摘要
基于高光谱数据综合分析不同施肥条件下谷子各生长期冠层叶绿素含量的高光谱特征,在分析各光谱特征参数与叶绿素相关性的基础上,基于偏最小二乘法和人工神经网络构建叶绿素含量的遥感反演模型。结果表明:NDVI(归一化植被指数)、GNDVI(绿色归一化植被指数)、PSNDa(特殊色素归一化指数a)、PSSRc(特征色素简单比值指数c)、RENDVI(红边归一化植被指数)及Dy(黄边幅值)与不同生育期的SPAD值均呈极显著相关关系(P<0.05)。基于上述光谱指数为自变量建立的最佳一元回归模型R^(2)(决定系数)在0.4~0.6之间,基于偏最小二乘法的回归模型R^(2)在0.55~0.71之间,RMSECV(交叉验证均方根)在1.34~2.23之间,Q^(2)_(cum)(主成分累积模型预测能力)在0.54~0.83之间,对自变量的解释能力在63.1%~95.8%之间,说明上述光谱参数对叶片叶绿素的解释程度较好。利用BP神经网络估测叶绿素含量可达到最优精度,建模集的R^(2)达到0.70以上,RMSE(均方根误差)在1.18~2.48之间。综上所述,利用BP神经网络建模效果最优。
This study used the comprehensive analysis of the hyperspectral data of the hyperspectral characteristics of the chlorophyll content in the millet canopy under different fertilization conditions to examine the correlation between the spectral characteristics and the chlorophyll.The remote sensing inversion model of chlorophyll content was constructed based on the partial least squares method and artificial neural network.The results showed that:through correlation analysis,NDVI,GNDVI,PSNDa,PSSRc,RENDVI,and Dy all had extremely significant correlations with SPAD in different growth stages.The coefficient of determination R^(2) of the best unary regression model established based on the above spectral index as the independent variable was between 0.4 and 0.6,and the coefficient of determination R^(2) of the regression model based on the partial least squares method was between 0.55 and 0.71.The cross-validated root mean square RMSECV fell between 1.34 and 2.23,and the predictive ability of the principal component accumulation model Q^(2)_(cum) was between 0.54 and 0.83.The explanatory ability of the independent variable was between 63.1%and 95.8%,indicating that the above-mentioned spectral parameters explained the leaf chlorophyll better.The BP neural network estimated the chlorophyll content to achieve the best accuracy,and the determination coefficient R^(2) of the modeling set was above 0.70.The RMSE was between 1.18 and 2.48.In summary,the modeling effect using BP neural network was the best.
作者
彭晓伟
张爱军
杨晓楠
王楠
赵丽
PENG Xiaowei;ZHANG Aijun;Yang Xiaonan;WANG Nan;ZHAO Li(National North Engineering Technology Research Center for Agricultural in Northern Mountainous Areas,Baoding, Hebei 071000,China;Hebei Mountain Research Institute, Baoding, Hebei 071000,China;College of Mechanical and Electrical Engineering, Agricultural University of Hebei, Baoding, Hebei 071000,China;College of Agriculture, Agricultural University of Hebei,Baoding, Hebei 071000,China)
出处
《干旱地区农业研究》
CSCD
北大核心
2022年第2期69-77,共9页
Agricultural Research in the Arid Areas
基金
河北省重点研发计划项目(19226421D)。
关键词
谷子
叶绿素含量
高光谱
特征波段
反演模型
millet
chlorophyll content
hyperspectral
characteristic band
inversion model
