[Hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status].
The correlations of rice plant nitrogen content with raw hyperspectral reflectance, first derivative hyperspectral reflectance, and hyperspectral characteristic parameters were analyzed, and the hyperspectral remote sensing diagnosis models of rice plant nitrogen nutritional status with these remote sensing parameters as independent variables were constructed and validated. The results indicated that the nitrogen content in rice plant organs had a variation trend of stem < sheath < spike < leaf. The spectral reflectance at visible light bands was leaf < spike < sheath < stem, but that at near-infrared bands was in adverse. The linear and exponential models with the raw hyperspectral reflectance at 796.7 nm and the first derivative hyperspectral reflectance at 738.4 nm as independent variables could better diagnose rice plant nitrogen nutritional status, with the decisive coefficients (R2) being 0.7996 and 0.8606, respectively; while the model with vegetation index (SDr - SDb) / (SDr + SDb) as independent variable, i. e., y = 365.871 + 639.323 ((SDr - SDb) / (SDr + SDb)), was most fit rice plant nitrogen content, with R2 = 0.8755, RMSE = 0.2372 and relative error = 11.36%, being able to quantitatively diagnose the nitrogen nutritional status of rice.
Tan
Chang-Wei
CW
Jiangsu Province Key Laboratory of Crop Genetics and Physiology, Yangzhou University, Yangzhou 225009, Jiangsu, China. tanwei010@126.com
Zhou
Qing-Bo
QB
Qi
La
L
Zhuang
Heng-Yang
HY
D000465:Algorithms; D008962:Models, Theoretical; D009584:Nitrogen / Q000032:analysis*; D012275:Oryza / Q000737:chemistry*; D018515:Plant Leaves / Q000737:chemistry; D018547:Plant Stems / Q000737:chemistry; D013057:Spectrum Analysis / Q000379:methods*
D009584:Nitrogen