Hyperspectral Estimation Of Chlorophyll And Nitrogen Content In Rice Leaves

Rice is one of the three major food crops in the world and is the main food crop in China,precision management of rice production is helpful to ensure national food security.Using hyperspectral remote sensing technology to accurately,rapidly and nondestructively monitor the chlorophyll and total nitrogen content in rice leaves is helpful to grasp the nutritional status and growth situation of rice in real time,and to provide scientific guidance for subsequent water and fertilizer management.In this study,rice in field experiment was taken as the research object,rice canopy and leaf hyperspectral data and corresponding physiological and biochemical paraments?SPAD values and leaf nitrogen content?LNC??were collected at different growth stages of rice.The hyperspectral characteristics of rice canopy leaf amd their response to growth stages,SPAD value and LNC were explored.By using correlation analysis method,the characteristic bands,characteristic parameters and vegetation indexes with which are strongly correlatied with SPAD values and LNC are defined.Combining linear regression,multivariate stepwise linear regression?MSLR?and BP neural network?BPNN?,the SPAD value and LNC hyperspectral estimation models of rice were established.The main conclusions are as follows:?1?The spectral reflectance of rice leaves is higher than that of canopy on the whole,and they have similar responses to defferent growth stages,SPAD values,and LNC.From tillering stage to heading stage,the spectral reflectance of visible band decreased gradually,while that in near infrared band increased gradually,red edge position moved to longer wavelength,red edge amplitude and red edge area increased gradually;From heading stage to filling stage,the spectral reflectance of visible band increased gradually,while that in near infrared band decreased gradually,red edge position moved to shorter wavelength,red edge amplitude and red edge area decreased gradually.?2?SPAD value estimations of rice leaves based on canopy and leaf spectra:The best estimation model based on canopy spectra is BP neural network model based on RVI(R₁₃₀₀,R₇₆₅)and NDVI(D₅₄₄,D₇₃₀).Different growth stages have diferent optimal models based on leaf spectral.The best model of jointong stage is a power function based on RVI(D₇₃₀,D₇₅₆),the optimum model of heading stage is power function based on RVI(D₆₃₅,D_{1 123}),the optimum model of filling stage is a polynomial model based on RVI(D₇₁₈,D₅₇₁),while the optimum model of whole growth stage is multivariate stepwise linear regression model based on D₅₃₄?SDr/SDy?DVI(R₅₇₀,R₅₁₃)?DVI(D_{1 120},D₅₇₄)and RVI(D₇₁₉,D₅₉₄).?3?LNC estimations of rice leaves based on canopy and leaf spectra:The best estimation model based on canopy spectra is BP neural network model based on RVI(R₆₈₇,R₆₀₀).The overall level of the models based on leaf spectrum is not high,especially at tillering and jointing stages.the accuracy of heading stage,filling stage and whole growth stage models is relatively high,the optimal models are the multiple stepwise linear regression model established by DVI(D₄₉₂,D₄₆₀)and RVI(D₆₀₆,D₅₈₁),the BP neural network model established by DVI(D₄₄₃,D₇₇₆)and RVI(D₅₀₀,D₁₀₁₇),the BP neural network model established by R₆₉₆,D854,DVI(R₄₅₂,R₄₃₉),DVI(D₅₇₉,D₅₇₇)and RVI(D₅₉₃,D₅₆₀),respectively.