Scientia Agricultura Sinica ›› 2021, Vol. 54 ›› Issue (21): 4525-4538.doi: 10.3864/j.issn.0578-1752.2021.21.004

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Determination of Suitable Band Width for Estimating Rice Nitrogen Nutrition Index Based on Leaf Reflectance Spectra

XU HaoCong1(),YAO Bo1,WANG Quan1,CHEN TingTing1,ZHU TieZhong1,HE HaiBing1,KE Jian1,YOU CuiCui1,WU XiaoWen2,GUO ShuangShuang3,WU LiQuan1,4,*()   

  1. 1College of Agronomy, Anhui Agricultural University, Hefei 230036
    2Lujiang County Agricultural Technology Extension Center, Lujiang 231500, Anhui
    3Zoomlion Intelligent Agriculture Co. Ltd., Wuhu 241000, Anhui
    4Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095
  • Received:2020-11-23 Accepted:2021-02-01 Online:2021-11-01 Published:2021-11-09
  • Contact: LiQuan WU E-mail:861389737@qq.com;wlq-001@163.com

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Abstract:

【Objective】The research aimed to analyze the relationship between rice (Oryza sativa L.) nitrogen nutrition index (NNI) and leaf spectral reflectance characteristics of leaf on different positions, so as to provide an effective method for nondestructive and timely evaluation of NNI in rice.【Method】Field experiments were conducted with different N application rates and rice cultivars across two growing seasons during 2018-2019, and the leaf hyperspectral reflectance of 350-2 500 nm of leaf on different positions and the plant NNI were measured during key fertility growth stages to construct a variety of spectral index model for rice NNI monitoring.【Result】The results indicated that green band (540 nm) at leaf level was the sensitive band for estimating NNI, and narrow band ratio index SR (R900, R540) composed of near infrared band and green band could be used to retrieve NNI of rice. However, the prediction accuracy of narrow band ratio index and rice NNI of leaf on different positions were different. In terms of prediction accuracy, the best single leaf position was the third leaf (L3) from the top (R2=0.731, RMSE=0.130, RE=11.6%), the second leaf (L2) from the top followed (R 2=0.707, RMSE=0.136, RE=12.2%), and the top one (L1) was the worst (R 2=0.443, RMSE=0.187, RE=14.7%). The averaged spectra of L2 and L3 (L23) was the optimum leaf spectra combination, which contributed to improving the predictability to NNI(R 2=0.740, RMSE=0.128, RE=11.5%). The samples were resampled at 50 nm and 10 nm in the near infrared region (900 nm) and green region (540 nm) respectively, and the accuracy of the wide band ratio index SR (AR(900±50), AR(540±10)) was not significantly lower than that of SR (R900, R540). The model accuracy and prediction accuracy of the two models were basically the same at L23. When the NNI of rice was less than 1, there was a significant positive linear correlation with the yield, and then it tended to be stable. 【Conclusion】The results showed that the reflectance spectra of L2 and L3 leaves were sensitive for monitoring NNI for rice, and L23 could improve the prediction accuracy of the model. Multiple band ratio indices SR (R900, R540) and SR (AR(900±50), AR(540±10)) based on leaf reflectance spectra could be used to rapidly estimate rice NNI, which provided a theoretical basis for monitoring rice NNI with various sensors.

Key words: leaf, rice(Oryza sativa L.), nitrogen nutrition index, ratio index, model, band width

Table 1

Soil information about experiments"

年份
Year		 有机质
Organic matter (g·kg-1)		 全氮
Total N (g·kg-1)		 有效磷
Available P (mg·kg-1)		 速效钾
Available K (mg·kg-1)		 pH
2018 32.36 2.03 24.80 211.42 5.11
2019 30.89 1.77 25.97 245.52 5.54

Fig. 1

Relationship between nitrogen nutrition index and yield in rice (2018-2019) ** indicates significance at 0.01 level"

Table 2

List of hyperspectral indices used in this study"

光谱指数
Spectral index		 计算公式
Algorithm		 参考文献
Reference
比值光谱指数 Simple ratio spectral index (SR) R1 / R2 本研究 This study
归一化差值光谱指数 Normalized difference spectral index (ND) (R1 - R2)/(R1 + R2) 本研究This study
修正红边归一化指数
Modified red edge normalized difference vegetation index (MSR 705)		 (R750-R705)/(R750+2×R445) [12]
红边归一化指数 Red edge normalized difference vegetation index (ND 705) (R750- R705)/(R750+ R705) [13]
比值指数-ldB Ratio index-1dB (RI-ldB) R735 /R720 [14]
Vogelmann 红边指数 Vogelman red edge index (VOG) R740 / R720 [15]
双峰冠层氮指数 Double-peak canopy nitrogen index (DCNI) (R720-R700)/(R700-R670)/(R720-R670+0.03) [16]
线性内插法红边位置 Red edge position: linear interpolation method (REPLI) 700+40×[(R670+R780)/2-R700] /(R740-R700) [17]
比值植被指数II Ratio Vegetation Index Ⅱ(RVI Ⅱ) R810 / R560 [18]

Table 3

Analysis of variance of NNI among varieties, nitrogen treatments, and growth stage, respectively"

分组因子
Grouping factor		 变异来源
Variation source		 平方和
Sum of squares		 自由度
df		 均方
Mean square		 F值
F-value		 P值
P-value
氮肥处理
Nitrogen rate		 组间 Between groups 2.643 4 0.661 31.745 0
组内 Within groups 1.561 75 0.021
总计 Total 4.203 79
品种
Variety		 组间 Between groups 0.231 3 0.077 1.475 0.228
组内 Within groups 3.972 76 0.052
总计 Total 4.203 79
生育期
Growth stage		 组间 Between groups 0.094 1 0.094 1.782 0.186
组内 Within groups 4.109 78 0.053
总计 Total 4.203 79

Fig. 2

Dynamic changes of NNI under different nitrogen application rates of rice plants"

Fig. 3

Leaf spectral reflectance under different nitrogen application rates"

Fig. 4

Correlation between NNI and leaf spectral reflectance of different position leaves of rice plants L1: The top leaf; L2: The second leaf; L3: The third leaf; L12: The averaged spectra of L1 and L2; L23: The averaged spectra of L2 and L3; L13: The averaged spectra of L1 and L3; L123: The averaged spectra of L1, L2 and L3"

Table 4

Correlation coefficient between typical spectral index and rice NNI at different growth stages of rice plants"

时期 Stage 叶位 Leaf position MSR705 ND705 RI-1dB VOG DCNI REPLI RVI Ⅱ
分蘖期
Tillering stage		 顶1叶（L1） 0.483** 0.534** 0.561** 0.564** 0.607** 0.478** 0.553**
顶2叶（L2） 0.555** 0.479** 0.444** 0.443** 0.367** 0.436** 0.563**
顶3叶（L3） 0.311** 0.394** 0.406** 0.409** 0.718** 0.488** 0.398**
顶1、2叶平均（L23） 0.441** 0.456** 0.446** 0.448** 0.555** 0.488** 0.499**
拔节期
Jointing stage		 顶1叶（L1） 0.568** 0.541** 0.538** 0.536** 0.253** 0.465** 0.657**
顶2叶（L2） 0.717** 0.665** 0.648** 0.649** 0.770** 0.538** 0.792**
顶3叶（L3） 0.765** 0.742** 0.718** 0.717** 0.652** 0.669** 0.810**
顶2、3叶平均（L23） 0.791** 0.748** 0.721** 0.721** 0.721** 0.655** 0.835**
孕穗期
Booting stage		 顶1叶（L1） 0.620** 0.635** 0.711** 0.712** 0.278** 0.629** 0.686**
顶2叶（L2） 0.781** 0.812** 0.833** 0.835** 0.476** 0.772** 0.859**
顶3叶（L3） 0.730** 0.755** 0.758** 0.758** 0.485** 0.714** 0.783**
顶2、3叶平均（L23） 0.763** 0.790** 0.807** 0.808** 0.490** 0.749** 0.837**
抽穗期
Heading stage		 顶1叶（L1） 0.462** 0.511** 0.510** 0.516** 0.519** 0.557** 0.521**
顶2叶（L2） 0.586** 0.652** 0.684** 0.690** 0.714** 0.592** 0.730**
顶3叶（L3） 0.546** 0.589** 0.635** 0.643** 0.600** 0.432** 0.706**
顶2、3叶平均（L23） 0.593** 0.649** 0.696** 0.703** 0.646** 0.538** 0.734**

Table 5

Correlation between existing spectral index and NNI in rice"

叶位 Leaf position MSR705 ND705 RI-1dB VOG DCNI REPLI RVI Ⅱ
顶1叶（L1） 0.571** 0.576** 0.570** 0.572** 0.477** 0.584** 0.621**
顶2叶（L2） 0.708** 0.726** 0.741** 0.745** 0.575** 0.710** 0.798**
顶3叶（L3） 0.629** 0.660** 0.698** 0.703** 0.577** 0.543** 0.739**
顶2、3叶平均（L23） 0.676** 0.702** 0.733** 0.738** 0.587** 0.642** 0.781**
样本量 160 个 Sample volume is 160

Fig. 5

Contour maps of determination coefficients (R2) between NNI and normalized difference (ND) and ratio (SR) spectral indices based on two wavebands-combination in rice"

Fig. 6

Contour maps of RMSE and RE values using simple ratio index or predicting NNI in rice (n=90)"

Table 6

Quantitative relationships between NNI (y) and different spectral index (x) in rice (n=249)"

叶位
Leaf position		 光谱指数
Spectral index		 回归方程
Regression equation		 模型精度
Model precision (R2)		 预测精度
Prediction precision (R2)		 均方根误
RMSE		 相对误差
RE (%)
顶一叶（L1） SR（R900,R540） y=0.2488x+0.1267 0.390 0.443 0.187 14.7
SR[AR（900±50）,AR（540±10）] y=0.2454x+0.1307 0.388 0.441 0.187 14.7
顶二叶（L2） SR（R900,R540） y=0.3943x-0.3199 0.657 0.707 0.136 12.2
SR[AR（900±50）,AR（540±10）] y=0.3896x-0.3154 0.654 0.706 0.136 12.2
顶三叶（L2） SR（R900,R540） y=0.3019x-0.0364 0.557 0.731 0.130 11.6
SR[AR（900±50）,AR（540±10）] y=0.2988x-0.0344 0.556 0.729 0.130 11.6
顶二、顶三光谱平均（L23） SR（R900,R540） y=0.3556x-0.1997 0.625 0.740 0.128 11.5
SR[AR（900±50）,AR（540±10）] y=0.3517x-0.1967 0.624 0.740 0.128 11.5
顶一、顶二、顶三光谱平均（L123） SR（R900,R540） y=0.3645x-0.2208 0.618 0.720 0.133 11.2
SR[AR（900±50）,AR（540±10）] y=0.3602x-0.217 0.616 0.718 0.133 11.3

Fig. 7

Comparison between predicted and observed values of NNI of rice plants (L23; n=249)"

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