Study On Hyperspectral Inversion Model Of Cotoon Physiological And Biochemical Parameters

Cotton is not only a commercial crop but also an important raw material for textile industry in our country, occupying a critical position in national economy. Thus, it is significant for production practice to study the quick and efficient acquirement of cotton agronomic parameters. Cotton cultivated in the dryland of Weibei are used as subjects in this research. The agronomic parameters and canopy spectra reflectance are measured during the main growth period of cotton. Using hyperspectral remote sensing technology, chlorophyll content in cotton leaf and Leaf Area Index(LAI) are evaluated in both spatial and temporal variation. By means of multivariate statistics and linear fitting method, sensitive bands of agronomic parameters are determined and 29 kinds of remote sensing spectral variables are extracted, the estimation models of agronomic parameters based on the correlation analysis are established. Finally, based on the results of accuracy testing, the best quantitative inverse models for different growth stages and the whole growth period are derived. A scientific basis for hyperspectral remote sensing monitoring of cotton growing in dryland of Weibei can be provided. The conclusions are as follows:1) Whether in different growth stages or the whole growth period, the first derivative spectra have higher correlation with cotton agronomic parameters than original reflectance spectra. The performance of inversion improves when model coefficients are higher, proving spectral differential processing does eliminate the effects of soil background noise. In the whole growth period, cotton canopy spectral reflectance can be used to estimate the leaf SPAD value since their correlation with SPAD is high. While, the first derivative spectra works better. The sensitive bands of SPAD value occur at 734.7nm and the correlation coefficient reaches 0.6992 **. In whole growth period, the sensitive bands of leaf area index appear on original canopy reflectance spectra at 704.2nm and the correlation coefficient reaches 0.611**; sensitive bands appear on first derivative spectra at 753 nm while the correlation coefficient is as high as 0.858**.2) Within the estimation models of canopy SPAD value, the determine coefficient of the logistic equation based on R734.7 as a variable from the first derivative spectral sensitive bands is 0.3002. The determine coefficient of estimation model build on Modified chlorophyll absorbs reflective index(MCARI) is 0.461, and the testing model’s determine coefficient R2 is up to 0.527. The best estimation model of SPAD value of cotton canopy is the SPAD-MCARI model.In the estimation models of Leaf Area Index, the determine coefficient of the best model based on R753 as a variable from the first derivative spectral sensitive bands is 0.784, and the testing model’s determine coefficient R2 is 0.7603. While the optimal estimation model based on spectral variables is the LAI-MTCI model, whose determine coefficient is 0.837 and the examine R2 is 0.760. It can be concluded that the estimation model based on spectrum variable is more accurate in predictions than the model based on the sensitive band. The estimation model based on spectral variable is more applicable in prediction of cotton agronomic parameters such as SPAD value and LAI.3) Comparing with conventional regression statistical methods and PLSR model, it can be concluded that the PLSR estimation model using multiple spectral variables works better, who has the highest accuracy, whose determine coefficient R2 is 0.733, and testing model’s determine coefficient R2 is up to0.737.4) The spectra of single growth period can sufficiently describe the growth condition of single growth period of cotton. In different growth stages, the best estimation model for SPAD value is a regression equation with Green Normalized Difference Vegetation Index(GNDVI) as an independent variable, which is a quadratic model in boll opening stage. A correlation of 0.6420 ** is achieved. When it comes to leaf area index estimation model, a quadratic regression equation with the light radiation index(PRI) as independent variable fits best, which is in the peak bolling stage. The correlation of cotton spectral characteristic and cotton agronomic parameters is higher because whole growth period is rich in data. The results showed that the prediction performance of cotton leaf SPAD values and leaf area index estimation model of whole growth period is more accurate, providing an important reference for remote sensing monitoring for the production of cotton growing.