Phosphorus is an important element for pasture growth.Taking the Dafeng Elk Nature Reserve as the research object,the prediction models of phosphate content are established to find the best modeling method and provide a reference basis for physiological and ecological monitoring of Spartina alterniflora.Based on correlation coefficient,principal component analysis,and variable importance for projection,the sensitive bands of reflectance,first derivative,and continuum removal were screened,and the multiple linear regression,partial least squares regression,and BP artificial neural network prediction models were established.The correlation and significance between spectral data and phosphorus content of S.alterniflora were enhanced after the first derivative and continuum removal transformation.The accuracy was sorted as first derivative＞reflectance＞continuum removal by comparing the inversion models established by different spectral data forms.The accuracy and stability of the three models were comprehensively compared,and the accuracy of the models was ranked as BP artificial neural network＞partial least squares regression＞multiple linear regression.The best inversion model was the first derivative-BP model based on full bands spectral data,with a modeling accuracy of R2=0.920,RMSE=0.059,and RPD=2.949.
关键词
高光谱/互花米草/主成分分析/变量投影重要性/逐步回归/偏最小二乘回归/BP神经网络
Key words
hyperspectral/Spartina alterniflora/principal component analysis/variable importance for projection/multiple linear regression/partial least squares regression/BP artificial neural network
维普
万方数据