基于高光谱的油菜叶片SPAD值估测模型比较

王克晓; 周蕊; 李波; 欧毅; 黄祥; 虞豹

doi:10.19303/j.issn.1008-0384.2021.11.003

基于高光谱的油菜叶片SPAD值估测模型比较

doi: 10.19303/j.issn.1008-0384.2021.11.003

重庆市农业科学院农业科技信息研究所，重庆　401329

基金项目: 重庆市技术创新与应用发展专项（cstc2019jscx-msxmX0355、cstc2020jscx-lyjsAX0012）；重庆市农业科学院农业发展资金项目（NKY-2020AB009、NKY-2021AC012）

详细信息

作者简介:
王克晓（1986−），男，硕士，助理研究员，主要从事农业遥感相关研究(E-mail：447215670@qq.com)

通讯作者:
周蕊(1980−)，女，硕士，副研究员，主要从事农业信息化研究（E-mail：12087836@qq.com）

中图分类号: S 565.4; S 127
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出版历程
- 收稿日期: 2021-07-14
- 修回日期: 2021-10-11
- 网络出版日期: 2021-12-30
- 刊出日期: 2021-11-28

Comparison of Estimation Models for Hyperspectral-based Rape Leaf SPAD

Institute of Agricultural Science and Technology Information, Chongqing Academy of Agricultural Sciences, Chongqing　401329, China

摘要

摘要: 目的比较基于高光谱参数的油菜叶片SPAD值估算模型效果。方法在分析光谱反射特征和光谱参数与SPAD值相关性的基础上，利用光谱特征参数优选并构建了偏最小二乘回归（PLSR）、传统反向传播神经网络（BPNN）、支持向量回归（SVR）和深度学习神经网络（DNN）等模型对叶片样本叶绿素SPAD值进行估测。结果 ①叶片原始光谱与叶片SPAD值在425～495 nm的蓝波、665～680 nm的红波区域呈现微弱正相关，与红边波段均呈现负相关，并在510～650 nm的绿、黄波段和690～735 nm的红边波段显著负相关；②与叶片SPAD值显著线性相关的S_Db与S_Dy、CARI与MCARI、CI与NDVI₇₀₅等三组光谱特征的组内参数具有一定的可替代性，而且有助于提高SPAD模型预测精度；③基于高光谱参数的深度学习DNN模型决定系数R²为0.93，RPD为3.92，具有较高的预测能力，SVR模型次之，PLSR和BPNN模型效果一般。结论油菜叶片光谱参数之间存在不同程度的相关性，基于机器学习的非线性估计模型具有较高的稳定性和预测能力，深度学习算法在油菜叶片叶绿素SPAD值估测方面具有更好的估测能力。
- 高光谱 /
- 叶绿素 /
- 机器学习 /
- 油菜
Abstract: Objective In order to compare the estimation model effect of SPAD of rape leaves based on hyperspectral parameters. Method Models of partial least squares regression (PLSR), back propagation neural network (BPNN), support vector regression (SVR), and deep neural network (DNN) based on the spectral parameters selected from the correlation analysis between the spectral reflectance parameters and SPAD data were constructed and compared for the estimation of chlorophyll SPAD of rape leaves. Result The SPADs and the original spectra in the blue wave of 425-495 nm and red wave of 665-680 nm of the leaves had a weak positive correlation. However, significantly inverse correlations between the SPADs and the green-yellow band of 510-650 nm and between that and the red edge band of 690-735nm were observed. The negative correlation coefficient between S_Db and S_Dy was as high as −0.98, while the positive correlation coefficient between CARI and MCARI, CI and NDVI₇₀₅ 0.99. The 3 sets of S_Db and S_Dy, CARI and MCARI, and CI and NDVI₇₀₅ had significant linear correlations with the leaf SPAD. They could be somewhat interchangeable rendering them potential for accuracy improvement. The DNN model had an R² of 0.93 and an RPD of 3.92 indicating a high predictability of the two models. They were followed by SVR, while PLSR and BPNN models being similar. Conclusion There were different degrees of correlation between the SPADs and the spectral parameters of rape leaves. The non-linear prediction model based on machine learning showed higher stability and predictability than the others, and the deep learning algorithm more effective in estimating SPAD of rape leaves.
- hyperspectral /
- chlorophyll /
- machine learning /
- rape

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图 1 叶片光谱与SPAD值相关性

Figure 1. Correlation between spectra and SPAD of leaves

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图 2 光谱参数及与SPAD值的相关性

注：×表示在0.01的检验水平上显著不相关。

Figure 2. Correlation between spectral parameters and SPAD

Note: × means significantly irrelevant at p<0.01.

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图 3 叶片SPAD值PLSR模型

Figure 3. PLSR model of leaf SPAD

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图 4 叶片SPAD值DNN回归模型

Figure 4. DNN model on rape leaf SPAD

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表 1 光谱参数及其定义或计算公式

Table 1. Spectral parameters and definitions or calculation formula

类型 Type	特征变量 Characteristic variable	定义或算法 Definition or algorithm
位置特征 Location feature	D_b（蓝边幅值） Blue edge amplitude	490～530 nm一阶微分最大值 Maximal 1^st-order differential 490-530 nm
	D_y（黄边幅值） Yellow edge amplitude	560～640 nm一阶微分最大值 Maximal 1^st-order differential 560-640 nm
	D_r（红边幅值） Red edge amplitude	680～760 nm一阶微分最大值 Maximal 1^st-order differential 680-760 nm
	R_g（绿峰反射率） Green peak reflectance	510～560 nm内光谱反射率最大值 Maximal reflectance 510-560 nm
	R_r（红谷反射率） Red valley reflectance	640～680 nm内光谱反射率最小值 Minimal reflectance 640-680 nm
面积特征 Area feature	S_Db（蓝边面积） Blue edge area	波长490～530 nm一阶导数光谱积分 1^st-derivative integral at 490-530nm
	S_Dy（黄边面积） Yellow edge area	波长560～640 nm一阶导数光谱积分 1^st-derivative integral at 560-640nm
	S_Dr（红边面积） Red edge area	波长680～760 nm一阶导数光谱积分 1^st-derivative integral at 680-760nm
植被指数 Vegetation index	CARI（叶绿素吸收比） Chlorophyll absorption reflectance index	（R₇₀₀−R₆₇₀）−0.2×（R₇₀₀−R₅₅₀）
	MCARI（改进叶绿素吸收比） Modified chlorophyll absorption reflectance index	[（R₇₀₀−R₆₇₀）−0.2×（R₇₀₀−R₅₅₀）]×（R₇₀₀/R₆₇₀）
	CI（红边叶绿素指数） Red edge chlorophyll index	R₇₅₀/R₇₀₅−1
	NDVI₇₀₅（红边归一化植被指数） Red edge normalized difference vegetation index	（R₇₅₀−R₇₀₅）/（R₇₅₀+R₇₀₅）
	NPCI（归一化总色素叶绿素指数） normalized total pigment to chlorophyll index	（R₆₈₀−R₄₃₀）/（R₆₈₀+R₄₃₀）

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表 2 深度神经网络算法参数

Table 2. Parameters applied for deep neural network algorithm

参数 Parameter	取值 Value	参数 Parameter	取值 Value
隐层层数（Hidden）	2	节点（Nodes）	c(5，5)
激活函数（Activation）	Rectifier	停止测度（Stopping metric）	方均误差（MSE）
停止容差（Stopping tolerance）	0.02	周期（Epochs）	500

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表 3 不同模型精度比较

Table 3. Accuracy of models

模型 Models	建模精度 Modeling accuracy			测试精度 Test accuracy
模型 Models	R²	RMSE	RPD	R²	RMSE
PLSR	0.66（0.61）	1.76（1.86）	1.69（1.61）	0.63（0.58）	1.77（1.96）
BPNN	0.72（0.69）	1.58（1.68）	1.89（1.78）	0.70（0.67）	2.02（2.22）
SVR	0.80（0.91）	1.39（1.02）	2.14（2.95）	0.69（0.74）	1.57（1.53）
DNN	0.93（0.92）	0.77（0.85）	3.92（3.51）	0.78（0.80）	1.78（1.76）
注：括号内数据为变量优选前相应模型的指标精度。 Note: Numbers in brackets are factors of models before variable optimization.

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