Abstract: 【Objective】Nitrogen is one of the most important nutrients in crop growth and development. The objective of this paper is to study the setting of effective index of leaf nitrogen content inversion in order to provide an important basis for the application of hyperspectral vegetation index of leaf nitrogen content estimation, and for real-time monitoring and accurate diagnosis of crops.【Method】A total of 225 groups of canopy reflectance and leaf nitrogen content data which covering the whole winter wheat growth period and under different levels of coverage, were collected to simulate different spectral index like different central wavelengths, SNR and band width indicators, and to analyze the influence of different observation pattern on quantitative models. And then, the indicators of accuracy evaluation, coefficient of determination, root mean square error, mean absolute error, mean relative error and P＜0.01 were used to select the optimal model and the best indicators, and the sensitivity and effectiveness of leaf nitrogen content quantitative models inversion were analyzed with different spectral indicators.【Result】MTCI_B was the best vegetation index for leaf nitrogen content inversion with the center wavelengths of 420 nm, 508 nm and 405 nm, band width of 1nm, SNR greater than 70 DB; the correlation with measured nitrogen content was preferably (R²=0.7674, RMSE=0.5511% , MAE=0.4625%, MRE=11.11 percentage points and P＜0.01). RVI_{inf_r} was the best index for inversion of high coverage with the optimal center wavelengths 826 nm and 760 nm (R²=0.6739, RMSE=0.2964%, MAE=0.2851%, MRE=6.44 percentage points and P＜0.01). MTCI was the best index for inversion of low coverage nitrogen (R²=0.8252, RMSE=0.4032%, MAE=0.4408%, MRE=12.22 percentage points and P＜0.01), corresponding to the optimal center wavelengths 750 nm, 693 nm and 680 nm. Using hyperspectral vegetation indexes RVI_{inf_r} and MTCI to build a joint inversion model, the model accuracy evaluation result (R²=0.9286, RMSE=0.3416%, MAE=0.2988%, MRE=7.16 percentage points and P＜0.01) was significantly better than the best single index MTCI_B. When the optimal model was used to simulate Hyperion and HJ1A-HSI data, the accuracy of the joint model (R² reached 0.92-0.93, RMSE were between 0.37%-0.39%) was better than the single vegetation index (R² were 0.79-0.81, RMSE were between 0.63%-0.66%).【Conclusion】A good estimation of crop leaf nitrogen content could be realized by using hyperspectral vegetation index, quantitative inversion of crop leaf nitrogen content had a strong sensitivity with different spectral indexes, center wavelength, SNR, and band width. Application of multi-exponential joint inversion model significantly improved the accuracy of the inversion. And the joint inversion model had a certain degree of universality in different hyperspectral sensors.

Key words: leaf nitrogen inversion, spectral index, winter wheat, vegetation index, hyperspectral remote sensing