及时、准确、快速的进行粮食产量预测预报对指导农业生产和国家制定粮食政策有重大意义。以不同水稻品种和施氮水平为试验因素，进行两因素裂区设计试验，在水稻拔节期、孕穗期和抽穗期测定其冠层光谱反射率，通过筛选出与产量相关性最高的最佳波段组合，计算最优波段组合组成的12种植被指数，并建立了基于单植被指数和多植被指数组合的水稻产量预测模型。结果表明，孕穗期，在401~723 nm波段范围内水稻冠层原始光谱反射率与产量呈显著负相关关系；各植被指数与产量的相关性达到极显著水平。基于单植被指数构建的水稻产量预测模型，以孕穗期线性模型精度最高（R²=0.436，RMSE=874.57 kg/hm²），最佳植被指数为重归一化植被指数（RDVI），模型表达式为y=7.7E+05×RDVI_{（455，456）}+1.1E+04；基于逐步回归构建的多植被指数产量预测模型同样以孕穗期表现最佳（R²=0.443，RMSE=861.81 kg/hm²），最优植被指数为比值植被指数（RVI）、土壤调节型植被指数（SAVI）和最佳植被指数（VIopt），模型表达式为y=1.8E+05×RVI _{（1661，1687）}-2.1E+05×SAVI _{（1235，1268）}+5.3E+04×VIopt _{（2260，2215）}-3.4E+05。总的来说，多植被指数产量预测模型的拟合精度和预测效果均优于单植被指数产量预测模型，其中以孕穗期模拟效果最好。

Timely, accurate and rapid prediction of grain yield is of great significance for guiding agricultural production and formulating national food policy. A split-plot design experiment, taking different rice varieties and nitrogen application levels as experimental factors, was conducted to measure the canopy spectral reflectance at rice jointing stage, booting stage and heading stage. By selecting the best band combination with the highest correlation with yield, 12 vegetation indexes composed of the best band combination were calculated, and a rice yield prediction model based on the combination of single vegetation index and multi vegetation index was established. The results showed that at booting stage, there were a significant negative correlation between the original spectral reflectance of rice canopy and yield in the band of 401~723 nm, the correlation between each vegetation index and yield reached a very significant level. The rice yield prediction model based on single vegetation index has the highest precision (R²=0.436, RMSE=874.57 kg/hm²) in the linear model at booting stage, and the best vegetation index is the normalized vegetation index (RDVI), and the model expression is y=7.7E+05×RDVI_{(455, 456)}+1.1E+04. The multi vegetation index yield prediction model based on stepwise regression also showed the best performance at booting stage (R²=0.443, RMSE=861.81 kg/hm²), the optimal vegetation index was ratio vegetation index (RVI), soil regulated vegetation index (SAVI) and optimal vegetation index (VIopt), and the model expression was y=1.8E+05×RVI_{(1661, 1687)}-2.1E+05×SAVI_{(1235, 1268)}+5.3E+04×VIopt_{(2260, 2215)}-3.4E+05. In general, the fitting accuracy and prediction effect of the multi vegetation index yield prediction model are better than those of the single vegetation index yield prediction model, and the simulation effect at booting stage is the best.