Crops ›› 2019, Vol. 35 ›› Issue (3): 126-131.doi: 10.16035/j.issn.1001-7283.2019.03.020

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Estimation Models of Maize Leaf SPAD Value Based on Hyperspectral Remote Sensing

Dong Zhe,Yang Wude,Zhang Meijun,Zhu Hongfen,Wang Chao   

  1. Institute of Dry Farming Engineering, Shanxi Agricultural University, Taigu 030801, Shanxi, China
  • Received:2018-09-21 Revised:2019-04-04 Online:2019-06-15 Published:2019-06-12
  • Contact: Wude Yang

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Abstract:

The leaf chlorophyll content at the filling stage plays an important role in photosynthesis and yield formation of maize. To estimate the maize leaf chlorophyll content accurately and efficiently, hyperspectral remote sensing was used by taking SPAD value as the relative chlorophyll content. Conventional regression models based on spectral characteristic parameters, PLSR models based on full spectrum and spectral characteristic parameters, and BP neural network model were constructed and compared. The results showed that SPAD value of PLSR model based on full spectrum had the best fitting effect (R 2=0.910, RMSE=2.071). The fitting effect of PLSR model based on spectral characteristic parameters was close to that of PLSR model based on full spectrum. But the measured and predicted values of the latter fitting effect (R 2=0.867, RMSE=2.581, RPD=2.628) was better than that of full spectrum PLSR model, and the PLSR model based on spectral characteristic parameters had short modeling time and tow complexity. The prediction effect of the BP neural network model was worse than the two PLSR models, but better than the conventional regression models based on spectral characteristic parameters. In brief, the estimation effect of PLSR model based on spectral parameters was the best.

Key words: Hyperspectral, Maize leaf, SPAD, Partial least square regression, BP neural network

Table 1

Spectral characteristic parameters formulas used in the study"

光谱特征参数Spectral characteristic parameter 定义Definition 来源Source
NDVI NDVI=(R750-R705)/(R750+R705) Gitelson等[11]
GNDVI GNDVI=(R750-R550)/(R750+R550) Gitelson等[11]
RVI RVI=R760/R710 Penuelas等[12]
λr 红光范围内一阶导数光谱最大值对应的波长 Horler等[13]
kλr 红光范围内一阶导数光谱的峰度 姚付启等[14]
sλr 红光范围内一阶导数光谱的偏度 姚付启等[14]

Table 2

Correlation coefficients between the spectral characteristic parameters and SPAD value of maize leaves (n=80)"

参数Parameter 相关系数
Correlation coefficient
植被指数Vegetation index NDVI 0.843**
GNDVI 0.811**
RVI 0.893**
红边参数The trilateral parameter λr 0.935**
kλr 0.872**
sλr 0.914**

Table 3

SPAD value estimation model of maize leaves based on the spectral indexes"

光谱特征参数
Spectral characteristic parameter		 最优拟合模型
The optimal fitting equation		 建模集Modeling set 验证集Validation set
R2 RMSE R2 RMSE RPD
NDVI y=12.696e2.6521x 0.728 3.686 0.636 4.072 1.500
GNDVI y=10.909e2.7102x 0.672 4.020 0.574 4.366 1.340
RVI y=20.758x0.9129 0.812 3.095 0.716 3.715 1.791
λr y=3E-104x36.805 0.880 3.774 0.854 3.327 2.005
kλr y=640.3e1.7036x 0.784 3.432 0.714 3.657 1.739
sλr y=87.296x2+54.674x+46.509 0.847 2.697 0.804 3.028 2.137

Table 4

SPAD value estimation model of maize leaves based on PLSR model"

模型Model 建模集
Modeling set		 验证集
Validation set
R2 RMSE R2 RMSE RPD
PLSR（全谱） 0.910 2.071 0.862 2.600 2.545
PLSR（光谱特征参数） 0.900 2.200 0.867 2.581 2.628

Table 5

SPAD value estimation model of maize leaves based on BP neural network"

建模集Modeling set 验证集Validation set
R2 RMSE R2 RMSE RPD
0.904 2.165 0.822 2.861 2.305

Table 6

Comparison of prediction effects of different estimation models"

模型
Model		 建模集Modeling set 验证集Validation set
R2 RMSE R2 RMSE RPD
PLSR（光谱特征参数）PLSR (spectral characteristic parameter) 0.900 2.200 0.867 2.581 2.628
PLSR（全谱）PLSR (full spectrum) 0.910 2.071 0.862 2.600 2.545
BP神经网络BP neural network 0.904 2.165 0.822 2.861 2.305
多项式Polynomial (sλr) 0.847 2.697 0.804 3.028 2.137

Fig.1

The relationships between measured value and predicted value of SPAD based on different estimation models a. PLSR (spectral characteristic parameter), b. PLSR (full spectrum), c. BP neural network, d. Polynomial (sλr)"

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