Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution

Journal of Integrative Agriculture 2019, 18(7): 1562–1570 Available online at www.sciencedirect.com

ScienceDirect

RESEARCH ARTICLE

Estimating total leaf nitrogen concentration in winter wheat by canopy hyperspectral data and nitrogen vertical distribution DUAN Dan-dan1, 2, 3, 4, ZHAO Chun-jiang2, 3, 4, LI Zhen-hai2, 3, 4, YANG Gui-jun2, 3, 4, ZHAO Yu1, 2, 3, 4, QIAO Xiao-jun2, 3, 4, ZHANG Yun-he2, 3, 4, ZHANG Lai-xi2, 3, 4, YANG Wu-de1 1

Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu 030801, P.R.China

2

National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China 3 Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs/Beijing Research Center for Information Technology in Agriculture, Beijing 100097, P.R.China 4 Beijing Engineering Research Center for Agriculture Internet of Things, Beijing 100097, P.R.China

Abstract The use of remote sensing to monitor nitrogen (N) in crops is important for obtaining both economic benefit and ecological value because it helps to improve the efficiency of fertilization and reduces the ecological and environmental burden. In this study, we model the total leaf N concentration (TLNC) in winter wheat constructed from hyperspectral data by considering the vertical N distribution (VND). The field hyperspectral data of winter wheat acquired during the 2013–2014 growing season were used to construct and validate the model. The results show that: (1) the vertical distribution law of LNC was distinct, presenting a quadratic polynomial tendency from the top layer to the bottom layer. (2) The effective layer for remote sensing detection varied at different growth stages. The entire canopy, the three upper layers, the three upper layers, and the top layer are the effective layers at the jointing stage, flag leaf stage, flowering stages, and filling stage, respectively. (3) The TLNC model considering the VND has high predicting accuracy and stability. For models based on the greenness index (GI), mND705 (modified normalized difference 705), and normalized difference vegetation index (NDVI), the values for the determining coefficient (R2), and normalized root mean square error (nRMSE) are 0.61 and 8.84%, 0.59 and 8.89%, and 0.53 and 9.37%, respectively. Therefore, the LNC model with VND provides an accurate and non-destructive method to monitor N levels in the field. Keywords: nitrogen concentration, hyperspectral, vertical nitrogen distribution, winter wheat

1. Introduction Received 3 January, 2019 Accepted 12 April, 2019 DUAN Dan-dan, E-mail: [email protected]; Correspondence YANG Wu-de, Tel: +86-10-51503810, E-mail: [email protected] © 2019 CAAS. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/). doi: 10.1016/S2095-3119(19)62686-9

Nitrogen (N) is a major component for chlorophyll, plant hormones, and vitamins in crops. It also directly influences crop yield and is a vital consideration for fertilization management (Yao et al. 2015; Geng et al. 2016; Zhang et al. 2017; Jeong and Bhattarai 2018). Therefore, how to make an accurate N diagnosis of crop is crucial for precious fertilizer, and yield and quality prediction.

DUAN Dan-dan et al. Journal of Integrative Agriculture 2019, 18(7): 1562–1570

Traditional lab-test methods to measure toal leaf N concentration (TLNC), such as Kjeldahl procedure, are mostly time-consuming, labor intensive, and destructive. However, remote-sensing technology has the potential to simplify this task because it offers rapid, large-scale, and nondestructive features. TLNC estimation as a whole part in the past studies was studied using remote sensing data (Xi and Yong 2016). In addition, the vertical distribution in the canopy, and especially the vertical N distribution (VND), is important in plant growth (Guo et al. 2015; Li et al. 2015; Kong et al. 2017; Wei et al. 2017). In previous work looking at these issues, Wang et al. (2012) found that LNC concentration decreased from the upper layer to the lower layer. Li et al. (2013) suggested two aspects of current research into the use of remote sensing to estimate the VND in crops: one possibility based upon hyperspectral imaging data and the other possibility by integrating a VND model and canopy reflectance data. Moreover, remote sensing technology cannot effectively detect LNC in the whole crop canopy. It is still unclear which leaf layers in the canopy should be involved in establishing a remote sensing model for estimating LNC. Based on the vertical distribution and the effective layer, Luo et al. (2016) established a model for estimating the N content in the three upper layers of the reed by using the plant pigment ratio (PPR) and the normalized difference vegetation index (NDVI), and obtained the determining coefficient (R2)=0.88 and RMSE=0.37%. In addition, by varying the crop variables in the wheat canopy and the spectral features of the canopy, Li et al. (2013) derived the response of the vegetation index (VI) to different leaf layers and to the spike. Numerous previous studies have shown the regular vertical N distribution in the crop canopy, but how each leaf layer affects the overall estimate of canopy N has yet to be considered. The conflict between N deficiency at the bottom of the canopy and remote sensing from above the canopy surface limits the applicability of remote sensing for estimating the N distribution in the crop canopy. How to estimate LNC combining non-uniform vertical N distribution and efficient but imperfect remote sensing data have become an urgent problem to be solved in this paper. Therefore, it is desirable to improve the accuracy of estimating the N distribution by integrating the VND information and hyperspectral remote-sensing data. The objective of the present study is thus to: (1) analyze the VND in winter wheat at different growth stages, (2) determine the optimal correlation between hyperspectral data and the upper layer of LNC (LNCup), and (3) construct the total LNC

(TLNC) in winter wheat by integrating into a single model the VND and the optimal upper LNCup.

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2. Materials and methods 2.1. Experimental design Field tests were conducted in 2013–2014 at the National Experimental Station for Precision Agriculture (40°10´30´´– 40°11´18´´N, 116°26´10´´–117°27´05´´E) in Xiaotangshan Town, Changping District, Beijing, China (Fig. 1). The region has a continental monsoon climate. The soil (0–0.3 m) in the test area is heavy loam with 15.84–20.62 g kg−1 organic matter, 3.16–14.82 mg kg−1 nitrate N, 10.02–12.32 mg kg−1 ammoniacal N, 86.83–120.62 mg kg−1 available potassium (K), and 3.14–21.18 mg kg−1 available phosphorus (P). The experiment was designed with three orthogonal factors (winter wheat cultivars, N application, and irrigation amount), with three replications of each treatment. Four N applications (N1, 0 kg ha−1; N2, 90 kg ha−1; N3, 180 kg ha−1; N4, 270 kg ha−1), three irrigation amounts (W1, 25 mm; W2, 171 mm; W3, 317 mm), and two cultivars (Jing 9843 (P1) and Zhongmai 175 (P2)) were applied with three repetitions. There were 16 plots, with each measuring 10 m×15 m. For all treatments, the row spacing was 15 cm and the fertilizers were applied with urea (46% N), calcium superphosphate (12% P2O5), and potassium sulfate (50% K2O).

2.2. Data acquisition Field canopy hyperspectral data The canopy reflectance spectra were collected by using a Field Spec Pro FR2500 (Analytical Spectral Devices, Boulder, CO, USA) at the jointing (Zadoks growth stage (ZS)3.3), booting (ZS4.5), flowering (ZS6.3), and filling (ZS7.5) stages of winter wheat (Zadoks et al. 1974). The spectral range of the spectrometer is 350–2 500 nm, with a sampling interval of 1.4 nm from 350 to 1 000 nm and 2 nm from 1 000 to 2 500 nm, and a spectral resolution of 3 nm from 350 to 1 000 and 10 nm from 1 000 to 2 500 nm. Measurements were made under clear-sky conditions between 10:00 and 14:00. In addition, all measurements were made at a nadir orientation 1.0 m above the canopy. A 0.4 m×0.4 m BaSO4 calibration panel was used to calculate the black and baseline reflectance. Ten reflectance spectra were collected per plot and averaged to produce the final spectra. Plant measurements Immediately after canopy reflectance measurements, leaves were collected from various leaf positions, which were labeled layer one (L1), two (L2), three (L3), four (L4), and five (L5) from the top of the canopy to the ground (Fig. 2). Samples were dried in an oven at 105°C for 30 min, and then dried at 80°C until attaining a constant mass for determining the dry weight (LBWi). All dried and

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ZM175

J9843

ZM175 10 m

N1 (1)

N4 (5)

N3 (9)

N2 (13)

N3 (2)

N2 (6)

N1 (10)

N4 (14)

N2 (3)

N3 (7)

N4 (11)

N1 (15)

N1 (8)

N2 (12)

N3 (16)

Huairou Miyun

Changping

Pinggu

805 km

15 m

0

80 m

Shunyi

Mentougou Haidian Shijingshan Chaoyang Fengtai Tongzhou Fangshan Daxing

W2

Yanqing

W1

Beijing

5m

J9843

W3

N

W2

DUAN Dan-dan et al. Journal of Integrative Agriculture 2019, 18(7): 1562–1570

9m

5m

N4 (4) 10 m

50 m

N

(1) Cultivar: P1 (J9843); P2 (ZM175) (2) N rate (kg N ha–1): N1 (0); N2 (90); N3 (180); N4 (270) (3) Irrigation (mm): W1 (25); W2 (171); W3 (317)

Fig. 1 Field location and experimental plots (green plots were sampled based on each leaf layer; others were sampled without leaf layer). J9843 (P1), Jing 9843; ZM175 (P2), Zhongmai 175. Numbers in brackets represented plot serial number.

milled plant samples were digested with H2SO4-H2O2 as described by Thomas et al. (1967). The LNCi of each layer in the digestion solution was analyzed using a flow injection auto-analyzer. The N concentration of the various upper layers are denoted upper layer one (U1), U2 (LNC of two upper layers), U3 (LNC of three upper layers), U4 (LNC of four upper layers), and U5 (LNC of five upper layers). The formula for the LNC of each upper layer is: LNCup=∑pi=1(LNCi×LBWi)/∑pi=1(LBWi) (1) Where, LNCup is the LNC of upper layer p, and LNCi and LBWi are the LNC mass and leaf biomass of layer i, respectively. In this study, there were four layers at the jointing (ZS3.3) and flowering (ZS6.3) stages, five layers at the booting stage (ZS4.5), and three layers at the filling stage (ZS7.5) (Fig. 2). When the P-values of jointing, booting, flowering, and filling stages correspond to 4, 5, 4, and 3, respectively, the value of formula (1) is expressed as TLNC.

2.3. Methods Spectral vegetation indices We selected 10 vegetation indices (VIs) considered to be good candidates for evaluating N (Table 1). The correlation between different LNCup and each selected spectral index was analyzed. Finally, the

optimal spectral indices were used to estimate the TLNC of winter wheat. VND for estimating TLNC One VND model for estimating the TLNC was constructed by considering the non-uniform leaf N distribution and spectral indices. In this case, the TLNC of winter wheat is calculated as follows: TLNC=LNCOL/β (2) Where, β is the ratio of LNCup and TLNC at each growth stage, and LNCOL is the LNCup of the optimized layer correlated with spectral indices at each stage, which is calculated from the related spectral indices as follows: LNCOL=f(VI) (3) Where, VI is a spectral vegetation index from Table 1. We constructed a linear regression model between LNCOL and VI. To validate the accuracy of the VND method for estimating TLNC, we use the three VIs most correlated with LNCup to construct the TLNC model. Statistical analysis Data collected from four separate leaf-layer plots (n=12 at each growth stage) were used to analyze the correlation between LNCup and the spectral VIs. We then constructed the model to estimate TLNC by using the VND method. Data collected from other plots (n=36 at each stage) were used to determine the accuracy of the estimate made by the TLNC model. Pearson correlations (r) between vegetation indices and

DUAN Dan-dan et al. Journal of Integrative Agriculture 2019, 18(7): 1562–1570

agronomic N variables were analyzed. R2 and normalized root mean square error (nRMSE) were used to quantify the accuracy and thereby evaluating the model (Delalieux et al. 2008). In general, the accuracy of a model’s estimate depends on R2 and nRMSE. A good model has a higher R2 and a lower nRMSE. The formulae for nRMSE is: nRMSE (%)=RMSE/Mean(Y)×100 (4)

three layers at ZS4.5 and ZS7.5, respectively. Second, the trends in the vertical distribution also varied in each growth stage: the LNC decreased from the top layer (L1) to the ground layer at ZS3.3, ZS6.3, and ZS7.5, and the LNC increased from L1 to L2 and then decreased from L2 to L5 at ZS4.5. We calculated β at the various layers and the related correlation between LNCup and TLNC (Table 2). The results showed that LNCup correlates strongly with TLNC, whereas the difference between LNCup and TLNC was demonstrated at different growth stages (e.g., at ZS3.3, ZS4.5, ZS6.3, and ZS7.5, β=1.095, 1.018, 1.164, and 1.137, respectively). The values of r increased from the top layer to the ground layer (e.g., for LNCu1, LNCu2, and LNCu3, r=0.74, 0.88, and 0.98, respectively).

1 n ∑ (Y –Y ´)2 (5) n i=1 i i Where, Yi is the measured value, Yi´ is the predicted value, and n is the number of samples. RMSE=

3. Results 3.1. Leaf N distribution in winter wheat

3.2. Optimized layer of hyperspectral detection

Fig. 3 showed the distribution of LNC at each layer in winter wheat. The results revealed differences in the vertical distribution of LNC at different growth stages. First, leaf layers at different growth stages were different; four layers were present at ZS3.3 and ZS6.3, whereas five layers and

Level 1

Level 2

Level 2

Level 3

Level 3

Level 4

Level 4

Level 5

ZS3.3

We analyzed the correlations between LNC up and 10 selected VIs (Table 3). The results showed that Pearson correlation coefficient r varied at different growth stages.

Level 1

Level 1

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Level 1

Level 2

Level 2

Level 3 Level 3 Level 4

ZS4.5

ZS6.5

ZS7.5

Fig. 2 Conceptual diagram of vertical layer division within the winter wheat canopy. ZS, Zadoks growth stage. Table 1 Summary of spectral vegetation indices used in this study Index Red edge model (CIred edge) Double-peak canopy nitrogen index (DCNI) Greenness index (GI) Green normalized difference vegetation index (GNDVI) Modified normalized difference 705 (mND705) Modified simple ratio (MSR) Normalized difference red edge index (NDRE) Normalized difference vegetation index (NDVI) NDVI canste (NDVIcanste) Wide dynamic range vegetation index (WDRVI) 1)

R, the reflectance of the specific band.

Formula1) (R750/R720)–1 (R720–R700)/(R700–R670)/(R720–R670+0.03) R554/R677 (R750–R550)/(R750+R550) (R750–R705)/(R750+R705–2R445) (R800/R670–1)/sqrt(R800/ R670+1) (R790–R720)/(R790+R720) (R800–R670)/(R800+R670) (R760–R708)/(R760+R708) (0.1R890 –R670)/(0.1R890+R670)

Reference Gitelaso et al. (2005) Chen et al. (2010) Delalieux et al. (2008) Lemaire et al. (2007) Chen (1996) Sims and Gamon (2002) Fitzgerald et al. (2010) Hurcom and Harrison (1998) Steddom et al. (2003) Gitelson et al. (2004)

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For example, CIred edge correlated strongly with LNCup with r=0.84–0.87 from LNCu1 to LNCu3 at ZS3.3, whereas r=0.37 to 0.74 from LNCu1 to LNCu4 at ZS4.5. At ZS3.3, r increased from LNCu1 to TLNC. TLNC was the most strongly correlated with the spectral indices, which meant that the optimized layer of hyperspectral data at ZS3.3 included the entire canopy. At ZS4.5 and ZS6.3, r increased from LNCu1 to

A

B C 4.5 y=–0.140x2+0.078x+4.041 5.0 y=–0.185x2+0.742x+3.487 4.5 y=–0.14x2+0.233x+3.926 4.0

LNC (%)

LNCu3 or LNCu4, and then decreased to the TLNC. For these two growth stages, the optimized layer of the hyperspectral data was the L3. At ZS7.5, r decreased from LNCu1 to TLNC, which indicated that the L1 was the optimized layer of hyperspectral data. Considering the L2 as the functional layers for grain filling, LNCu2 was determined to be the optimized layer for such an analysis.

R = 0.99 2

4.5

3.5 3.0 2.5 2.0 1.5

L1

L2

L3

L4

R2=0.96

4.0

4.0

3.5

3.5

3.0

3.0

2.5

2.5

L1

L2

L3

L4

L5

R2=0.99

D

4.0 y=–0.242x2+0.314x+3.55 R2=1.00

3.5 3.0 2.5

2.0

L1

L2

L3

L4

2.0

L1

L2

L3

Fig. 3 Leaf nitrogen (N) concentrations (LNC) in leaves from the first layer (L1) to the fifth layer (L5) of the ZS3.3 (A), ZS4.5 (B), ZS6.3 (C), and ZS7.5 (D). ZS, Zadoks growth stage.

Table 2 β values of each upper layer LNC and correlations with TLNC (n=12)1) Index2) β r

LNCu1

ZS3.3 LNCu2

LNCu3

LNCu1

ZS4.5 LNCu2 LNCu3

LNCu4

LNCu1

ZS6.3 LNCu2

LNCu3

ZS7.5 LNCu1 LNCu2

1.095 0.94**

1.077 0.99**

1.036 1**

1.018 0.79**

1.077 0.96**

1.024 0.99**

1.164 0.74**

1.119 0.88**

1.045 0.98**

1.137 0.94**

1.070 0.98**

1.078 0.98**

1)

LNC, leaf nitrogen concentration; TLNC, total LNC. ZS, Zadoks growth stage. β, the ratio of upper layer nitrogen concentration to total layers; r, the correlation coefficient. ** represents model significant at the 0.01 probability level (P<0.01). 2)

Table 3 Correlations between leaf nitrogen concentration (LNC) at each upper layer and spectral vegetation indices (n=12)1) Growth stage2)

Layer

CIred edge

ZS3.3

LNCu1 LNCu2 LNCu3 TLNC LNCu1 LNCu2 LNCu3 LNCu4 TLNC LNCu1 LNCu2 LNCu3 TLNC LNCu1 LNCu2 TLNC

0.84** 0.86** 0.87** 0.87** 0.37 0.62* 0.74** 0.74** 0.72** 0.62* 0.73** 0.79** 0.80** 0.69** 0.65* 0.59*

ZS4.5

ZS6.3

ZS7.5

1)

DCNI 0.52 0.56* 0.59* 0.59* 0.54 0.69** 0.74** 0.74** 0.72** 0.65* 0.69** 0.75** 0.81** 0.71** 0.69* 0.60*

GI

GNDVI

mND705

MSR

NDRE

NDVI

NDVIcanste

WDRVI

0.83** 0.85** 0.85** 0.87** 0.20 0.52 0.64* 0.67* 0.65* 0.54 0.63* 0.67* 0.67* 0.65* 0.62* 0.58*

0.84** 0.88** 0.88** 0.89** 0.40 0.61* 0.72** 0.72** 0.69** 0.63* 0.75** 0.82** 0.83** 0.63* 0.59* 0.52

0.83** 0.87** 0.88** 0.89** 0.43 0.63* 0.73** 0.74** 0.71** 0.62* 0.72** 0.79** 0.81** 0.66* 0.62* 0.56*

0.86** 0.87** 0.88** 0.89** 0.28 0.57* 0.70** 0.71** 0.69** 0.59* 0.70** 0.75** 0.75** 0.66* 0.62* 0.57*

0.84** 0.87** 0.88** 0.88** 0.40 0.63* 0.74** 0.74** 0.71** 0.63* 0.74** 0.81** 0.83** 0.67* 0.62* 0.56*

0.84** 0.88** 0.89** 0.90** 0.41 0.60* 0.70** 0.71** 0.68* 0.62* 0.73** 0.79** 0.81** 0.62* 0.58* 0.52

0.84** 0.87** 0.88** 0.89** 0.40 0.62* 0.73** 0.74** 0.71** 0.62* 0.73** 0.80** 0.82** 0.66* 0.62* 0.55

0.85** 0.88** 0.89** 0.89** 0.33 0.59* 0.71** 0.72** 0.69** 0.60* 0.71** 0.77** 0.78** 0.65* 0.61* 0.56*

CIred edge, red edge model; DCNI, double-peak canopy nitrogen index; GI, greenness index; GNDVI, green normalized difference vegetation index; mND705, modified normalized difference 705; MSR, modified simple ratio; NDRE, normalized difference red edge index; NDVI, normalized difference vegetation index; NDVIcanste, NDVI canste; WDRVI, wide dynamic range vegetation index. 2) ZS, Zadoks growth stage. ** and * represent model significant at the probability level of 0.01 (P<0.01) and 0.05 (P<0.05), respectively.

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3.3. TLNC models based on LNCOL and VND

better estimates of the TLNC than the other selected VIs.

We calculated the correlation coefficients between LNCOL

correlated with LNCOL, and the correlation between MSR and

However, the three VIs, all with r=0.79, were more strongly

and the VIs, and between TLNC and the VIs (Table 4).

WDRVI and LNCOL also reached 0.79. These results showed

Comprising the relationship between TLNC and vegetation

that LNCOL was more sensitive to spectral information than

indices, the relationship between LNCOL and each vegetation

TLNC.

index showed higher correlations. Especially for DCNI, there

Three VIs (GI, mND705, and NDVI) were then selected

was no significant correlation being found between DCNI

to establish linear models for TLNC (Fig. 4) and for LNCOL

and the TLNC, although a significant difference (P<0.05)

(Fig. 5). The results showed that the accuracy of the

appeared between DCNI and LNCOL. GI, mND705, and

TLNC model based on GI was R2=0.56, whereas the

NDVI, with r=0.75, 0.72, and 0.71, respectively, provided

model of LNCOL was R2=0.63. Based on mND705, the

Table 4 Correlations between TLNC/LNCOL and spectral indices at all growth stage (n=48)1) Layer2)

CIred edge

DCNI

GI

GNDVI

mND705

MSR

NDRE

NDVI

NDVIcanste

WDRVI

TLNC LNCOL

0.67* 0.77**

0.08 0.27*

0.75** 0.79**

0.68* 0.78**

0.72** 0.79**

0.71** 0.79**

0.65* 0.75**

0.71** 0.79**

0.70** 0.78**

0.70** 0.79**

1)

CIred edge, red edge model; DCNI, double-peak canopy nitrogen index; GI, greenness index; GNDVI, green normalized difference vegetation index; mND705, modified normalized difference 705; MSR, modified simple ratio; NDRE, normalized difference red edge index; NDVI, normalized difference vegetation index; NDVIcanste, NDVI canste; WDRVI, wide dynamic range vegetation index. 2) TLNC, total leaf nitrogen concentration; LNCOL, LNC of optimized layer at each growth stage, which was TLNC at ZS3.3, LNCu3 at ZS4.5, LNCu3 at ZS6.3, and LNCu2 at ZS7.5, respectively. ZS, Zadoks growth stage. ** and * represent model significant at the probability level of 0.01 (P<0.01) and 0.05 (P<0.05), respectively.

TLNC (%)

A

4.5

B 4.5

C 4.5

4.0

4.0

4.0

3.5

3.5

3.5

3.0

3.0

3.0

2.5 2.0 0.5

2.5

y=0.661x+2.26 R2=0.56 1.0

1.5

2.0 GI

2.5

3.0

2.5

y=2.372x+1.784 R2=0.51

2.0 0.3 0.4 0.5 0.6 0.7 0.8 0.9 mND705

2.0 0.5

y=2.385x+1.456 R2=0.50 0.6

0.7

0.8

0.9

1.0

NDVI

Fig. 4 Total leaf nitrogen concentration (TLNC) estimation in wheat using linear model with optimized spectral indices. A, greenness index (GI). B, modified normalized difference 705 (mND705). C, normalized difference vegetation index (NDVI).

B 4.5

C 4.5

4.0

4.0

4.0

3.5

3.5

3.5

3.0

3.0

3.0

LNCOL (%)

A 4.5

y=0.642x+2.457 R2=0.63

2.5 2.0 0.5

1.0

1.5

2.0 GI

2.5

2.5 3.0

2.0 0.3

y=2.409x+1.926 R2=0.63 0.5 0.7 mND705

y=2.450x+1.571 R2=0.62

2.5 0.9

2.0 0.5

0.6

0.7 0.8 NDVI

0.9

1.0

Fig. 5 Optimized layer of LNCOL (leaf nitrogen concentration of optimized layer at each growth stage) estimation in wheat using vertical N distribution (VND) method and optimized spectral indices. A, greenness index (GI). B, modified normalized difference 705 (mND705). C, normalized difference vegetation index (NDVI).

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R2 for estimating TLNC and LNCOL were 0.51 and 0.63, respectively. As for NDVI, the R2 for estimating TLNC and LNCOL were 0.50 and 0.62, respectively. Clearly, the model for LNCOL performed better than the TLNC model, which further established that the optimal layer was critical in the remote sensing inversion of N concentration in winter wheat. Therefore, we used the VND model with the LNCOL model and the related β values at each growth stage to estimate the TLNC during the entire growth stage. We estimated the TLNC in winter wheat by using the VND method (Fig. 6); the results showed that TLNC estimate was more accurate than the direct linear method. For the VND method of estimating TLNC based on GI, mND705, NDVI, and nRMSE was 7.39, 7.65, and 7.64%, respectively, which was less than the deviation between measured and estimated TLNC. Clearly, the underestimation at high TLNC values and overestimation at low TLNC values were reduced upon using the VND method.

Estimated TLNC (%)

A

B

4.5

3.4. Validation of winter wheat N concentration To prove the stability of the TLNC model with the VND method, the TLNC estimates were verified by using a validating dataset (Fig. 7). The results indicated that a TLNC model based on three VIs accurately predicted the TLNC by using the VND method. For the models based on GI, mND705, and NDVI, R2 was 0.61, 0.59, and 0.53, respectively, and nRMSE was 8.84, 8.89, and 9.37%, respectively. All values for nRMSE were less than 10%, which reflected a significant improvement in the accuracy of the remote-sensing retrieval model of N concentration based on the VND. In addition, the optimal layer improved significantly when using the remote-sensing inversion method.

4. Discussion The plant N distribution varies in the vertical direction and C

4.5

4.5

4.0

4.0

4.0

3.5

3.5

3.5

3.0

3.0

3.0

2.5 2.0 2.0

2.5

VND (7.39%) Linear (8.31%) 2.5

3.0

3.5

4.0

2.0 2.0

4.5

2.5

VND (7.65%) Linear (8.79%) 2.5 3.0 3.5 4.0 Measured TLNC (%)

4.5

2.0 2.0

VND (7.64%) Linear (8.93%) 2.5

3.0

3.5

4.0

4.5

Fig. 6 Comparison of estimated total leaf nitrogen concentration (TLNC) in winter wheat with linear method and vertical N distribution (VND) from greenness index (GI, A), modified normalized difference 705 (mND705, B), and normalized difference vegetation index (NDVI, C). Values in the bracket represents nRMSE (normalized root mean square error). R2, determining coefficient.

Estimated TLNC (%)

A

5.0 4.5 4.0

B R2=0.61 nRMSE=8.84%

ZS3.3 5.0 4.5 4.0

ZS4.5

R2=0.59 nRMSE=8.89%

ZS6.3

ZS7.5 C 5.0 4.5 4.0

3.5

3.5

3.5

3.0

3.0

3.0

2.5

2.5

2.5

2.0

2.0

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1.5 1.5 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 Measured TLNC (%)

R2=0.53 nRMSE=9.37%

1.5 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Fig. 7 Validation of estimated total leaf nitrogen concentration (TLNC) with vertical N distribution (VND) method in wheat using represented spectral indices: greenness index (GI, A), modified normalized difference 705 (mND705, B), and normalized difference vegetation index (NDVI, C) (n=144). ZS, Zadoks growth stage. R2, determining coefficient; nRMSE, normalized root mean square error.

DUAN Dan-dan et al. Journal of Integrative Agriculture 2019, 18(7): 1562–1570

also across different growth stages, which correlates with leaf growth and N allocation. For example, at ZS4.5, the upper first leaf unfolded and the N allocation to this leaf increased, so the LNC of L1 was not greater than that of L2 (Fig. 2). However, as the wheat grew, leaf N was allocated more into the upper layer, and the LNC in the different layers decreased from the upper layer to the lower layer (Wang et al. 2012). Correlations between spectral information and the various upper layers show that LNC of the upper layer in wheat canopy with VIs is more sensitive than the TLNC of the entire canopy (Tables 3 and 4), except for the relationship in ZS3.3. At ZS3.3, stems did not begin to elongate, so the entire wheat canopy could be subjected to remote sensing. In other growth stages, such as ZS4.5 and ZS6.3, the optimized relationship between spectral information occured in the upper three layers of the wheat canopy. This may be attributed to the structure of the plant, which caused depressions in the upper leaves and shading of the lower leaves. Luo et al. (2016) found that the estimate of the N concentration of the three upper reed layers based on PPR and NDVI had optimal accuracy, which is consistent with the present results. However, Luo et al. (2016) only demonstrated a single growth stage in reed, and did not show the tendency of the remotely sensed layer at other growth stages. At the filling stage ZS7.5 in wheat, the optimized remote sensing layer with upper LNC was the upper first layer. These results are attributed mainly to: (1) an increasing proportion of the wheat spike affecting the spectral data, and (2) the wheat blade curling because of reduced leaf water content, which increases the interruption of the lower leaves. A correlation is used to evaluate the sensitivity of the spectral information obtained from the remote sensed layer. Further studies will evaluate the index or research method (e.g., removal of leaves from base to top). Compared with the linear TLNC model, the TLNC using VND method increases the final accuracy: the underestimation at high TLNC and the overestimation at low TLNC are reduced (Fig. 6). According to the above correlations between different upper layers and spectral information, remote-sensing data did not reflect all the canopy information (e.g., only the upper three layers have maximum correlation at ZS4.5 and ZS6.3), so the phenomenon of overestimation or underestimation exists only when using linear models. However, the TLNC model with the VND method considers the optimized remotely sensed canopy layer, and one calibration index β is used to adjust the relationship between the remotely sensed layer and the entire canopy. Additionally, the relationship showed low accuracy between observed TLNC and estimated TLNC at one growth stage from validated dataset (Fig. 7), although

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underestimation at high TLNC and the overestimation at low TLNC are reduced (Fig. 6). This could be explained by the set of β value, one value at one growth stage, in the VND model, where the value is also different at one growth stage influenced by various cultivars and management treatments. Furthermore, only four growth stages were tested in winter wheat and each growth stage was given a single value for β. One dynamic β value changed by growth stages needs to further investigation. Therefore, the simple VND model was constructed to correct the TLNC estimation from hyperspectral data in this study, and more complex and supported factors, e.g., growth stages, management treatments and cultivars, should be taken into account in the following studies. There is also an opportunity for further studies to link the VND method and satellite imagery to regional TLNC monitoring.

5. Conclusion In this study, we constructed a TLNC model with the VND to improve estimates of LNC based on remote sensing of the upper layer of the wheat canopy. We used this model to estimate the TLNC in winter wheat. The results led to the following major conclusions: (1) The effective layer for remote sensing detection varies at different growth stages. The effective layers are the entire canopy and the top layer in the jointing and filling stages, respectively. The three upper layers are the effective layers in the flag leaf and flowering stages. (2) At each growth stage, VIs are more closely related to the N concentration in the effective layers (GI, r=0.79; MND705, r=0.79; NDVI, r=0.79), than to the canopy total N concentration, which does not consider the stratification of leaf structure (GI, r=0.75; mND705, r=0.72; NDVI, r=0.71). (3) The nRMSE between the estimated and measured TLNC with the VND method based on GI, mND705, and NDVI is 8.84, 8.89, and 9.37%, respectively.

Acknowledgements This study was supported by the Natural Science Foundation of Beijing Academy of Agriculture and Forestry Sciences (BAAFS), China (QNJJ201834), the National Natural Science Foundation of China (41471285 and 41671411), and the National Key R&D Program of China (2017YFD0201501). We are grateful to Mr Li Weiguo and Ms Chang Hong from BAAFS for data collection.

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Executive Editor-in-Chief LI Shao-kun Managing editor WANG Ning