首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于高光谱遥感的小麦籽粒产量预测模型研究
引用本文:冯伟,朱艳,田永超,姚霞,郭天,曹卫星.基于高光谱遥感的小麦籽粒产量预测模型研究[J].麦类作物学报,2007,27(6):1076-1084.
作者姓名:冯伟  朱艳  田永超  姚霞  郭天  曹卫星
作者单位:1. 南京农业大学/江苏省信息农业高技术研究重点实验室,江苏南京,210095;河南农业大学/国家小麦工程技术研究中心,河南郑州,450002
2. 南京农业大学/江苏省信息农业高技术研究重点实验室,江苏南京,210095
3. 河南农业大学/国家小麦工程技术研究中心,河南郑州,450002
基金项目:国家自然科学基金 , 江苏省自然科学基金
摘    要:为了确立能够准确预测小麦籽粒产量的敏感光谱参数和定量模型,于2003~2006年连续3个生长季,通过不同小麦品种和不同施氮水平的4个大田试验,在小麦不同生育期采集田间冠层高光谱数据并测定植株氮含量、重量和叶面积指数及成熟期籽粒产量,定量分析小麦籽粒产量与冠层高光谱参数的相互关系.结果显示,小麦籽粒产量随施氮水平的提高而增加,不同地力水平间存在显著差异.灌浆前期叶片氮积累量和叶面积氮指数均能够较好地反映成熟期籽粒产量状况,而叶片氮含量和氮积累量及叶面积氮指数在拔节~成熟期的累积值与成熟期籽粒产量的回归拟合效果更好.对叶片氮含量和氮积累量及叶面积氮指数的光谱反演,在不同品种、氮素水平和年度间可以使用统一的光谱参数.根据"特征光谱参数-叶片氮素营养-籽粒产量"这一技术路径,以叶片氮素营养为交接点将模型链接,建立了基于灌浆前期高光谱参数及拔节期~成熟期特征光谱指数累积值的小麦籽粒产量预测模型.经两年独立试验数据检验表明,利用灌浆前期关键特征光谱指数可以有效地评价小麦成熟期籽粒产量状况,拔节~成熟期特征光谱指数的累积值能够稳定预报不同条件下小麦成熟期籽粒产量的变化.因此,利用冠层特征光谱指数可以快速无损地预报小麦成熟期籽粒产量.

关 键 词:小麦  氮素营养  籽粒产量  高光谱遥感  预报模型  光谱遥感  小麦  籽粒产量  预测模型  研究  Wheat  Remote  Sensing  Canopy  Grain  Yield  Predicting  稳定预报  快速  冠层特征  变化  条件  值能  评价  关键特征  利用  数据检验
文章编号:1009-1041(2007)06-1076-09
收稿时间:2007-05-09
修稿时间:2007-06-10

Model for Predicting Grain Yield with Canopy Hyperspectal Remote Sensing in Wheat
FENG Wei,ZHU Yan,TIAN Yong-chao,YAO Xi,GUO Tian-cai,CAO Wei-xing.Model for Predicting Grain Yield with Canopy Hyperspectal Remote Sensing in Wheat[J].Journal of Triticeae Crops,2007,27(6):1076-1084.
Authors:FENG Wei  ZHU Yan  TIAN Yong-chao  YAO Xi  GUO Tian-cai  CAO Wei-xing
Institution:1.Nanjing Agricultural University/Hi-Tech Key Laboratory of Information Agriculture of Jiangsu Province,Nanjing,Jiangsu 210095,China,2.Henan Agricultural University/National Engineering Researchl Centre for Wheat,Zhengzhou,Henan 450002,China
Abstract:Non-destructive and quick prediction of grain yield is necessary in wheat production.The objectives of this study were to determine the relationships of grain yield to ground-based canopy hyper-spectral reflectance and spectral parameters,and to derive regression equations for predicting grain yield in winter wheat(Triticum aestivum L.) with canopy hyper-spectral remote sensing.Four 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,plant dry weight,nitrogen content and leaf area index during the experiment periods,and grain yield at maturity.The results showed that the grain yield at maturity in wheat increased with increasing nitrogen rates,with significant difference among different soil fertility levels.Plant N nutrition status as leaf N accumulation(LNA) and leaf area N index(LANI) at initial grain filling stage could well indicate grain yield at maturity,and cumulative value of LNA,LANI and leaf N content(LNC) from booting to maturity were highly correlated with grain yield at maturity,with the determination coefficients(R2) as 0.957,0.961 and 0.915 from logarithm equation,respectively.The regression analyses between existing vegetation indices and leaf N index as LNC,LNA and LANI indicated that some key spectral parameters could accurately estimate the changes in leaf N status across a broad ranges of growth stages,nitrogen levels and growing seasons,with unified spectral parameters for each wheat cultivars,such as REPle and mND705 for LNC,SDr/SDb and FD742 for LNA,SDr/SDb,FD755,VOG2 and(R750-800/R695-740)-1 for LANI.Based on the technical-route of characteristic spectral parameters-leaf N nutrition-grain yield,predicting models on grain yield were constructed with canopy hyper-spectral parameters at initial grain filling and cumulative value of key spectral parameters from booting to maturity in wheat by linking the two sets of models with leaf N nutrition as intersection.Testing of the predicting models with independent two-year dataset indicated that the above linked models gave accurate yield estimation with better agronomy mechanism and physics base.Overall,the grain yield at maturity in wheat could be predicted by key vegetation indices.
Keywords:Wheat  Nitrogen nutrition  Grain yield  Hyper-spectral remote sensing  Predicting model
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《麦类作物学报》浏览原始摘要信息
点击此处可从《麦类作物学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号