| Hyperspectral characteristics and remote sensing inversion model of chlorophyll content of millet |
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| DOI：10.7606/j.issn.1000-7601.2022.02.09 |
| Key Words: millet chlorophyll content hyperspectral characteristic band inversion model |
| Author Name | Affiliation | | PENG Xiaowei | National North Engineering Technology Research Center for Agricultural in Northern Mountainous Areas, Baoding, Hebei 071000,China | | ZHANG Aijun | National North Engineering Technology Research Center for Agricultural in Northern Mountainous Areas, Baoding, Hebei 071000,China
2.Hebei Mountain Research Institute, Baoding, Hebei 071000,China | | YANG Xiaonan | National North Engineering Technology Research Center for Agricultural in Northern Mountainous Areas, Baoding, Hebei 071000,China | | WANG Nan | College of Mechanical and Electrical Engineering, Agricultural University of Hebei, Baoding, Hebei 071000,China | | ZHAO Li | College of Agriculture, Agricultural University of Hebei,Baoding, Hebei 071000,China |
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| Abstract: |
| 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 Q2cum 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. |
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