谷子叶绿素含量高光谱特征分析及其反演模型构建

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...

Hyperspectral characteristics and remote sensing inversion model of chlorophyll content of millet

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 R² 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 R² 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 Q²_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 R² 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.