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作物学报 ›› 2013, Vol. 39 ›› Issue (02): 309-318.doi: 10.3724/SP.J.1006.2013.00309

• 耕作栽培·生理生化 • 上一篇    下一篇

高光谱遥感估测大豆冠层生长和籽粒产量的探讨

吴琼1,**,齐波1,**,赵团结1,姚鑫锋2,朱艳2,盖钧镒1,*   

  1. 1南京农业大学大豆研究所 / 国家大豆改良中心 / 农业部大豆生物学与遗传育种重点实验室(综合) / 作物遗传与种质创新国家重点实验室，江苏南京 210095; 2 南京农业大学 / 国家信息农业工程技术中心 / 江苏省信息农业高技术研究重点实验室, 江苏南京 210095
  • 收稿日期:2012-05-21 修回日期:2012-10-09 出版日期:2013-02-12 网络出版日期:2012-12-11
  • 通讯作者: 盖钧镒, E-mail: sri@njau.edu.cn
  • 基金资助:

    本研究由国家重点基础研究发展计划(973计划)项目(2009CB1184, 2010CB1259, 2011CB1093)，国家公益性行业(农业)专项(200803060, 201203026-4)，江苏省优势学科建设工程专项和国家重点实验室自主课题。

A Tentative Study on Utilization of Canopy Hyperspectral Reflectance to Esti-mate Canopy Growth and Seed Yield in Soybean

WU Qiong1,**,QI Bo1,**,ZHAO Tuan-Jie1,YAO Xin-Feng2,ZHU Yan2,GAI Jun-Yi1,*   

  1. 1 Soybean Research Institute / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General), Minister of Agriculture / National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China; 2 National Engineering and Technology Center for Information Agriculture / Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
  • Received:2012-05-21 Revised:2012-10-09 Published:2013-02-12 Published online:2012-12-11
  • Contact: 盖钧镒, E-mail: sri@njau.edu.cn

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被引次数

40

摘要：

现代作物育种需要监测大量育种材料的生长并估测产量潜势, 高光谱遥感技术为此提供了简单、快捷、非损伤性测定的可能途径。选取30份大豆育成品种进行连续2年的产量比较试验, 在盛花期(R2)、盛荚期(R4)和鼓粒始期(R5)测定地上部生物量(ADM)和叶面积指数(LAI), 并利用ASD高光谱地物仪同步收集大豆冠层反射光谱信息。供试品种间ADM、LAI和产量差异显著或极显著。不同生育期可见光和近红外区域的光谱反射率与大豆ADM、LAI及产量均有显著相关, 尤其在R4和R5期相关性最高。在构建大量光谱参数的基础上, 遴选出对ADM、LAI及产量预测精度较好的回归模型。其中, R5期的P_Area560光谱参数与LAI和R4期的V_Area1450光谱参数与ADM构建的两个生长性状的监测模型效果最好, 决定系数(R2)分别为0.582和0.692。未发现单一生育期光谱参数对大豆估产的有效模型, 但综合R2期NPH1280、R4期V_Area1190以及R5期NPH560构建的产量估测模型, 决定系数(R2)达到0.68, 效果较好。本研究

关键词: 大豆, 高光谱遥感, 冠层反射光谱, 地上部生物量, 叶面积指数, 产量

Abstract:

Modern plant breeding needs to monitor the growth and evaluate the yield potential for an accurate selection in a great number of breeding lines. The hyperspectral reflectance technology has been demonstrated to be potential in meeting this kind of requirement with a simple, fast and nondestructive technology. Thirty soybean cultivars from Middle and Lower Yangtze Valleys with close growing days to maturity were chosen and tested in a randomized blocks design experiment during the two consecutive years. The measurement of above-ground dry biomass (ADM) and leaf area index (LAI) was synchronized with the information collection of the canopy hyperspectral reflectance by using a portable spectroradiometer (FieldSpec Pro FR2500, Analytical Spectral Devices, Inc., Boulder, CO, USA) at three different growth stages (R2, R4, and R5) in soybean. Significant differences in ADM, LAI and plot yield among the tested cultivars were detected, which allowed a further regression analysis of the traits on the hyperspectral reflectances. There existed significant correlations between hyperspectral reflectance in the visible and infrared region and LAI, ADM, and yield, respectively. In particular, the highest correlations were observed at R4 and R5 stages. Based on a large number of spectral parameters in the literature, we selected the regression models with the best accuracy for ADM, LAI, and yield prediction. Among them, the regression model of LAI at R5 on P_Area560 and that of ADM at R4 on V_Area1450 were the best ones with their determination coefficients of 0.582 and 0.692, respectively. There was no single spectral index found for yield prediction. But the multiple regression of yield on NPH1280 at R2, V_Area1190 at R4 and NPH560 at R5 was found to provide a best yield prediction with R2=0.68. The obtained results suggested that hyperspectral remote sensing for monitoring growth status and estimating yields in soybean is feasible and potential, providing that a more accurate and stable regression model is searched based on an enlarged testing program under multiple environments. It might be especially useful and valuable for early generation yield prediction in a large-scale breeding program.

Key words: Soybean, Hyperspectral remote sensing, Canopy reflectance spectra, Above-ground dry biomass, Leaf area index, Yield

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