Abstract:

【Objective】 The objectives of this study were to analyze the relationships between leaf protein nitrogen concentrations (LPNC) and spectral reflectance characteristics, and to establish useful hyperspectral bands and hyperspectral indices for nondestructive and quick assessment of LPNC in top leaves of rice (Oryza sativa L.). 【Method】 Three field experiments were conducted with different N rates and rice cultivars. Time-course measurements were taken on hyperspectral reflectance of 350-2 500 nm and LPNC in four top leaves. Statistical analyses were made on the relationships between LPNC and reflectance indicators such as simple ratio indices (SR[λ1, λ2]) and normalized difference spectral index (ND[λ1,λ2]) using all combinations of two wavelengths (λ1 and λ2 nm) and other existing indices. 【Result】 The results indicated that the LPNC in rice and spectral reflectance varied distinctly with nitrogen rates, growth stages and leaf positions. The sensitivity bands mostly occurred 530-580 nm within green light region and 695-715 nm within red edge region, and a close correlation existed between red-edge district and LPNC. The SR indices composed of reflectance around 700 nm and near infrared short wavelengths were significantly correlated with LPNC, next came the 587 nm. A new spectral index SR (770,700) and existing indices GM-2, SR705, RI-half were found to be good indicators for LPNC, and linear regression models were established with determination of coefficients (R2) as 0.874, 0.873, 0.871 and 0.867, respectively. Tests with other independent datasets showed that the models based on those key spectral indices could be used to predict LPNC reliably. 【Conclusion】 It can be concluded that the LPNC in rice could be monitored directly by key spectral indices, such as SR (770, 700), GM-2, SR705 and RI-half.

Key words: rice leaves, hyperspectral remote sensing, protein nitrogen concentration, ratio spectral index, estimation model