbon cyclings evaluations (Ollinger et al., 2008). One effective and timely approach used is to remotely estimate canopy N content using calibrated relationships between crop 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). 1161-0301/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eja.2013.09.006

analysis data (Mistele and Schmidhalter, 2008). As plant N concentration is linked to the amount of chlorophyll, many studies have focused on estimating crop leaf chlorophyll concentration, which give an indirect assessment of canopy- or leaf-based N status of crops (Haboudane et al., 2008). The most common method of deriving canopy N content using remote sensing is to derive spectral indices by incorporating two or more characteristic wavebands into a simple ratio or into a more complicated formula based on algorithms and N-related plant physiological significance (Pinter et al., 2003; Hatfield et al., 2008; Ollinger, 2011). However, unlike aboveground biomass production and canopy N uptake, canopy N content decreases with the progression of growth stages and produce “dilution effects” as described by Lemaire et al. (2008). For example, the N content of plants is highest at early growth stages and decreases continually up to the stage of senescence because the N uptake per unit of above-ground biomass accumulated decreases as the leaf area per unit crop mass decreases. In the vegetative growth period in particular, an increase in the rate of biomass production compared to that of canopy N uptake results in a rapid decrease in canopy N content. The variation in above-ground biomass and canopy structure dominates the canopy spectral reflectance. Thus, the “dilution effect” and the variation in canopy structure probably

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 the algorithm of all possible two band combinations at 400–1000 nm, Hansen and Schjoerring (2003) found that the combination of R440 with R573 was the best performing NDVI-like index for deriving the canopy N content of winter wheat. Li et al. (2010), however, suggested that (R410 − R365)/(R410 + R365) could better estimate canopy N content of winter wheat compared with other published spectral indices. Similarly, there were inconsistencies observed in the selection of the sensitive bands as reported by Zhu et al. (2007), Stroppiana et al. (2009) and Tian et al. (2011) for rice. These inconsistencies may result from an indirect estimation of plant N concentraton because nitrogen does not directly absorb radiation in the VIS-NIR region. Delegido et al. (2010, 2011) proposed an areabased index, the Normalised Area Over Reflectance Curve (NAOC), that successfully derived the canopy chlorophyll content of different crops under heterogeneous conditions. However, there is little knowledge available relating to the derivation of canopy N content based on mass using the NAOC. Spectral indices with simple ratios or combined formulas focus mostly on 2–3 bands only, which make it difficult to construct a unified index to remotely estimate canopy N content across different growth stages, cultivars, sites and years due to the influences of the “dilution effect” and the variation in the canopy structure of the crops. Optimum multiple narrow band reflectance using step linear regression analysis has been commonly used to identify the characteristic bands related to the crop biophysical and biochemical parameters of interest (Thenkabail et al., 2000; Serrano et al., 2002). However, this method suffers from “over fitting”, because the number of spectral bands exceeds the number of experimental samples (Thenkabail et al., 2000; Nguyen and Lee, 2006). In contrast, although most of the waveband reflectances have been used to estimate plant biochemical concentrations, partial least square regression (PLSR) overcomes the problems of collinearity and “over-fitting” compared to step linear regression analysis if optimally choosing a suitable number of principal components and deleting the noise bands (Herrmann et al., 2011). However, the small number of sampling may limit the number of latent variables in the PLSR model and reduce the calibration accuracy (Van Der Heijden et al., 2007). The PLSR has been widely used to derive the chemical compositions in reagents and dry samples (Wold et al., 2001; Gislum et al., 2004) and to assess N related indicators of crops in homogeneous areas (Nguyen and Lee, 2006; Soderstrom et al., 2010). Limited research has been conducted to estimate canopy N content in heterogeneous fields with different growth stages, cultivars, sites and years under contrasting climatic conditions. Furthermore, there is limited knowledge on how these factors affect the performance of PLSR in evaluating mass-related canopy N content of winter wheat. To date, many studies have been performed that attempt to derive biophysical and biochemical parameters of interest with a 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 homogeneous medium with the same ecological area under controlled conditions. Limited experiments were performed to address the effects of canopy structure or the “dilution effect” on remote evaluation of the canopy N content of winter wheat under heterogeneous field conditions Therefore, the main objectives of the current study were as follows: (1) to address how the dilution effect, growth stage, cultivar, site and year influence the relationships between spectral parameters and the canopy N content of winter wheat; and (2) to compare the performance of spectral indices and PLSR for estimating the canopy N content in winter wheat. 2. Materials and methods 2.1. Field experiments and design All experiments were conducted at the Dürnast Research Station of the Technische Universität München (TUM) in Freising, in southeast Germany, and at the experimental station of the China Agricultural University (CAU) in Quzhou County in the North China Plain during the winter wheat growing seasons of 2009 through 2011. As illustrated in Fig. 1, Freising is characterised by a typical oceanic climate with mild cloudy winters and wet, cool summers, while Quzhou County lies in the warm-temperate subhumid-continental monsoon zone and is cold in winter and dry and hot in summer. Hence, no irrigation is applied in Freising, whereas the farmers in Quzhou irrigate their winter wheat 3–4 times during the season using flood irrigation with water from wells. An experiment at Freising in 2008/2009 was performed using eight 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 and 2010/2011. One German cultivar (Tommi) and two local cultivars (Heng4399 and Kenong9204) were used in Experiment 1 with seven N rates (0, 60, 120, 180, 240, 300, 360 kg N ha−1 ) based on the residual soil mineral N previously assessed using a quick-test method (Schmidhalter, 2005). One Chinese wheat cultivar, Liangxing99, was used in Experiment 2; the five N treatments used in this experiment were the control (no N applied), 50% of the optimum (Opt) N rate, Opt rate, 150% of the Opt and conventional (Con) N rate. The Opt was based on the above-ground N requirement and the soil N supply for the two growing periods (sowing to shooting, shooting to harvest) (Chen et al., 2006). The conventional N treatment represents the local farmers’ practice, in which 150 kg N ha−1 was applied before sowing and 150 kg N ha−1 was applied as top-dressed fertiliser at the shooting stage. In addition, some farmers’ fields near the Quzhou experimental station were selected in 2009–2011; these fields were managed by the farmers.

200

F. Li et al. / Europ. J. Agronomy 52 (2014) 198–209 100

100

Chinese cultivars German cultivars

60

40

20

60 40 20 0

0

200

400

600

800

1000

1200

Quzhou Freising

80

Reflectance (%)

80

Reflectance (%)

Reflectance (%)

80

100 Before flowering After flowering

60 40 20 0

200

400

Wavelength (nm)

600

800

1000

200

1200

400

600

800

1000

1200

Wavelength (nm)

Wavelength (nm)

Fig. 2. Monthly rainfall and average temperature in (a) Freising from 1999 to 2008 and (b) Quzhou from 2007 to 2011. 6

6 Shooting stage Booting stage After flowering

5

Canopy N content (%)

Canopy N content (%)

5

4

3

2

3

2

1

1

0

4

a

0 0

5

10

15

20

b 0

25

4

8

12

16

Above-ground biomass (t)

Above-ground biomass (t)

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

Formula

References

Ratio and normalised based algorithms Normalised difference vegetation index (NDVI1) Normalised difference spectral index 1 (NDVI2) Normalised difference spectral index 2 (NDVI3) Normalised difference spectral index 2 (NDVI4) Ratio vegetation index (RVI1) Ratio vegetation index (RVI2) NIR/NIR Red edge position (REIP) Optimised vegetation index 2 (VIopt2) Zarco-Tejada & Miller (ZTM) Normalised difference red edge index (NDRE) The MERIS terrestrial chlorophyll index (MTCI) Red-edge model index (R-M) Green model index (G-M) Chlorophyll absorption in reflectance index (CARI) Transformed chlorophyll absorption in reflectance index (TCARI) Modified chlorophyll absorption in reflectance index (MCARI) TCARI/OSAVI Canopy chlorophyll content index (CCCI) Normalised difference spectral index (NDSI)#

(R780 − R670 )/(R780 + R670 ) (R573 − R440 )/(R573 + R440 ) (R410 − R365 )/(R410 + R365 ) (R503 − R483 )/(R503 + R483 ) R780 /R670 R787 /R765 R780 /R740 700 + 40 × [(R670 + R780 )/2 − R700 ]/(R740 − R700 ) R760 /R730 R750 /R710 (R790 − R720 )/(R790 + R720 ) (R750 − R710 )/(R710 − R680 ) (R750 /R720 ) − 1 (R750 /R550 ) − 1 (R700 − R670 ) − 0.2 × (R700 + R550 ) 3 × [(R700 − R670 ) − 0.2 × (R700 − R550 )(R700 /R670 )] [(R700 − R670 ) − 0.2 × (R700 − R550 )](R700 /R670 )) TCARI/OSAVI (NDRE − NDREMIN )/(NDREMAX − NDREMIN ) (R1 − R2 )/(R1 + R2 ), R1 > R2

Rouse et al. (1974) Hansen and Schjoerring (2003) Li et al. (2010) Stroppiana et al. (2009) Pearson and Miller (1972) Fava et al. (2009) Mistele and Schmidhalter (2010) Guyot et al. (1988) Jasper et al. (2009) Zarco-Tejada et al. (2001) Barnes et al. (2000) Dash and Curran (2004) Gitelson et al. (2005) Gitelson et al. (2005) Kim et al. (1994) Haboudane et al. (2002) Daughtry et al. (2000) Haboudane et al. (2002) Barnes et al. (2000) This study

Chlorophyll absorption area based algorithms Triangle vegetation index (TVI) Modified triangular vegetation index 1 (MTVI1)

0.5 × [120 × (R750 − R550 ) − 200 × (R670 − R550 )] 1.2 × [1.2 × (R800 − R550 ) − 2.5 × (R670 − R550 )]

Broge and Leblanc (2000) Haboudane et al. (2004)

Modified triangular vegetation index 2 (MTVI2)



1.5×[1.2×(R800 −R550 )−2.5×(R670 −R550 )]

b

Normalised area over reflectance curve (NAOC)& #

1−

√

(2×R800 +1)2 −(6×R800 −5×

a

Haboudane et al. (2004)

R670 −0.5)

d

max (b−a)

Delegido et al. (2010)

1 and 2 stand for the wavelength in 300–1150 nm and R1 and R2 stand for the reflectance wavelength 1 and 2. The bands combination (1, 2, R1 and R2 ) was optimised 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 integration limits.

0.00 0.00 0.00 0.21 0.00 0.26 0.02 0.19 0.02 0.02 0.03 0.02 0.20 0.00 0.40 0.00 0.14 0.08 0.03 0.06 0.01 0.16 0.01 0.44 0.01 0.16 0.10 0.47 0.51 0.25 0.52 0.53 0.50 0.69 0.66 0.36 0.05 0.06 0.00 0.37 0.08 0.00 0.25 0.06 0.03 0.00 0.00 0.01 0.10 0.04 0.00 0.20 0.05 0.01 0.00 0.01 0.00 0.01 0.00 0.17 0.00 0.02 0.04 0.00 0.00 0.00 0.01 0.02 0.08 0.10 0.00 0.03 0.01 0.03 0.00 0.02 0.03 0.14 0.11 0.00 0.05 0.01 0.00 0.00 0.31 0.07 0.12 0.34 0.23 0.00 0.04 0.01 0.00 0.40 0.09 0.10 0.35 0.15 0.00 0.02 0.00 0.00 0.30 0.08 0.11 0.28 0.11 0.01 0.04 0.08 0.01 0.40 0.11 0.02 0.35 0.10 0.02 0.04 0.01 0.00 0.40 0.09 0.10 0.35 0.15 0.00 0.07 0.05 0.01 0.42 0.12 0.02 0.35 0.07 0.02 0.02 0.09 0.01 0.30 0.10 0.08 0.34 0.11 0.01 Chinese cultivar German cultivar Before flowering After flowering Quzhou, NCP Dürnast, TUM, 2009 2010 2011 All data

Values should be indicated as bold values to highlight the more significant ones.

0.06 0.09 0.00 0.42 0.11 0.00 0.28 0.04 0.05 0.09 0.10 0.00 0.47 0.14 0.00 0.36 0.04 0.06 0.19 0.23 0.03 0.08 0.21 0.23 0.38 0.04 0.16 0.04 0.00 0.00 0.51 0.06 0.08 0.32 0.19 0.00 0.11 0.07 0.08 0.16 0.09 0.25 0.10 0.22 0.07

CARI G-M R-M MTCI NDRE ZTM VIopt2 REIP NIR/ NIR RVI2 RVI1 NDVI4 NDVI3 NDVI2 NDVI1

All data, consisting of 878 observations from all experiments, were pooled in a calculation spreadsheet. The dataset was randomly separated into two databases: 75% for the calibration set and 25% for the validation set. To address the influences of the “dilution effect”, growth stage, cultivar, site and year on the performances of spectral indices and PLSR method in deriving the canopy N content of winter wheat, we organised the datasets into 9 dataset formations with different cultivars, sites and years, in addition to organising data combinations into calibration and validation datasets. To identify the best performing algorithms and indices, two types of spectral indices (Table 1) were selected and compared based on their relationships with the canopy N content in the field measurements using the calibration datasets. Then, the best performing 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 now are regarded as kind of a benchmark for researchers developing new spectral indices. Thus, one type of indices are RVI- and NDVI-like indices based on ratio and normalised algorithms. We selected the commonly used algorithms of the two-band combination ratio, such as NDVI-like indices (NDVI, NDRE, MTCI, R-M, G-M CARI, TCARI, MCARI, TCARI/OSAVI and CCCI) and RVI-like indices (RVI, NIR/NIR, VIopt2 and ZTM) (Table 1). For this algorithm, we also tested all possible two-band combinations from 300 through 1150 nm to relate these combinations to the canopy N content and to identify the optimised band combinations. Secondly, the chlorophyll absorption area-based indices, including the triangle vegetation index (TVI), MTVI1, MTVI2 and NAOC (Table 1), were tested. TVI is calculated as the area of the triangle defined by 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 wavelength and incorporated a soil adjustment factor, and then the MTVI1 and MTVI2 were proposed. Similarly, Delegido et al. (2010)

Data formations

2.4. Data analysis

Table 2 Coefficients of determination (R2 ) of the linear relationships between canopy N content and the indices selected with 9 calibration datasets.

The above-ground biomass was destructively sampled by randomly cutting five 1 m consecutive rows in each plot or farmer’s field within the scanned areas immediately after the reflectance measurements. All of the plant samples were oven dried at 70 ◦ C to a constant weight and then weighed and ground for subsequent chemical analysis. A subsample was taken from the ground samples for canopy N content determination.

TCARI

2.3. Biomass sampling

0.01 0.44 0.07 0.03 0.08 0.38 0.40 0.04 0.17

MCARI

TCARI/ OSOVI

CCCI

NDSI

TVI

MTVI1

MTVI2

Spectral reflectance data at the canopy level were collected using a passive spectrometer (tec5, Oberursel, Germany). The spectral reflectance of different cultivars, growth stages and sites is shown in Fig. 2. The measuring head of this device consists of two optics: the upper optic is used to quantify the incoming light as a reference, and the lower optic records the reflectance from the vegetation and the ground (Erdle et al., 2011; Winterhalter et al., 2011; Li et al., 2012). The sensors have a bandwidth of 3.3 nm and can measure 256 bands, with a spectral detection range from 300 to 1150 nm. Depending on the length of the plots, we measured the reflectance in the winter wheat by holding the sensor approximately 0.8–1.0 m above the canopy and walking at constant speed along the plots. The sensor path was parallel to the sowing rows and the sensing was performed before biomass sampling in all the wheat plots.

0.06 0.04 0.08 0.11 0.03 0.24 0.04 0.08 0.04

NAOC

2.2. Canopy spectral measurements

201

0.43 0.40 0.24 0.52 0.53 0.44 0.69 0.40 0.35

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

202

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

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 all possible 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) 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) all data combinations.

developed NAOC based on the chlorophyll absorption area. The algorithms of the optimising band combinations for the spectral indices and the regression analyses were created using a self-developed computer program of MATLAB 7.0 software (The MathWorks, Inc., Natick, MA). PLSR is a method that specifies a linear relationship between a set of independent and response variables. In this study, PLSR was used to model the correlation between canopy reflectance spectra (predictor variables) and canopy N content (response variable). The PLSR modelling was performed using software developed by Viscarra Rossel (2008). All calibration spectral data used for building the PLSR models were corrected for light scattering using Standard Normal Variate Transformation (SNV) techniques. Before analysis, we deleted the noise bands of less than 350 nm and more than 1050 nm, and then used a second order Savitzky–Golay filter to smooth spectra and the spectral data sets were further centred or standardised (mean of zero and standard deviation of one) to make their distribution fairly symmetrical (Wold et al., 2001; Viscarra Rossel, 2008). The performance of the model was estimated by comparing the differences in prediction abilities using the coefficient of determination (R2 ), the root mean square error of crossvalidation/prediction (RMSECV/RMSEP) and relative error (RE, %). The higher the R2 and the lower the RMSECV/RMSEP and RE, the higher the precision and accuracy of the model to predict the canopy N content.

3. Results 3.1. Variation in canopy N content The seasonal variation of the investigated canopy N content was influenced by the phenological development of winter wheat. As illustrated in Fig. 3, the average canopy N content of German cultivars decreased from 3.9% at shooting stage and 2.8% at the booting stage to 1.6% after flowering. For Chinese cultivars, the average canopy N content declined from 3.3% to 1.7%. These results show that the canopy N content of German cultivars was generally higher in the shooting stage compared to that of the Chinese cultivars, while no obvious difference was observed after flowering. However, compared with Chinese cultivars, the response of German cultivars to N fertiliser was more sensitive. The variation in canopy N content was greater at the given above-ground biomass (Fig. 3). The results show that the canopy N content and above-ground biomass or leaf area index (LAI) should be remotely estimated separately. 3.2. Evaluation of optimised spectral indices To evaluate the stability of spectral indices in deriving canopy N content, we established the relationships between representatively published spectral indices and canopy N content with 9 dataset formations using calibration datasets. As illustrated in Table 2, most

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

203

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 all possible 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) 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) all data combinations.

spectral indices had only weak relationships with canopy N content. With the exception of the optimised NDSI and NAOC, none of the spectral indices showed a consistent performance in estimating the canopy N content across 9 calibration dataset formations. Spectral indices that are composed of the red edge and the shoulder of NIR bands were found to be more competent predictors than red light based indices after the heading stage. In addition, all spectral indices showed a poor predictive ability for the calibration datasets during the period before flowering. This may be due to the influence of variation of the above-ground biomass and canopy structure of winter wheat.

Optimum bands significantly increase the predictive power of spectral indices. Compared with spectral indices with fixed band combinations, optimised ratio-based NDSI and area-based NAOC have the highest R2 . However, the band combinations for optimising NDSI and NAOC varied among the 9 calibration datasets (Figs. 4 and 5). For NDSI, the best performing bands have a greater variation than do those of NAOC (Table 3). To further check the robustness of the NDSI and NAOC, nine corresponding validation datasets were used to validate the best performing relationship between NDSI, NAOC and canopy N content. The results indicate that with the exception of a low R2 for the dataset corresponding

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

Chinese cultivar German cultivar Before flowering After flowering Quzhou, NCP Dürnast, TUM, 2009 2010 2011 All data

n

120 98 118 100 133 85 86 47 218

Range (N%)

0.57–4.35 1.02–5.09 1.23–5.09 0.57–4.14 0.57–4.93 1.02–5.09 1.17–4.93 0.57–3.87 0.57–5.09

Validation for NDSI

Validation for NAOC

1/2

R2

RMSEP (N%)

RE (%)

1/2

R2

RMSEP (N%)

RE (%)

664/680 380/408 390/398 746/794 664/676 978/1098 662/674 302/694 662/682

0.54 0.56 0.28 0.52 0.55 0.54 0.61 0.63 0.41

0.64 0.69 0.71 0.43 0.67 0.75 0.61 0.56 0.80

26.4 22.6 21.5 24.9 24.7 27.7 20.7 24.2 29.4

666/676 640/682 642/684 744/766 660/676 642/684 656/674 664/678 648/682

0.48 0.43 0.26 0.56 0.54 0.45 0.62 0.54 0.40

0.67 0.78 0.72 0.43 0.68 0.82 0.61 0.62 0.80

27.8 25.4 21.7 24.4 24.9 30.3 20.6 27.1 29.6

204

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

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

Calibration datasets n

Chinese cultivar German cultivar Before flowering After flowering Quzhou, NCP Dürnast, TUM, 2009 2010 2011 All data

386 274 395 265 405 255 240 165 660

Validation datasets

Range (N%)

PCs

R2

RMSECV (N%)

RE (%)

n

0.68–4.84 0.75–5.55 1.05–5.55 0.68–3.43 0.68–5.55 0.75–5.30 1.05–5.55 0.68–4.17 0.68–5.55

12 13 13 12 10 8 7 9 13

0.81 0.82 0.75 0.75 0.86 0.86 0.87 0.85 0.81

0.38 0.44 0.43 0.27 0.37 0.39 0.37 0.33 0.44

16.1 14.7 13.4 15.0 14.4 14.2 13.6 13.9 16.7

120 98 118 100 133 85 86 47 218

Range (N%) 0.57–4.35 1.02–5.09 1.23–5.09 0.57–4.14 0.57–4.93 1.02–5.09 1.17–4.93 0.57–3.87 0.57–5.09

R2

RMSEP (N%)

RE (%)

0.86 0.82 0.79 0.81 0.88 0.86 0.90 0.90 0.84

0.35 0.44 0.38 0.27 0.35 0.41 0.31 0.29 0.42

14.4 14.3 11.5 14.7 12.7 15.1 10.5 12.7 15.4

PCs, Number of latent variables.

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

derive the agronomic parameters of interest. PLSR searches the sensitive information from whole continuous spectra and then uses the leave-one-out-cross-validation procedure to calculate the calibration PLSR model. Application of the PLSR method to the nine calibration data formations produced nine calibration models; the descriptive statistics for the model performance parameters are presented in Table 4. Weighing the RMSECV, Akaike information criterion (AIC) values and the performance of the PLSR calibration model, we determined the optimal number of latent variables used

3.3. Evaluation of PLSR method Through the selection of 2–3 sensitive bands incorporating different formula, the method of spectral indices was widely used to 0.9 RMSE AIC

0.9

-100

0.4 0.3

-300

0.5

-350

0.4

-400

0.3

0.44

-150

10

15

20

25

-220

0.9

0.38

-240

0.8

0.36

-260

RMSE

AIC

RMSE

0.34 0.30

-300

0.5

-320

0.4

-340

0.3

0.28 0.26 0.24 20

25

0.6 -150 0.5

RMSE

AIC

-100

20

25

-200

0.4 0.3

-250 30

AIC

30 -40 -60 -80 -100 -120

0.6

-140 -160

0.5

-180 -200 -220

-400 5

10

15

20

25

0.3

-20

1.0

-40

0.9

-60

-100 0.5

5

10

15

20

25

30 0

i -100

0.8

-80

0.6

-240 0

30

-200

0.7 -300 0.6

-120

0.4

Number of latent variables

15

0.4

0.7

25

10

0.9

-300

0.8

0.7

RMSE

-200

-50

20

-350 5

0.7

h

0.8

15

-300

-100

0.9

0

g

10

0.4

0.8

0

30

0.9

5

-250

f

0.6

-280

0

0.5

0 0

0.7

0.32

15

-200

e

0.40

10

0.6

30

1.0

-200

5

-150

0.3 5

d

0.42

0

0.7

-200 0

30

AIC

0.6

AIC

25

-100

RMSE

20

0.7

RMSE

15

-100

AIC

0.5

10

0.8

-140

0.3 0.2 0

5

10

41 15

20

25

Number of latent variables

30

-400

0.5

-160

0.4

-180

0.3

AIC

-250

RMSE

0.6

AIC

-200

RMSE

0.8

-150

5

c -50

0.7

0

-50

0.9

0

b

AIC

0.8

RMSE

1.0

-50

a

-500 -600 0

5

10

15

20

25

30

Number of latent variables

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 9 calibration 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) all data combinations.

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

205

6

Predicted value (%)

c

b

a 5 4 3 2

y = 0.86x + 0.41 R2 = 0.82

y = 0.87x + 0.34 R2 = 0.86

1

y = 0.76x + 0.76 R2 = 0.79

0

Predicted value (%)

d

e

f

5 4 3 2

y = 0.89x + 0.30 R2 = 0.88

y = 0.80x + 0.41 R2 = 0.81

1

y = 0.86x + 0.42 R2 = 0.86

Predicted value (%)

0

h

g

5

i

4 3 2

y = 0.93x + 0.16 R2 = 0.90

1

y = 0.89x + 0.34 R2 = 0.90

y = 0.83x + 0.49 R2 = 0.84

0 0

1

2

3

4

Observed value (%)

5

0

1

2

3

4

Observed value (%)

5

0

1

2

3

4

5

6

Observed value (%)

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) after flowering, (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 model could be obtained using 13 potential variables from all data combinations 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 0.75 before flowering to 0.87 in the 2010 dataset, the RMSECV varied between 0.27% N and 0.44% N and the RE, % varied from 13.4% to 16.7%. Compared with the method of spectral indices, PLSR greatly increased 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 the canopy N content of winter wheat in different data formations. The R2 in the validation sets are higher than the R2 in the calibration sets, whereas the RMSECV and RE, % are somewhat lower compared to the statistical parameters of the calibration (Table 4 and Fig. 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 and Lee, 2006; Fava et al., 2009; Stroppiana et al., 2009; Wang et al., 2012). Most of the studies addressed in these papers are based on leaf-level N concentrations at several growth stages. Furthermore, these experiments were conducted in the same ecological region under controlled conditions. The results found in these studies showed that the spectral parameters used were closely related to the canopy N content of plants. In contrast, our experiments were conducted over an extensive period covering the entire growth period, among different cultivars and years, and in contrasting ecological and climatic sites, characterised by a cool and wet season in south-eastern Germany and a dry and hot season in the North China Plain. The results of the present study revealed that the spectral indices that were reported in the literature to have performed well did not appear to work, indicating that the “dilution effect”, growth stage, cultivar, year and ecological conditions greatly influence the relationship between the parameters and the canopy N content of winter wheat. Compared with the spectral index method, the PLSR has great potential for effectively deriving canopy N content of winter wheat.

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

6

6

5

5

Predicted value (%)

Predicted value (%)

206

4

3

2

1

4

3

2

1

b

a 0

0 0

1

2

3

4

5

6

0

2

3

4

5

6

Observed value (%)

8

8

6

6

Predicted value (%)

Predicted value (%)

Observed value (%)

1

4

2

4

2

c

d

0

0 0

2

4

6

8

Observed value (%)

0

2

4

6

8

Observed value (%)

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 of Chinese 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 validation dataset 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 a calibration dataset of 2010.

Most of the published spectral indices performed poorly at deriving the canopy N content in this study. For the “before flowering” dataset, none of the spectral indices with fixed wavebands were positively related to the canopy N content of winter wheat. This was most likely due to the “dilution effect” mentioned by Justes et al. (1994). The rate of the above-ground biomass production exceeds the rate of N uptake by plants before flowering when the amount of biomass dominates the canopy reflectance. In contrast, the leaf and stem biomass is no longer increased and the “dilution effect” is over after flowering when plant N dominates the canopy reflectance. Thus, the canopy N content is relatively easily evaluated using spectral indices during this period, particularly for red edge based spectral indices that react more sensitively to plant N than red light based spectral indices (Table 2). Hansen and Schjoerring (2003), Li et al. (2010) and Stroppiana et al. (2009) used the algorithm of two band combinations to extract optimum NDVI-like spectral indices in winter wheat and rice, respectively, which significantly improved the predictive power compared to the selected published spectral indices. Similarly, the results of the current study show that the optimised bands algorithm greatly increased the performance of NDSI and NAOC at deriving the canopy N content of winter wheat compared to all other selected published spectral indices. However, the band combinations for

the optimum spectral indices varied with the variation of calibration datasets (Table 3). Compared with the variation observed in ratio-based algorithms, the variation observed using chlorophyll absorption area-based algorithms was relatively small, and the optimum bands mainly focused on the red light area (Figs. 4 and 5). The optimum spectral indices derived from the literature (Hansen and Schjoerring, 2003; Li et al., 2010) are only significantly related to canopy N content in datasets 1–3 of 9. The issues addressed above may suggest that the relationship between spectral indices and the canopy N content of winter wheat is specific to the cultivar, growth stage, site and year. Overall, it is difficult to develop a unified spectral index to derive the canopy N content and the magnitude of the relationship was never observed to be sufficient to develop a appropriate methodology using the method of spectral indices. Many spectral indices and the corresponding formulas that are based on plant physiology have been developed to evaluate the N status-related parameters of crops. However, many bands that are sensitive to canopy structure rather than plant photosynthetic pigment have been positively related to the canopy N content (Ollinger, 2011). This may be the reason why the spectral indices involving 2–3 wavebands were difficult to use to derive the canopy N content. The PLSR would be a better choice because the PLSR provides a regression model in which the entire spectral dataset is

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

6

6 Chinese cultivar German cultivar

Before flowering After flowering

5

Predicted value (%)

5

Predicted value (%)

207

4

3

2

1

4

3

2

1

b

a 0

0 0

1

2

3

4

5

6

0

1

Observed value (%)

3

4

5

6

Observed value (%)

6

6 Quzhou Durnast

2009 2010 2011

5

Predicted value (%)

5

Predicted value (%)

2

4

3

2

1

4

3

2

1

c

0

d

0 0

1

2

3

4

5

6

Observed value (%)

0

1

2

3

4

5

6

Observed value (%)

Fig. 9. Relationship between the estimated and observed canopy N content for the data from validation datasets, using the established unified model with all calibration datasets 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 weights of the main latent variables show that the reflectance at various wavebands was loaded in our study. The high loading values were focused on the wavebands of blue, green, red, and red edge, at the shoulder of the NIR and at approximately 1000 nm in the three main latent variables of all nine PLSR models predicting canopy N content (Fig. 10). This further confirms that the method of PLSR should include more sensitive wavebands compared to the method of spectral indices. Although limited multivariate calibration methods were used to remotely estimate the aerial N indicators in the agricultural fields, the PLSR models performed better than the best of the selected spectral indices in 9 calibration datasets based on linear curve fitting (Tables 2–4). The average R2 for the PLSR increased by 76.8%, with a range of 26.1–200.0%, compared to the R2 for the relationships between the best performing spectral indices and the canopy N content in the calibration datasets. Similarly, in the validation datasets, the method of PLSR enhanced the average R2 by 75.5% and decreased the RMSE by 89.6% compared with the method based on spectral indices, indicating that PLSR is indeed a potentially robust method to derive the canopy N content of winter wheat. In agreement with this study, the R2 for the best performing spectral indices related to the canopy N content presented in the literature 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 LleraCid, 2011). The findings of Wang et al. (2012) are an exception. These results suggest that the R2 for the optimum three-band spectral index is related to the canopy N content of winter wheat and achieved a value of 0.86 across the growth stages, experiments and years. The explanation for the result may be that the authors related the spectral indices to the leaf N concentration rather than to the whole plant canopy concentration; in addition, their experiments were conducted in similar ecological regions and under relatively controlled conditions. The robustness of the PLSR models strongly depends on whether spanning those spectral variations as much as possible in calibration datasets used. Prediction errors can be observed if insufficient spectral variation information of calibration datasets is used to calibrate the model. When contrasting datasets with different growth stages, cultivars, sites and years were used to calibrate and validate each other, the accuracy and precision of the prediction strongly decreased and the predictive values deviated substantially from the 1:1 line (Fig. 8). In contrast, if the dataset of “all data combinations” was used to calibrate the model and/or if any one of the nine validation datasets was used to validate the model, the predictive performance was significantly increased and the prediction values almost coincided with the 1:1 line (Fig. 9). Thus, a global model for N estimation in winter wheat for all conditions is probably

208

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

Loading weights of first latent variable

0.15

Chinese cultivar German cultivar Before flowering After flowering Quzhou

0.10

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

Freising 2010 2011 All data

0.05

Acknowledgements

0.00

This research was financially supported by the German Federal Ministry of Education and Research (BMBF) (Project No. FKZ 0330800A) and the International Bureau of the German Federal Ministry of Education and Research (Project No: CHN11/052).

-0.05 -0.10

References

-0.15

Loading weights of second latent variable

-0.20 300

400

500

600

700

800

900

1000

1100

300

400

500

600

700

800

900

1000

1100

300

400

500

600

700

800

900

1000

1100

0.20 0.15 0.10 0.05 0.00 -0.05 -0.10 -0.15 -0.20

Loading weight of third latent variable

0.3

0.2

0.1

0.0

-0.1

-0.2

-0.3

Wavelength (nm) Fig. 10. Comparison of the loading weights for each PLSR model depending on the main latent variables.

developed when the spectra and the canopy N content sampling from other cultivars, ecological areas and years are involved. 5. Conclusions Although the bands were optimised using band optimum algorithms, the method of spectral indices did not deliver satisfactory results for the derivation of the canopy N content of winter wheat grown under contrasting field conditions; this is most likely due to the influences of the “dilution effect”, cultivar, site and year. The PLSR is a potentially useful method to evaluate the canopy N content in the field in a timely manner compared with the method of spectral indices. Particularly, when more spectral and field

Barnes, E.M., Clarke, T.R., Richards, S.E., Colaizzi, P.D., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., Thompson, T., Lascano, R.J., Li, H., Moran, M.S., 2000. Coincident detection of crop water stress nitrogen status and canopy density using ground based multispectral data. In: Robert, P.C., Rust, R.H., Larson, W.E. (Eds.), Proc. 5th Int. Conf. Precis Agric. Bloomington, MN, USA. Broge, N.H., Leblanc, E., 2000. Comparing prediction power and stability of broad band and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment 76, 156–172. Chen, X.P., Zhang, F.S., Römheld, V., Horlacher, D., Schulz, R., Böning-Zilkens, M., Wang, P., Claupein, W., 2006. Synchronizing N supply from soil and fertilizer and N demand of winter wheat by an improved Nmin method. Nutrient Cycling in Agroecosystems 74, 91–98. Cho, M.A., Skidmore, A.K., 2006. A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method. Remote Sensing of Environment 101, 181–193. Cho, M.A., Skidmore, A.K., Atzberger, C., 2008. Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAILH) simulated data. International Journal of Remote Sensing 29, 2241–2255. Dash, J., Curran, P.J., 2004. The MERIS terrestrial chlorophyll index. International Journal of Remote Sensing 25, 5403–5413. Daughtry, C.S.T., Walthall, C.L., Kim, M.S., Colstoun, E.B., McMurtrey III, J.E., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74, 229–239. Delegido, J., Alonso, L., González, G., Moreno, J., 2010. Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC). International Journal of Applied Earth Observation and Geoinformation 12, 165–174. Delegido, J., Vergara, C., Verrelst, J., Gandía, S., Moreno, J., 2011. Remote estimation of crop chlorophyll content by means of high-spectral-resolution reflectance techniques. Agronomy Journal 103, 1834–1842. Erdle, K., Mistele, B., Schmidhalter, U., 2011. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Research 124, 74–84. Fava, F., Colombo, R., Bocchi, S., Meroni, M., Sitzia, M., Fois, N., Zucca, C., 2009. Identification of hyperspectral vegetation indices for Mediterranean pasture characterization. International Journal of Applied Earth Observation and Geoinformation 11, 233–243. Gislum, R., Micklander, E., Nielsen, J.P., 2004. Quantification of nitrogen concentration in perennial ryegrass and red fescue using near-infrared reflectance spectroscopy (NIRS) and chemometrics. Field Crops Research 88, 269–277. Gitelson, A.A., Vi˜na, A., Ciganda, V., Rundquist, D.C., Arkebauer, T.J., 2005. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters 32, L08403, http://dx.doi.org/10.1029/2005GL022688. Green, D.S., Erickson, J.E., Kruger, E.L., 2003. Foliar morphology and canopy nitrogen as predictors of light-use efficiency in terrestrial vegetation. Agricultural and Forest Meteorology 115, 163–171. Guyot, G., Baret, F., Major, D.J., 1988. High spectral resolution: determination of spectral shifts between the red and the near infrared. International Archives of Photogrammetry and Remote Sensing 11, 750–760. Haboudane, D., Miller, J.R., Tremblay, N., Zarco-Tejada, P.J., Dextraze, L., 2002. Integrated narrowband vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sensing of Environment 81, 416–426. Haboudane, D., Miller, J.R., Pattey, E., Zarco-Tejada, P.J., Stachan, I.B., 2004. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sensing of Environment 90, 337–352. Haboudane, D., Tremblay, N., Miller, J.R., Vigneault, P., 2008. Remote estimation of crop chlorophyll content using spectral indices derived from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 46, 423–437. Hansen, P.M., Schjoerring, J.K., 2003. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment 86, 542–553. Hatfield, J.L., Gitelson, A.A., Schepers, J.S., Walthall, C.L., 2008. Application of spectral remote sensing for agronomic decisions. Agronomy Journal 100, 117–131.

F. Li et al. / Europ. J. Agronomy 52 (2014) 198–209 Herrmann, I., Pimstein, A., Karnieli, A., Cohen, Y., Alchanatis, V., Bonfil, D.J., 2011. LAI assessment of wheat and potato crops by VEN mu S and Sentinel-2 bands. Remote Sensing of Environment 115, 2141–2151. Jasper, J., Reusch, S., Link, A., 2009. In: Van Henten, E.J., Goense, D., Lokhorst, C. (Eds.), Active sensing of the N status of wheat using optimized wavelength combination: impact of seed rate, variety and growth stage, Precision Agriculture 09: Papers from the 7th European Conference on Precision Agriculture, Wageningen. , pp. 23–30. Jordan, C.F., 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology 50, 663–666. Justes, E., Mary, B., Meynard, J.M., Machet, J.M., Thelier-Huche, L., 1994. Determination of a critical nitrogen concentration dilution curve for winter wheat. Annals of Botany 74, 397–407. Kim, M.S., Daughtry, C.S.T., Chappelle, E.W., McMurtrey III, J.E., Walthall, C.L., 1994. The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (APAR). In: Proceedings of 6th Symposium Physical Measurements and Spectral Signature in Remote Sensing, Val D’Isere, France. Lemaire, G., Jeuffroy, M., Gastal, F., 2008. Diagnosis tool for plant and crop N status in vegetative stage: theory and practices for crop N management. European Journal of Agronomy 28, 614–624. Li, F., Miao, Y., Hennig, S., Gnyp, M., Chen, X., Jia, L., Bareth, G., 2010. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precision Agriculture 11, 335–357. Li, F., Mistele, B., Hu, Y., Yue, X., Yue, S., Miao, Y., Chen, X., Cui, Z., Meng, Q., Schmidhalter, U., 2012. Remotely estimating aerial N status of phenologically differing winter wheat cultivars grown in contrasting climatic and geographic zones in China and Germany. Field Crops Research 138, 21–32. Mistele, B., Schmidhalter, U., 2008. Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. European Journal of Agronomy 29, 184–190. Mistele, B., Schmidhalter, U., 2010. Tractor-based quadrilateral spectral reflectance measurements to detect biomass and total aerial nitrogen in winter wheat. Agronomy Journal 102, 499–506. Mutanga, O., Skidmore, A.K., 2004. Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing 25, 3999–4014. Nguyen, T.A., Lee, B.W., 2006. Assessment of rice leaf growth and nitrogenstatus by hyperspectral canopy reflectance and partial least square regression. European Journal of Agronomy 24, 349–356. Ollinger, S.V., 2011. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytologist 189, 375–394. Ollinger, S.V., Richardson, A.D., Martin, M.E., Hollinger, D.Y., Frolking, S.E., Reich, P.B., Plourde, L.C., Katul, G.G., Munger, J.W., Oren, R., Smith, M.L., Paw, U.K.T., Bolstad, P.V., Cook, B.D., Day, M.C., Martin, T.A., Monson, R.K., Schmid, H.P., 2008. Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: functional relations and potential climate feedbacks. Proceedings of the National Academy of Sciences USA 105, 19335–19340. Oppelt, N., Mauser, W., 2004. Hyperspectral monitoring of physiological of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing 25, 145–159. Pearson, P.L., Miller, L.D., 1972. Remote mapping of standing crop biomass for estimation of the productivity of the short grass prairie. In: Proceedings of the 8th International Symposium of Remote Sensing of the Environment, Pawnee National Grasslands, Colorado, pp. 1357–1381.

209

Pinter, P.J., Hatfield, J.L., Schepers, J.S., Barnes, E.M., Moran, M.S., Daughtry, C.S.T., Upchurch, D.R., 2003. Remote sensing for crop management. Photogrammetric Engineering & Remote Sensing 69, 647–664. Rodriguez-Moreno, F., Llera-Cid, F., 2011. Evaluating spectral vegetation indices for a practical estimation of nitrogen concentration in dual-purpose (forage and grain) triticale. Spanish Journal of Agricultural Research 9, 681–686. Rouse, J.W., Haas, R.H., Schell, J.A., Deering, D.W., Harlan, J.C., 1974. Monitoring the vernal advancement of retrogradation of natural vegetation. NASA/GSFC, Type III, Final Report, Greenbelt MD, USA. Schmidhalter, U., 2005. Development of a quick on-farm test to determine nitrate levels in soil. Journal Plant Nutrition and Soil Science 168, 432–438. ˜ Serrano, L., Penuelas, J., Ustin, S.L., 2002. Remote sensing of nitrogen and lignin in Mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sensing of Environment 81, 355–364. Smith, M.L., Martin, M.E., Plourde, L., Ollinger, S.V., 2003. Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison between an airborne (AVIRIS) and space borne (Hyperion) sensor. IEEE Transactions on Geoscience and Remote Sensing 41, 1332–1337. Soderstrom, M., Borjesson, T., Pettersson, C.G., Nissen, K., Hagner, O., 2010. Prediction of protein content in malting barley using proximal and remote sensing. Precision Agriculture 11, 587–599. Stroppiana, D., Boschetti, M., Brivio, P.A., Bocchi, S., 2009. Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. Field Crops Research 111, 119–129. Tarpley, L., Raja Reddy, K., Sassenrath-Cole, G.F., 2000. Reflectance indices with precision and accuracy in predicting cotton leaf nitrogen concentration. Crop Science 40, 1814–1819. Thenkabail, P.S., Smith, R.B., Pauw, E.D., 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sensing of Environment 71, 158–182. Tian, Y.C., Yao, X., Yang, J., Cao, W.X., Hannaway, D.B., Zhu, Y., 2011. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crops Research 120, 299–310. Van Der Heijden, G., Clevers, J., Schut, A.G.T., 2007. Combining close-range and remote sensing for local assessment of biophysical characteristics of arable land. International Journal of Remote Sensing 28, 5485–5502. Viscarra Rossel, R.A., 2008. ParLeS: software for chemometric analysis of spectroscopic data. Chemometrics and Intelligent Laboratory Systems 90, 72–83. Wang, W., Yao, X., Yao, X.F., Tian, Y.C., Liu, X.J., Ni, J., Cao, W.X., Zhu, Y., 2012. Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crops Research 129, 90–98. Winterhalter, L., Mistele, B., Jampatong, S., Schmidhalter, U., 2011. High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. European Journal of Agronomy 35, 22–32. Wold, S., Sjöström, M., Eriksson, L., 2001. PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems 58, 109–130. Zarco-Tejada, P.J., Miller, J.R., Noland, T.L., Mohammed, G.H., Sampson, P., 2001. Scaling up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 39, 1491–1501. Zhu, Y., Zhou, D., Yao, X., Tian, Y., Cao, W., 2007. Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice. Australian Journal of Agricultural Research 58, 1077–1085.