植物净光合速率是植物生产的基础,是体现植物生长状况的重要生理指标.本文将小麦旗叶高光谱波段反射率进行一阶导数变换后与净光合速率(Pn)进行相关性分析确定敏感波段,分别采用二次多项式逐步回归(QPSR)、偏最小二乘法(PLSR)、BP神经网络法(BPNN)3种方法构建小麦旗叶的净光合速率反演模型,并对3种模型的预测精度进行比较分析.结果表明:(1)将小麦旗叶的原始光谱进行一阶导数变换后与Pn进行相关性分析确定的敏感谱区集中在750~925 nm之间,确定的6个敏感波段分别是:760、761、767、814、815、889 nm;(2)基于QPSR、PLSR、BPNN3种方法以及敏感波段的反射率一阶导数构建的Pn估测模型预测精度都较高,说明用这3种方法以及敏感波段对Pn的估测是可行的,其中模型估算能力顺序为QPSR>BPNN>PLSR,说明小麦旗叶Pn的最佳高光谱分析模型为小麦叶片750~925 nm反射率一阶导数变化后的QPSR模型.%Net photosynthetic rate of plants is the basis of plant production, and is an important physiological index to reflect the growth of plants. In this paper, hyperspectral reflectance of flag leaves of wheat was transformed with the first derivative and then correlated with net photosynthetic rate(Pn)to determine the sensitive bands, adopting three methods quadratic polynomial stepwise regression(QPSR), partial least squares regression(PLSR), back propagation neural network(BPNN)respectively to construct the inversion model of Pn for flag leaves of wheat, and to compare and analyze the prediction accuracy of the three models. The result showed that:(1) After the first derivative transformation of the original spectra of wheat leaves, and analysis with Pn in correlation the determined sensitive zone concentrate occured on 750~925 nm, and the six sensitive bands were determined as 760, 761, 767, 814, 815 nm and 889 nm.(2)Based on QPSR, PLSR and BPNN the Pn estimation model constructed was highly forecasting precision . This illustrates the three methods and sensitive band was feasible to estimate Pn. Among them the order of the ability to estimate the model was QPSR>BPNN>PLSR, which indicated the best hyperspectral model for flag leaf Pn of wheat was QPSR model whose first derivative changed after 750~925 nm reflectivity of wheat leaf.
展开▼
