冬小麦冠层叶绿素质量分数高光谱遥感反演研究

叶绿素质量分数是评估冬小麦生长状况和预测产量的重要参数,估算叶绿素质量分数对于冬小麦的生长监测具有重要意义。利用SPAD-502叶绿素仪和SVCHR 1024i型便携式高光谱仪对冬小麦冠层叶绿素质量分数和光谱特征进行田间测量,分别利用回归分析方法和BP神经网络方法搭建冬小麦叶绿素质量分数的估算模型,并将模型估算的叶绿素质量分数与田间实测的叶绿素质量分数进行对比,分析反演精度,从中筛选出精度最高的模型。结果表明:基于BP神经网络的冬小麦冠层叶绿素质量分数估算模型拟合精度要优于其他7种基于植被指数的估算模型,其相关系数(R)为0.961 4,均方根误差(RMSE)为1.875 4,相对误差(RE)为2.815 2%,以及检验方程的决定系数(R~2)为0.704 8,RMSE为1.744 6,RE为2.845 1%。研究结果为估测冬小麦冠层叶绿素质量分数提供参考,从而为冬小麦叶绿素质量分数的实时、快速、无损监测奠定基础。

Inversion Chlorophyll Mass Fraction in Winter Wheat Canopy by Hyperspectral Reflectance

Chlorophyll mass fraction is one of the important parameter to assess growth situation and forecast yields of winter wheat,it is significant to estimate chlorophyll mass fraction for monitoring of winter wheat growing information.During the growth of winter wheat growth stage,the winter wheat canopy spectral reflectance and chlorophyll mass fraction were monitored in the field by the SPAD-502 and the SVCHR 1024i handheld spectrometer.Based on the results of correlation analysis,the estimation models of chlorophyll mass fraction were established according to regression and BP neural network.Then simulation precision for different models were analyzed,evaluated and the best model from the different ones was chosen.The results showed that the fitting precision of estimation models with BP neural network were superior to other models of vegetation index.The coefficient of determination (R) was 0.961 4,the root mean square error (RMSE) was 1.875 4,the relative error (RE) was 2.815 2%.And the coefficient of determination (R) of inspection equation was 0.704 8,RMSE of inspection equation was 1.744 6,RE of inspection equation was 2.845 1%.The study provided a reference for estimating winter wheat by chlorophyll contents.And it would be a solid foundation for the winter wheat to obtain chlorophyll mass fraction of real-time,fast and nondestructive monitoring method.