中国农业气象

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冠层反射光谱与小麦产量及产量构成因素的定量关系

杨智;李映雪;徐德福;刘寿东;   

  1. 南京信息工程大学应用气象学院;
  • 出版日期:2008-06-10 发布日期:2008-06-10
  • 基金资助:
    江苏省高校自然科学研究指导性计划项目(06KJD210116);; 南京信息工程大学校基金(QD49)

Relationships of Canopy Reflectance Spectra with Wheat Yield and Yield Components

YANG Zhi1,2,LI Ying-xue1,XU De-fu1,LIU Shou-dong1(1.Nanjing University of Information Science & Technology,Nanjing,210044,China;2.Dali National Climate Observatory)   

  • Online:2008-06-10 Published:2008-06-10

摘要: 基于4个小麦品种、5个施氮水平的田间试验,在比较小麦冠层多光谱和高光谱反射特征的基础上,讨论了不同生育期冠层反射光谱参数与小麦产量及产量构成因素的定量关系。结果表明,拔节期冠层多光谱参数与理论产量和实际产量的相关性较高,可用于预测小麦产量,而冠层高光谱反射参数与小麦产量间的相关性较差,难以直接利用预测小麦产量;冠层的多光谱和高光谱参数对亩穗数的预测效果均较好,小麦拔节期、灌浆中期和成熟期的冠层多光谱参数、高光谱参数均与亩穗数间具有极显著正相关关系(P<0.01),从而分别建立了各时期利用高光谱参数A(760,850)/R550、多光谱比值植被指数RVI(810,560)的小麦估产方程。研究结果对选择合适的光谱参数建立估产模型、保证高光谱遥感信息反演精度具有重要价值。

关键词: 小麦, 冠层多光谱反射光谱, 冠层高光谱反射光谱, 产量, 产量构成因素

Abstract: Based on the data of the field experiments with four wheat varieties and five level of the nitrogen application,the relationships of the canopy reflectance spectra with the wheat yield and yield components were analyzed.The results showed that the correlation of the canopy multispectral reflectance between theoretical and actual yields was significant at the jointing stage.Therefore,it could be used to estimate the yield.However,the correlation of the canopy hyperspectral reflectance and yield was significant,so it couldn't be used to estimate the yield directly.The panicle number per mu was well forecasted by using canopy multispectral/ hyperspectral reflectance.The canopy multispectral and hyperspectral reflectance were linearly related to panicle number per mu at the jointing,mid-filling and maturity stage of wheat(p<0.01).Thus,the estimate equations of the canopy hyperspectral reflectance A(760,850)/R550 and multispectral reflectance RVI(810,560) were constituted.The research results provided the important references for choosing appropriate canopy reflectance indexes,constituting the yield estimate model and ensuring the precision of the hyperspectral remote sensing information retrieval.

Key words: Wheat, Wheat, Canopy multispectral reflectance, Canopy hyperspectral reflectance, Yield, Yield components