基于偏最小二乘回归的谷子冠层氮素含量高光谱估测研究OA北大核心CSTPCD

In order to construct a hyperspectral monitoring model of nitrogen content in the canopy of multiple varieties of millet,the hyperspectral reflectance and leaf nitrogen content of millet in the whole growth stage of millet were obtained by setting up different nitrogen levels and field experiments of multiple varieties of millet.The data of hyperspectral reflectance were preprocessed by convolution smoothing and first derivative transformation.The correlation between hyperspectral data and leaf nitrogen content of millet was analyzed.Nitrogen sensitive bands,vegetation index and hyperspectral characteristic parameters of millet at different and whole growth stages were screened by successive projections algorithm(SPA)and correlation analysis between spectral data and nitrogen content.The partial least square regression(PLSR)estimation model of nitrogen content in millet canopy was established by combining the 3 combinations.The results showed that the optimal estimation models of different growth periods were different.At jointing stage,the model based on sensitive band,vegetation index and hyperspectral characteristic parameters had the highest accuracy.At heading stage,the model based on sensitive band,vegetation index model had the highest accuracy.At pustulation stage,the model based on vegetation index and hyperspectral characteristic parameter model had the highest accuracy.At mature stage,the model based on sensitive band,and vegetation index model had the highest accuracy.In the whole growth period,the model based on sensitive bands and vegetation index had the highest accuracy.The multi-input level synthesis model could make full use of the spectral information to effectively improve the prediction accuracy and stability of the model,and the model based on sensitive band and vegetation index performs had best effect,which the R2 of prediction set was more than 0.82,root-mean-square error(RMSE)was less than 0.119,and relative predicted deviation(RPD)was greater than 2.1.Above results provided theoretical basis and technical support for hyperspectral remote sensing to diagnose nitrogen surplus and deficiency and fertilization decision of millet in the whole growth period.