氮气
农学
产量(工程)
数学
叶面积指数
回归分析
阶段(地层学)
叶绿素
植被(病理学)
反射率
光谱指数
归一化差异植被指数
线性回归
生物系统
园艺
回归
光谱分析
预处理器
植物
植被指数
粮食产量
扎梅斯
环境科学
冬小麦
发育阶段
联轴节(管道)
禾本科
作物产量
生长季节
化学
遥感
作者
Fu Zhang,Fu Zhang,Baoping Yan,Le Yang,Fangyuan Zhang,Fangyuan Zhang,Yakun Zhang,Yafei Wang,Shaukat Ali,Sanling Fu
摘要
ABSTRACT Accurate estimation of leaf SPAD is crucial for maize growth and yield formation. Many methods for monitoring SPAD currently lack the analysis of sensitive leaf position in different stages of maize. In this paper, the spectra and temporal–spatial characteristics of maize leaf SPAD were analyzed to describe the sensitive stage and leaf position. After exploring the dynamic growth effects of SPAD in maize leaves, the sensitive stage of SPAD was determine. Several preprocessing methods and spectral vegetation indices were used to analyze the spectral reflectance of typical leaf positions in sensitive stages. The function regression methods based on single vegetation index and the random forest regression (RFR) based on multi‐vegetation indices were employed. The results showed that the twelve‐leaf (V12) and the silking (R1) were the sensitive stages. The strongest RVI at the V12 stage and NDRE for the ear leaves at the R1 stage were observed under SG‐SNV method. The best prediction data ( R 2 = 0.7) was showed at the V12 stage under MSC‐RF. The prediction effect of the ear leaves after MSC pretreatment was slightly better ( R 2 = 0.69). In addition, SPAD value can indirectly reflect the chlorophyll content, nitrogen content and yield status of maize leaves, and its accurate monitoring provides effective guidance for maize leaf nutrition information and yield prediction.
科研通智能强力驱动
Strongly Powered by AbleSci AI