欢迎访问作物学报,今天是

作物学报 ›› 2015, Vol. 41 ›› Issue (07): 1073-1085.doi: 10.3724/SP.J.1006.2015.01073

• 耕作栽培·生理生化 • 上一篇    下一篇

利用高光谱技术估测大豆育种材料的叶面积指数

齐波,张宁, 赵团结,邢光南,赵晋铭*,盖钧镒   

  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重点实验室,江苏南京 210095
  • 收稿日期:2015-02-04 修回日期:2015-05-04 出版日期:2015-07-12 网络出版日期:2015-05-15
  • 通讯作者: 盖钧镒, E-mail: sri@njau.edu.cn; 赵晋铭, E-mail: jmz3000@126.com
  • 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(2011CB1093), 国家高技术研究发展计划(863计划)项目(2011AA10A105),国家公益性行业(农业)科研专项经费项目(201203026-4), 教育部高等学校学科创新引智计划(111工程)项目(B08025),教育部创新团队项目(PCSRT13073),江苏省优势学科建设工程专项,江苏省现代作物生产协同创新中心项目(JCIC-MCP)和中央高校基本科研业务费项目(KYZ201202-8)资助。

Prediction of Leaf Area Index Using Hyperspectral Remote Sensing in Breeding Programs of Soybean

QI Bo,ZHANG Ning,ZHAO Tuan-Jie,XING Guang-Nan,ZHAO Jing-Ming*,GAI Jun-Yi*   

  1. Soybean Research Institute of Nanjing Agricultural University / National Center for Soybean Improvement / Key Laboratory for Biology and Genetic Improvement of Soybean (General), Ministry of Agriculture / National Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing 210095, China
  • Received:2015-02-04 Revised:2015-05-04 Published:2015-07-12 Published online:2015-05-15
  • Contact: 盖钧镒, E-mail: sri@njau.edu.cn; 赵晋铭, E-mail: jmz3000@126.com

摘要:

叶面积指数(LAI)是反映田间作物长势及产量潜力的重要参数,规模化育种要求及时、快速、无损地获取大量育种材料的田间生长信息。本研究利用52份大豆品种()2年田间试验,在盛花期(R2)、盛荚期(R4)及鼓粒初期(R5)测定大豆冠层反射光谱,同步测定大豆LAI和地上部生物量(ABM)。结果表明,不同生育期LAI与冠层光谱在可见光波段(426~710 nm)均表现显著负相关(P<0.05),在近红外波段(748~1331 nm)均表现为显著正相关(P<0.05)。根据文献已报道的植被指数与LAI的线性相关性分析,NDVIRVI类型的植被指数能够较好地指示大豆LAI,进而在全光谱250~2500 nm范围内涵盖上述两种类型的植被指数,经对建立的大豆LAI线性与非线性模型综合评价,遴选出不同生育期敏感植被指数的最优估测模型。其中,R2RVI (825, 586)所建模型(y = 0.03x1.83)R4RVI (763, 606)所建模型(y = 0.38e0.14x)R5RVI (744, 580)所建模型(y = 0.06x1.79)的预测表现最好,决定系数(R2)分别为0.6770.6390.664,相对标准误(RRMSE)均小于20%;模型验证的决定系数(R2*)分别为0.6430.6120.634,均根方误差(RRMSE*)20%。进而发现针对LAIABMRVI共性核心波段组合为R2期的825 nm586 nmR4763 nm606 nm以及R5744 nm580 nm。本研究结果可望为大豆规模化育种中获取大量不设重复试验材料的田间长势信息提供快速无损预测的技术支持。

关键词: 大豆, 高光谱, 遥感, 叶面积指数, 地上部生物量

Abstract:

Leaf area index (LAI) is an important parameter in observing field growth status and yield potential of crop plants, which is important in evaluating field growth performance of breeding lines in modern large scale plant breeding programs. The measurement of LAI and aboveground biomass (ABM) was synchronized with the information collection of the canopy hyperspectral reflectance at R2, R4, and R5 growth stages in a field experiment with 52 soybean varieties under randomized blocks design with three replications in two years. The results indicated that LAI have significant positive correlation with canopy spectral reflectance in the visible region (426–710 nm) and significant negative correlation in the near infrared region (748–1331 nm) (P<0.05). According to the linear correlation analysis between the vegetation indices and LAI in the literature, NDVI and RVI are superior vegetation indices for soybean LAI prediction. The linear and nonlinear regression models of LAI on NDVI and RVI vegetation indices were constructed and evaluated for all two–band combinations in the full spectral range of 350–2500 nm under 1 nm windows. Three single–stage regression models, i.e. R2 RVI (825, 586) model (y = 0.03x1.83), R4 RVI (763,606) model (y = 0.38e0.14x) and R5 RVI (744, 580) model (y = 0.06x1.79) were selected and validated as the best ones with fitness of 0.677, 0.639, 0.664 and less than 20% relative standard error, respectively, with their validation determination coefficients of 0.643, 0.612, 0.634, and around 20% validation standard error, respectively. Furthermore, the common core two–band combinations for both LAI and ABM prediction at R2, R4, and R5 were selected as 825 nm and 586 nm, 763 nm and 606 nm, and 744 nm and 580 nm, respectively. The obtained indices along with their prediction models can provide a technical support for quick and nondestructive field survey of soybean growth status in large scale breeding programs.

Key words: Soybean, Hyperspectral reflectance, Remote sensing, Leaf area index (LAI), Aboveground biomass (ABM).

[1]Watson D J. Comparative physiological studies on the growth of field crops: I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Ann Bot, 1947, 11: 41–76



[2]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



[3]Goetz S J, Prince S D. Remote sensing of net primary production in boreal forest stands. Agric For Meteorol, 1996, 78: 149–179



[4]Moran M S, Maas S J, Pinter Jr P J. Combining remote sensing and modeling for estimating surface evaporation and biomass production. Remote Sens Rev, 1995, 12: 335–353



[5]Tucker C J, Holben B N, Elgin J H, Jr., McMurtrey J E. Relationship of spectral data to grain yield variation. Photogramm Eng Rem S, 1980, 46: 657–666



[6]Lam H M, Xu X, Liu X, Chen W, Yang G, Wong F L, Li M W, He W, Qin N, Wang B, Li J, Jian M, Wang J, Shao G, Wang J, Sun S S, Zhang G. Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection. Nat Genet, 2010, 42: 1053–1059



[7]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



[8]Thenkabail P S, Smith R B, De Pauw E. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens Environ, 2000, 71: 158–182



[9]Mutanga O, Skidmore A K, van Wieren S. Discriminating tropical grass (Cenchrus ciliaris) canopies grown under different nitrogen treatments using spectroradiometry. Isprs J Photogr Remote Sens, 2003, 57: 263–272



[10]Miller J R, Hare E W, Wu J. Quantitative characterization of the vegetation red edge reflectance: 1. An inverted-Gaussian reflectance model. Intl J Remote Sens, 1990, 11: 1755–1773



[11]薛利红, 曹卫星, 罗卫红, 王绍华. 光谱植被指数与水稻叶面积指数相关性的研究. 植物生态学报, 2004, 28: 47–52



Xue L H, Cao W X, Luo W H, Wang S H. Relationship between spectral vegetation indices and LAI in rice. Acta Phytoecol Sin, 2004, 28: 47–52 ( in Chinese with English abstract)



[12]吴琼, 齐波, 赵团结, 姚鑫锋, 朱艳, 盖钧镒. 高光谱遥感估测大豆冠层生长和籽粒产量的探讨. 作物学报, 2013, 39: 309–318



Wu Q, Qi B, Zhao T J, Yao X F, Zhu Y, Gai J Y. A tentative study on utilization of canopy hyperspectral reflectance to estimate canopy growth and seed yield in soybean. Acta Agron Sin, 2013, 39: 309–318 (in Chinese with English abstract)



[13]宋开山, 张柏, 王宗明, 张渊智, 刘焕军. 基于人工神经网络的大豆叶面积高光谱反演研究. 中国农业科学, 2006, 39: 1138–1145



Song K S, Zhang B, Wang Z M, Zhang Y Z, Liu H J. Soybean LAI estimation with in-situ collected hyperspectral data based on BP-neural networks. Sci Agric Sin, 2006, 39: 1138–1145 (in Chinese with English abstract)



[14]黄春燕, 刘胜利, 王登伟, 战勇, 张恒斌, 袁杰, 马勤建, 陈燕, 赵鹏举. 大豆叶面积指数的高光谱估算模型研究. 大豆科学, 2008, 27: 228–232



Huang C Y, Liu S L, Wang D W, Zhan Y, Zhang H B, Yuan J, Ma Q J, Chen Y, Zhao P J. Models for estimating soybean leaf area index using hyperspectral data. Soybean Sci, 2008, 27: 228–232 (in Chinese with English abstract)



[15]Gitelson A A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J Plant Physiol, 2004, 161: 165–173



[16]Wang W, Yao X, Yao X F, Tian Y C, Liu X J, Ni J, Cao W D, Zhu Y. Estimating leaf nitrogen concentration with three-band vegetation indices in rice and wheat. Field Crops Res, 2012, 129: 90–98



[17]Gitelson A A, Merzlyak M N. Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J Plant Physiol, 1996, 148: 494–500



[18]Gutierrez-Rodriguez M, Escalante–Estrada J A, Rodriguez gonzalez M T, Reynolds J P. Canopy reflectance indices and its relationship with yield in common bean plants (Phaseolus vulgaris L.) with phosphorous supply. J Agric Biol, 2006, 2: 203–207



[19]Marshak A, Knyazikhin Y, Davis A B, Wiscombe W J, Pilewskie P. Cloud-vegetation interaction: use of normalized difference cloud index for estimation of cloud optical thickness. Geophys Res Lett, 2000, 27: 1695–1698



[20]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



[21]Blackburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens Environ, 1998, 66: 273–285



[22]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



[23]Loague K, Green R E. Statistical and graphical methods for evaluating solute transport models: Overview and application. J Contam Hydrol, 1991, 7: 51–73



[24]Jamieson P D, Porter J R, Wilson D R. A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Res, 1991, 27: 337–350



[25]Efron B, Stein C. The Jackknife estimate of variance. Ann Stat, 1981, 9: 586–596



[26]Efron B. The Jackknife, the Bootstrap and Other Resampling Plans. Philadelphia: Society for Industrial and Applied Mathematics, 1982. pp 14–22



[27]Heege H J, Reusch S, Thiessen E. Prospects and results for optical systems for site-specific on-the-go control of nitrogen-top-dressing in Germany. Precis Agric, 2008, 9: 115–131



[28]Erdle K, Mistele B, Schmidhalter U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res, 2011, 124: 74–84



[29]Goel P K, Prasher S O, Landry J A, Patel R M, Viau A A, Miller J R. Estimation of crop biophysical parameters through airborne and field hyperspectral remote sensing. Trans ASAE, 2003, 46: 1235–1246



[30]Zarco–Tejada P J, Miller J R, Noland T L, Mohammed G H, Sampson P H. Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE T Geosci Remote, 2001, 39: 1491–1507



[31]Baret F, Guyot G. Potentials and limits of vegetation indexes for lai and apar assessment. Remote Sens Environ, 1991, 35: 161–173



[32]Broge N H, Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sens Environ, 2001, 76: 156–172



[33]Chen J M. Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can J Remote Sens, 1996, 22: 229–242



[34]Brown L, Chen J M, Leblanc S G, Cihlar J. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis. Remote Sens Environ, 2000, 71: 16–25

[1] 梁进宇, 尹嘉德, 王红丽, 张国平, 侯慧芝, 董博, 马明生. 基于无人机高光谱和机器学习的旱地饲用玉米叶片氮含量估测[J]. 作物学报, 2026, 52(6): 1788-1801.
[2] 金昱何, 王雪菲, 徐张一娃, 缪怡宁, 蒋云杰, 伊莹, 缪德麟, 朱静仪, 钟一帆, 陈铭亨, 方芳, 刘鹏. 外源激素对低温胁迫下大豆叶片叶绿素荧光参数及抗氧化酶系统的影响[J]. 作物学报, 2026, 52(6): 1817-1829.
[3] 唐宽强, 李公允, 宋美毅, 赵雪, 常春玲. 大豆株高性状全基因组关联分析及预测模型构建[J]. 作物学报, 2026, 52(6): 1743-1756.
[4] 姚术, 郭凯悦, 翟慧慧, 姚佳慧, 邓文琪, 闫玲, 黄驰, 高阳, 俞嫣然, 赵振邦, 李英慧, 王晓波, 李佳佳. 大豆苗期耐低铁综合评价及优异种质筛选[J]. 作物学报, 2026, 52(5): 1373-1387.
[5] 杨月, 张新新, 贺增辉, 李瑞东, 潘昱洁, 李嘉康, 杜薇, 徐大勇, 堵劲松. 基于高光谱成像的烟叶主要化学成分无损检测与可视化[J]. 作物学报, 2026, 52(3): 922-935.
[6] 张晴, 杨昱, 郭茜, 岳霈尧, 殷丛丛, 牛景萍, 赵晋忠, 杜维俊, 岳爱琴. 大豆GmARA6a的克隆及响应盐胁迫的功能分析[J]. 作物学报, 2026, 52(2): 480-493.
[7] 王克晶, 李向华. 我国珍稀的大豆属多年生烟豆和短绒野大豆物种遗传资源濒危性评估分析[J]. 作物学报, 2025, 51(8): 2009-2019.
[8] 孟然, 李赵嘉, 冯薇, 陈悦, 刘路平, 杨春燕, 鲁雪林, 王秀萍. 大豆不同生育时期耐盐性综合评价及耐盐种质筛选[J]. 作物学报, 2025, 51(8): 1991-2008.
[9] 贺红利, 张雨涵, 杨静, 程云清, 赵杨, 李星诺, 司洪亮, 张兴政, 杨向东. 大豆e1-as基因突变体的创制及生理分析[J]. 作物学报, 2025, 51(8): 2228-2239.
[10] 胡蒙, 沙丹, 张晟瑞, 谷勇哲, 张世碧, 李静, 孙君明, 邱丽娟, 李斌. 大豆分枝数QTL定位及候选基因筛选[J]. 作物学报, 2025, 51(7): 1747-1756.
[11] 王琼, 邹丹霞, 陈兴运, 张威, 张红梅, 刘晓庆, 贾倩茹, 魏利斌, 崔晓艳, 陈新, 王学军, 陈华涛. 大豆开花时间和成熟期性状全基因组关联分析与候选基因预测[J]. 作物学报, 2025, 51(6): 1558-1568.
[12] 殷丛丛, 李睿琦, 岳霈尧, 李晨, 牛景萍, 赵晋忠, 杜维俊, 岳爱琴. 基于闭合哑铃介导等温扩增可视化检测大豆花叶病毒SC15方法的建立及应用[J]. 作物学报, 2025, 51(5): 1248-1260.
[13] 王清华, 朱格格, 方雯, 刘诗诗, 鲁剑巍. 基于高光谱遥感的油菜叶片氮磷养分含量诊断[J]. 作物学报, 2025, 51(5): 1326-1337.
[14] 许睿, 何妙华, 王昊, 李卫, 任杰, 夏志强. 基于空间转录组技术解析大豆种胚对X射线辐射的响应机制[J]. 作物学报, 2025, 51(12): 3121-3132.
[15] 林洋, 史晓蕾, 陈强, 刘兵强, 杨庆, 于慧娟, 闫龙, 武小霞, 杨春燕. 大豆蛋白质脂肪及脂肪酸组分相关QTL定位[J]. 作物学报, 2025, 51(11): 2899-2910.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!