Accuracy and precision of nitrogen estimation can be improved by hyperspectral remote sensing that leads effective management of nitrogen application in precision agriculture. The objectives of this experiment were to identify N sensitive spectral wavelengths, their combinations and spectral vegetation indices (SVIs) that are indicative of nitrogen nutritional condition and to analyze the accuracy of different spectral parameters for remote estimation of nitrogen status temporally. A study was conducted during 2010 at Northwest A & F University, China, to determine the relationship between leaf hyperspectral reflectance (350-1075 nm) and leaf N contents in the field-grown corn (Zea may L) under five nitrogen rates (0, 60, 120, 180, and 240 kg/ha pure nitrogen) were measured at key developmental stages. The fitting of liner and nonlinear regressions models between leaves total nitrogen and the spectral of original reflectance. The accuracy of nitrogen nutrition diagnosis among the single (R) and dual (R1+R2) spectral reflectance, first order differential transform, spectral ratio (SR), NDVI, GNDVI, and SAVI were compared. Choose 2-3 high coefficient and F value model to verification RMSE and RRMSE at each stage, take the smaller as the best model. The results showed that there was best fitting between nitrogen contents and their spectral parameters of R710, SDr, R550 (SDr-SDb)/(SDr+SDb), D(R630?jat 10-12 leaf, silking, tasseling, and early dent stages. Spectral ratios with R810/R670 showed highest R2 at 10-12 leaf stage, silking and tasseling stages respectively, followed by R810/R670. SAVI was the best indicator of nitrogen contents at 6-8, leaf stages. GNDVI showed the highest R2 (0.88) at 10-12 leaf stage and at silking stage (0.74) followed by NDVI. The results showed that leaf nitrogen status can be best predicted at 10-12 leaf, silking and tasseling stages by using spectral vegetation indices with GNDVI. The study results indicated that leaf hyperspectral reflectance can be used for real time monitoring of corn nitrogen status and important tool for N fertilizer management in precision agriculture.
