Scientia Agricultura Sinica ›› 2020, Vol. 53 ›› Issue (18): 3679-3692.doi: 10.3864/j.issn.0578-1752.2020.18.005

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

Using Canopy Time-Series Vegetation Index to Predict Yield of Winter Wheat

XIANG FangLin(),LI XinGe,MA JiFeng,LIU XiaoJun,TIAN YongChao,ZHU Yan,CAO WeiXing,CAO Qiang()   

  1. Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture/Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Engineering and Research Center for Smart Agriculture, Ministry of Education/Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095
  • Received:2019-12-09 Accepted:2020-03-12 Online:2020-09-16 Published:2020-09-25
  • Contact: Qiang CAO E-mail:2017101003@njau.edu.cn;qiangcao@njau.edu.cn

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Abstract:

【Objective】The research elaborated the dynamic change trend of canopy time series vegetation index in winter wheat. The yield prediction model was constructed based on time series vegetation index in wheat, which provided effective technical support for obtaining crop yield information timely and accurately. 【Method】 From 2017 to 2019, the field experiments involving different nitrogen (N) rates and varieties were conducted in Ten Thousand Acres Grain Industrial Park located in Xinghua, Jiangsu province. The normalized difference red edge (NDRE) and normalized difference vegetation index (NDVI) were obtained from the active canopy sensor RapidSCAN CS-45. The curve of time-series vegetation index was fitted based on the double logistic function, and the characteristic parameters of curve were extracted. The correlation between each characteristic parameters and yield were analyzed. The yield estimation models were verified with independent test data. 【Result】 The results of the study indicated that the relationship between NDRE and yield was performed well at booting and heading stage, and R2 of them was 0.86 and 0.85, respectively. The results of multiple stepwise linear regression showed that the yield prediction models could be improved using NDRE of two or more growth stages, compared with using single growth stage information. The first and second selected periods were jointing and booting stage, respectively. Based on the relative NDRE (RNDRE) and relative NDVI (RNDVI) of the whole growth period, the time-series curve was constructed and the yield prediction models were developed using the characteristic parameters of the curve. The maximum value, accumulative value and growth rate of RNDRE and RNDVI time series curve had a good relationship with the yield. The yield prediction models based on the maximum and accumulative values of RNDRE performed satisfactorily with validation using independent data, the R2 was greater than 0.80, and the relative root mean square error and relative error were less than 10%. The validation effect was better than NDRE-based prediction model with the single-period or multi-period, which was better than NDVI-based yield predicted model. 【Conclusion】 The maximum and the accumulative RNDRE extracted from the canopy time series vegetation index had a good potential to estimate the yield, which provided technical support for real-time and accurate yield prediction in the field.

Key words: winter wheat, canopy sensor, time series vegetation index, yield, prediction model

Fig. 1

Map of experiment 1"

Table 1

Coefficients of determination (R2) for the relationship between vegetation indices (NDRE and NDVI) and yield at different growth stages across varieties and years"

植被指数
Vegetation index		 品种
Variety		 样本
Sample		 拔节期
Jointing stage		 孕穗期
Booting stage		 抽穗期
Heading stage		 开花期
Flowering stage		 灌浆期
Filling stage
NDRE 镇麦12 Zhenmai 12 30 0.66 0.89 0.88 0.86 0.89
扬麦23 Yangmai 23 30 0.46 0.83 0.82 0.75 0.84
宁麦13 Ningmai 13 30 0.72 0.92 0.92 0.91 0.91
所有品种 All 90 0.60 0.86 0.85 0.81 0.81
NDVI 镇麦12 Zhenmai 12 30 0.62 0.79 0.81 0.84 0.83
扬麦23 Yangmai 23 30 0.54 0.79 0.75 0.77 0.75
宁麦13 Ningmai 13 30 0.81 0.86 0.81 0.84 0.85
所有品种 All 90 0.63 0.79 0.77 0.80 0.76

Table 2

Stepwise multiple linear regression models based on vegetation indices for estimating winter wheat yield at different growth stages"

植被指数
Vegetation index		 回归方程
Regression equation		 决定系数
R2		 相对均方根误差
RRMSE (%)		 相对误差
RE (%)		 赤池信息准则
AIC
NDRE y= -2.16x1+16.83x2+9.15x3-4.40x4-2.35x5-0.75 0.86 8.98 9.82 -97.95
y= -2.85x1+17.69x2+5.67x3-3.23x5-0.90 0.86 8.99 9.84 -99.74
y= -2.54x1+21.94x2-2.14x5-0.81 0.86 9.02 9.82 -101.37
y= -2.53x1+19.90x2-0.75 0.86 9.02 9.84 -103.01
NDVI y=0.14x1+11.23x2-13.22x3+23.12x4-3.57x5-8.31 0.81 10.41 11.70 -71.45
y=11.25x2-13.36x3+23.27x4-3.38x5-8.36 0.81 10.40 11.76 -73.44
y=12.21x2-14.79x3+19.45x4-7.45 0.81 10.43 11.62 -74.96

Fig. 2

Dynamic changes of NDRE based on AGDD with different nitrogen rates The red dots indicate the tests at the greening stage, jointing stage, booting stage, heading stage, flowering stage, filling stage and maturity stage. A: Zhenmai12; B: Yangmai23; C: Ningmai13; D: Zhenmai12; E: Yangmai23; F: Ningmai13. The same as below"

Fig. 3

Dynamic changes of NDVI based on AGDD with different nitrogen rates"

Fig. 4

The dynamic changes of time-series curve of RNDRE (A) and RNDVI (B) with different nitrogen rates"

Table 3

The fitting parameters of time-series curve of RNDRE and RNDVI based on RAGDD with different nitrogen rates"

植被指数 Vegetation index 氮处理
N treatment		 曲线参数 Parameters of curves 决定系数
R2		 均方根误差
RMSE
A1 A2 a b c d
RNDRE N0 0.655 0.506 7.10 0.21 11.84 0.84 0.78 0.066
N1 0.876 0.714 8.97 0.27 14.25 0.85 0.91 0.063
N2 0.969 0.791 11.42 0.28 15.04 0.88 0.96 0.047
N3 0.988 0.786 12.74 0.28 15.51 0.90 0.97 0.043
N4 0.995 0.788 12.70 0.28 15.10 0.90 0.97 0.043
RNDVI N0 0.837 0.668 5.85 0.18 14.29 0.86 0.79 0.082
N1 0.956 0.785 8.80 0.23 15.70 0.88 0.91 0.062
N2 0.987 0.776 11.59 0.23 16.60 0.92 0.94 0.053
N3 0.997 0.760 12.29 0.24 16.22 0.93 0.96 0.044
N4 0.996 0.754 12.46 0.24 15.84 0.93 0.95 0.045

Table 4

The characteristic parameters of time-series curve of RNDRE and RNDVI with different nitrogen levels"

氮处理
N treatment		 RNDRE RNDVI
最大值
Maximum		 累积值
Accumulative value		 增长速率
Increase rate		 下降速率
Decrease rate		 最大值
Maximum		 累积值
Accumulative value		 增长速率
Increase rate		 下降速率
Decrease rate
N0 0.582 0.349 0.721 1.187 0.750 0.456 0.772 1.693
N1 0.809 0.464 1.245 1.841 0.911 0.558 1.186 2.091
N2 0.931 0.550 1.598 2.037 0.968 0.623 1.396 2.039
N3 0.963 0.580 1.731 2.028 0.981 0.637 1.452 1.983
N4 0.968 0.583 1.737 2.018 0.980 0.639 1.488 1.885

Table 5

The characteristic parameters of time-series RNDRE and RNDVI curve based prediction models of winter wheat yield"

植被指数
Vegetation index		 特征参数
Characteristic parameter		 单产预测模型
Yield prediction model		 R2 RRMSE
(%)		 RE
(%)		 AIC
RNDRE 最大值 Maximum y = 8.72x - 1.37 0.80 10.2 12.9 62.29
累积值 Accumulative value y = 14.39x - 1.22 0.82 9.9 12.9 60.41
增长速率 Increase rate y = 3.32x + 1.38 0.82 9.9 12.8 60.35
下降速率 Decrease rate y = 3.73x - 0.75 0.73 12.0 14.3 71.98
RNDVI 最大值 Maximum y = 14.24x - 7.03 0.77 11.0 13.4 66.48
累积值 Accumulative value y = 18.24x - 4.58 0.80 10.2 12.9 62.18
增长速率 Increase rate y = 4.82x - 0.02 0.81 10.2 12.8 61.84
下降速率 Decrease rate y = 5.04x - 3.72 0.22 20.3 25.6 103.41

Table 6

Validation results of yield prediction model based on NDRE and NDVI in winter wheat"

植被指数
Vegetation index		 模型
Model		 单产预测模型
Yield prediction model		 R2 RRMSE
(%)		 RE
(%)
RNDRE 单生育时期线性回归单产模型
Yield model based on linear regression in single growth stage		 拔节期 Jointing y=12.47x+1.67 0.59 24.3 23.5
孕穗期 Booting y =17.34x-0.65 0.79 18.7 18.2
抽穗期 Heading y =17.46x-0.73 0.77 17.4 16.6
开花期 Flowering y =17.97x-0.87 0.79 17.7 16.7
灌浆期 Filling y =17.46x+0.13 0.71 15.5 14.4
多元逐步线性回归模型
Yield model based on stepwise multiple linear regression		 y= -2.16x1+16.83x2+9.15x3-4.40x4-2.35x5-0.75 0.81 17.6 17.3
y= -2.85x1+17.69x2+5.67x3-3.23x5-0.90 0.80 17.8 17.6
y= -2.54x1+21.94x2-2.14x5-0.81 0.78 18.3 18.2
y= -2.53x1+19.90x2-0.75 0.79 17.7 17.4
RNDVI 单生育时期线性回归单产模型
Yield model based on linear regression in single growth stage		 拔节期 Jointing y = 8.8027x - 0.4221 0.56 14.2 17.2
孕穗期 Booting y = 11.822x - 3.4517 0.73 13.6 14.4
抽穗期 Heading y = 12.877x - 4.3125 0.68 16.1 17.4
开花期 Flowering y = 17.707x - 8.1348 0.73 17.5 19.5
灌浆期 Filling y = 13.383x - 3.9326 0.75 16.4 15.3
多元逐步线性回归模型
Yield model based on stepwise multiple linear regression		 y=0.14x1+11.23x2-13.22x3+23.12x4-3.57x5-8.31 0.69 16.6 19.6
y=11.25x2-13.36x3+23.27x4-3.38x5-8.36 0.69 16.7 19.7
y=12.21x2-14.79x3+19.45x4-7.45 0.68 16.3 18.2

Table 7

Validation results of evaluation with winter wheat yield prediction models based on the characteristic parameters of time-series RNDRE and RNDVI curve"

特征参数
Characteristic parameter		 RNDRE RNDVI
R2 RRMSE (%) RE (%) R2 RRMSE (%) RE (%)
最大值 Maximum 0.81 8.0 8.4 0.65 12.1 15.1
累积值 Accumulative value 0.80 9.6 9.9 0.72 16.9 15.9
增长速率 Increase rate 0.36 19.8 20.8 0.01 78.1 90.8
下降速率 Decrease rate 0.44 24.3 23.7 0.07 46.0 44.8

Fig. 5

Validation results of evaluation with winter wheat yield prediction models based on the characteristic parameters of time-series RNDRE (A: Maximum; B: Accumulative value) and RNDVI (C: Maximum; D: Accumulative value) curve "

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