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Acta Agron Sin ›› 2013, Vol. 39 ›› Issue (02): 309-318.doi: 10.3724/SP.J.1006.2013.00309

• TILLAGE & CULTIVATION · PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

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 Online:2013-02-12 Published:2012-12-11
  • Contact: 盖钧镒, E-mail: sri@njau.edu.cn

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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