The carbon-nitrogen (C:N) ratio plays a crucial role in regulation of the nutrient utilization efficiency and growth rate of plants. However, challenges are faced when hyperspectral data are directly used to estimate the C:N ratio due to the lack of corresponding sensitive feature bands. This study aims to explore the feasibility of using important bands (IBs) determined for the C:N ratio and known absorption bands (KBs) of protein, chlorophyll, N, and carbon-containing compounds from hyperspectral measurements to estimate the forage C:N ratio. Random forest (RF) and support vector machine (SVM) algorithms are employed to establish a model for the estimation of the forage C:N ratio. The results show that the KBs exhibit good performance in estimating the forage C:N ratio (V-R² of 0.70–0.80, with a mean of 0.77), and the IBs derived from the red and red-edge regions significantly contribute to the forage C:N ratio estimation, with V-R² of 0.77–0.80. This study also demonstrates that the models based on combined bands (CBs) (the combination of KBs and IBs) slightly improve the accuracy of the forage C:N ratio estimation. Moreover, further optimization of the CBs produces satisfactory estimation of the forage C:N ratio (V-R² = 0.82, V-RMSE = 5.53), explaining 85–92% of the variation in the forage C:N ratio at the different growth stages (May to November). Overall, the direct estimation of the forage C:N ratio in alpine grassland using hyperspectral feature bands is promising.

碳氮比在调节植物养分利用效率和生长速率中起着至关重要的作用。但是，由于缺乏相应的敏感特征带，直接将高光谱数据用于估计C：N比时面临挑战。这项研究旨在探讨使用高谱带测定的C：N比重要谱带（IBs）和蛋白质，叶绿素，N和含碳化合物的已知吸收谱带（KBs）估算饲料C：N的可行性比。采用随机森林（RF）和支持向量机（SVM）算法来建立估计草料C：N比的模型。结果表明，KBs在估计饲料C：N比方面表现出良好的性能（VR ²（0.70–0.80，平均值为0.77），并且来自红色和红色边缘区域的IB对草料C：N比率的估算有显着贡献，VR ²为0.77–0.80。这项研究还表明，基于组合带（CB）（KB和IB的组合）的模型会稍微提高草料C：N比估算的准确性。此外，牛肝菌的进一步优化产生了令人满意的草料C：N比估算值（VR ²  = 0.82，V-RMSE = 5.53），解释了不同生长阶段草料C：N比变化的85–92％ （五月至十一月）。总体而言，利用高光谱特征带直接估算高寒草原牧草C：N比是有希望的。