摘要
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(R^(2)=0.436,RMSE=874.57 kg/hm^(2)),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(R^(2)=0.443,RMSE=861.81 kg/hm^(2)),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(R^(2)=0.436,RMSE=874.57 kg/hm^(2))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(R^(2)=0.443,RMSE=861.81 kg/hm^(2)),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.
作者
高钰琪
许桂玲
冯跃华
王晓珂
任红军
由晓璇
韩志丽
李家乐
GAO Yuqi;XU Guiling;FENG Yuehua;WANG Xiaoke;REN Hongjun;YOU Xiaoxuan;HAN Zhili;LI Jiale(College of Agronomy,Guizhou University,Guiyang 550025,China;The Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region(Ministry of Education),Guizhou University,Guiyang 550025,China)
出处
《中国稻米》
北大核心
2023年第5期38-44,共7页
China Rice
基金
国家自然科学基金(32260531)
国家重点研发计划项目子课题(2022YFD1901500,2022YFD1901505-07)
贵州省特色粮油作物栽培与生理生态研究科技创新人才团队(黔科合平台人才[2019]5613号)
贵州省高层次创新型人才项目(黔科合平台人才[2018]5632,黔科合平台人才[2018]5632-2)
贵州省科技计划项目(黔科合支撑[2019]2303号)
公益性行业(农业)科研专项子项目(201503118-03)
贵州省普通高等学校粮油作物遗传改良与生理生态特色重点实验室项目(黔教合KY字[2015]333)。
关键词
水稻
产量
冠层高光谱
植被指数
模型
rice
yield
canopy hyperspectral
vegetation index
model
