植物营养与肥料学报2012，Vol.18Issue(4)：813-824,12.
夏玉米叶片全氮含量高光谱遥感估算模型研究
Total nitrogen content estimation models of summer maize leaves using hyperspectral remote sensing
摘要
Abstract
Based on the treatments of five nitrogen fertilizer application amounts and two cultivars of summer maize, crop canopy spectral reflectance and total nitrogen content of maize leaves were measured at the jointing stage, huge bellbottom stage, tasseling stage, silking stage and milk stage. The canopy spectral reflectance in 470, 550, 620 and 720 nm wavelength of hyperspectral remote sensing were chosen to establish liner and nonlinear regression relationship between leaf total nitrogen content and canopy spectral parameters for each cuhivar, which includes original spectral reflectance, first order differential transform, and part of hyperspectral characteristic parameters （ i. e. spectrum position, area, characteristic parameters of vegetation index）. Three models with high coefficients and F values of each cultivar at each growth stage were chosen to verify root mean square error and relative error with the second year data of spectral reflectance and total nitrogen content of two cultivars separately. The smallest root mean square error and relative error models were taken as the best models. The results show that： at the jointing stage, huge bellbottom stage, tasseling stage, silking stage and milk stage of maize, spectrum parameter for the best fitting regression relationship with leaf total nitrogen content was R720, DR720, SDb, DR550 and DR550, respectively. And the best model to estimate total nitrogen content of maize leaf based on above best spectrum parameter of hyperspectral remote sensing in five growth stages was Y = 5. 129e-2.317x, y = 3. 421 - 10. OlOx - 477802.331x3, Y =4.070 -2.304x -52.177x2, Y = -0.468 -0.5281nx and Y = -2.390 -0.7931nx, respectively.关键词
玉米/氮素/高光谱/模型Key words
maize/nitrogen/hyperspectrum/model分类
农业科技引用本文复制引用
刘冰峰,李军,赵刚峰,Naveed Tahir,贺佳..夏玉米叶片全氮含量高光谱遥感估算模型研究[J].植物营养与肥料学报,2012,18(4):813-824,12.基金项目
国家自然科学基金项目（30771280,31071374）资助. （）
