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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

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ADMLAI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ADMLAI及产量均有显著相关, 尤其在R4R5期相关性最高。在构建大量光谱参数的基础上, 遴选出对ADMLAI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LAIR4期的V_Area1450光谱参数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0.5820.692。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R2NPH1280R4V_Area1190以及R5NPH560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

[1]Ma B L, Morrison M J, Dwyer L M. Canopy Light reflectance and field greenness to assess nitrogen fertilization and yield of Maize. Agron J, 1996, 88: 915–920



[2]Clevers J G P W. A simplified approach for yield prediction of sugar beet based on optical remote sensing data. Remote Sens Environ, 1997, 61: 221–228



[3]Clevers J G P W, Büker C, van Leeuwen H J C, Bouman B A M. A framework for monitoring crop growth by combining directional and spectral remote sensing information. Remote Sens Environ, 1994, 50: 161–170



[4]Vaesen K, Gilliams S, Nackaerts K, Coppin P. Ground-measured spectral signatures as indicators of ground cover and leaf area index: the case of paddy rice. Field Crops Res, 2001, 69: 13–25



[5]Slafer G A, Molina-Cano J L, Savin R, Araus J L, Romagosa I. Barley Science: Recent Advances from Molecular Biology to Agronomy of Yield and Quality. Binghamton, NH: Haworth Press, 2002. pp 387–412



[6]Filella I, Serrano L, Serra J, Penuelas J. Evaluating wheat nitrogen status with canopy re?ectance indices and discriminant analysis. Crop Sci, 1995, 35: 1400–1405



[7]Aparicio N, Villegas D, Araus J L, Casadesus J, Royo C. Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Sci, 2002, 42: 1547–1555



[8]Aparicio N, Villegas D, Casadesus J, Araus J L, Royo C. Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron J, 2000, 92: 83–91



[9]Aparicio N, Villegas D, Royo C, Casadesus J, Araus J L. Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral re?ectance measurements in durum wheat under contrasting Mediterranean conditions. Int J Remote Sens, 2004, 25: 1131–1152



[10]Royo C, Aparicio N, Villegas D, Casadesus J, Monneveux P, Araus J L. Usefulness of spectral re?ectance indices as durum wheat yield predictors under contrasting Mediterranean environments. Int J Remote Sens, 2003, 24: 4403–4419



[11]Shibayama M, Akiyama T. Seasonal visible, near-infrared and mid-infrared spectra of rice canopies in relation to LAI and above-ground dry phytomass. Remote Sens Environ, 1989, 27: 119–127



[12]Hansen P M, Schjoerring J K. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sens Environ, 2003, 86: 542–553



[13]Wang X-Z(王秀珍), Huang J-F(黄敬峰), Li Y-M(李云梅), Wang R-C(王人潮). Study on hperspectral remote sensing estimation models for the ground fresh biomass of rice. Acta Agron Sin (作物学报), 2003, 29(6): 815–821 (in Chinese with English abstract)



[14]Liu L-Y(刘良云), Wang J-H(王纪华), Huang W-J(黄文江), Zhao C-J(赵春江), Zhang B(张兵), Tong Q-X(童庆禧). Improving winter wheat yield prediction by novel spectral index. Trans Chin Soc Agric Eng (农业工程学报), 2004, 20(1): 172–175 (in Chinese with English abstract)



[15]Tang Y-L(唐延林), Huang J-F(黄敬峰), Wang R-C(王人潮), Wang F-M(王福民). Comparison of yield estimation simulated models of rice by remote sensing. Trans CSAE (农业工程学报), 2004, 20(1): 166–171 (in Chinese with English abstract)



[16]Xue L-H(薛利红), Cao W-X(曹卫星), Luo W-H(罗卫红). Rice yield forecasting model with canopy reflectance spectra. J Remote Sens (遥感学报), 2005, 9(1): 100–105 (in Chinese with English abstract)



[17]Moran M S, Inoue Y, Barnes E M. Opportunities and limitations for image based remote sensing in precision crop management. Remote Sens Environ, 1997, 61: 319–346



[18]Curran P J, Dungan J L, Gholz H L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine. Tree Physiol, 1990, 7: 33–48



[19]Pu R-L(浦瑞良), Gong P(宫鹏). Hyperspectral Remote Sensing and Its Applications (高光谱遥感及其应用). Beijing: Higher Education Press, 2000 (in Chinese)



[20]Miller J R, Wu J Y, Boyer M G, Belanger M, Hare E W. Season patterns in leaf reflectance red edge characteristics. Intl J Remote Sens, 1991, 12: 1509–1523



[21]Demetriades-Shah T H, Steven M D, Clark J A. High resolution derivative spectra in remote sensing. Remote Sens Environ, 1990, 33: 55–64



[22]Kokaly R F, Clark R N. Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression. Remote Sens Environ, 1999, 67: 267–287



[23]Clark R N, Roush T L. Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. J Geophys Res, 1984, 89: 6329–6340



[24]Gamon J A, Surfus J S. Assessing leaf pigment content and activity with a reflectometer. New Phytol, 1999, 143: 105–117



[25]Gitelson A A, Kaufman Y J, Stark R, Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ, 2002, 80: 76–87



[26]Metternicht G. Vegetation indices derived from high-resolution airborne videography for precision crop management, Int J Remote Sens, 2003, 24: 2855–2877



[27]Curran P J, Dungan J L, Peterson D L. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: testing the Kokaly and Clark methodologies. Remote Sens Environ, 2001, 76: 349–359



[28]Mutanga O, Skidmore A K, Prins H H T. Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sens Environ, 2004, 89: 393–408



[29]Feng W(冯伟). Mmonitoring nitrogen status and growth characters with canopy hyperspectral remote sensing in wheat. PhD Dissertation of Nanjing Agricultural University, 2007. p 31 (in Chinese with English abstract)



[30]Haboudane D, Miller J R, Pattey E, Zarco-Tejada P J, Strachan I B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ, 2004, 90: 337–352



[31]Yang F(杨飞), Zhang B (张柏), Song K-S(宋开山), Wang Z-M(王宗明), Liu D-W(刘殿伟), Liu H-J(刘焕军), Li F(李方), Li F-X(李凤秀), Guo Z-X(国志兴), Jin H-A(靳华安). Comparison of methods for estimating soybean leaf area index. Spectroscopy Spectral Anal (光谱学与光谱分析), 2008, 28(12): 2951–2955 (in Chinese with English abstract)

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