Abstract: 【Objective】Carbon and nitrogen metabolism in crop plants reflects plant physiological status, growth activity and anti-disease ability, and coordinated carbon and nitrogen metabolism provides physiological basis for yield and quality formation in wheat. Thus, non-destructive and quick assessments of soluble sugar to nitrogen ratio is necessary for growth diagnosis and nitrogen management in wheat production. The objectives of this study were to determine the relationships between leaf soluble sugar to nitrogen ratio and ground-based canopy hyper-spectral reflectance and spectral parameters, and to derive regression equations for monitoring leaf soluble sugar to nitrogen ratio in winter wheat (Triticum aestivum L.) with canopy hyper-spectral remote sensing. 【Method】Three field experiments were conducted with different wheat varieties and nitrogen levels across three growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and leaf soluble sugar to nitrogen ratio during the experiment periods. 【Result】The results showed that the soluble sugar to nitrogen ratio in wheat leaves decreased with increasing nitrogen rates, with significant difference among growing seasons. Dynamic changes of the soluble sugar to nitrogen ratio at different growth stages took on the trends of high-low-high pattern. The proper time for monitoring leaf soluble sugar to nitrogen ratio should be from jointing to mid-filling, with best stage as anthesis. FWBI and Area980 of water-index were highly correlated with leaf soluble sugar to nitrogen ratio, with the determination of coefficients (R2) as 0.762 and 0.768 from exponential equation, respectively, and the standard errors (SE) as 1.27 and 1.28, respectively. (R750-800/R695-740)-1 and VOG2 of pigment-index were also significantly related to leaf soluble sugar to nitrogen ratio, with R2 as 0.718 and 0.712 from exponential equation, respectively, and SE as 1.87 and 1.95, respectively. Thus, the exponential equation with some key water-index and pigment-index as variables could well describe the dynamic change patterns in the leaf soluble sugar to nitrogen ratio in wheat, with better performance from water-index than from pigment-index. Testing of the monitoring models with independent dataset indicated that FWBI, Area1190, (R750-800/R695-740)-1 and VOG2 were the best indicators to estimate leaf soluble sugar to nitrogen ratio, with the predictive precision (R2) of 0.627, 0.618, 0.691 and 0.795, respectively, the relative error (RE) of 19.2%, 18.7%, 17.9% and 18.3%, respectively. 【Conclusion】Overall, the soluble sugar to nitrogen ratio in wheat leaves could be estimated by vegetation indices based on spectral characteristics related to water and pigment absorption, and FWBI, Area1190, (R750-800/R695-740)-1 and VOG2 could be used for reliably estimating the leaf soluble sugar to nitrogen ratio in wheat leaves.

Key words: Wheat (Triticum aestivum L.), Hyper-spectral remote sensing, Soluble sugar to N ratio, Monitoring model