期刊文献+

长江中下游生态区大豆生长性状及产量的冠层高光谱偏最小二乘回归预测 被引量:1

Using Canopy Hyperspectral Reflectance to Predict Growth Traits and Seed Yield of Soybeans from Middle and Lower Yangtze Valleys through Partial Least Squares Regression
下载PDF
导出
摘要 高光谱遥感能够快速无损地估测作物生长性状及产量,这为作物规模化育种的田间评价与选择提供了高效手段。选用生育时期相似、生长性状有差异的52份大豆品种(系)进行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,同步测定大豆叶面积指数(LAI)和地上部生物量(ABM),收获后测定产量。针对不同生育时期冠层光谱与生长性状及产量进行偏最小二乘回归(PLSR)分析。结果表明:不同生育时期LAI的PLSR模型可以解释LAI总变异的54.4%~61.0%;不同生育时期ABM的PLSR模型可以解释ABM总变异的65.5%~67.0%;R5期是利用冠层光谱估测产量的最佳生育时期,其PLSR模型可以解释产量总变异的66.1%。本研究结果可望为大豆规模化育种中大量试验材料的田间长势监测和产量估测提供快速无损预测的技术支持。 Hyperspectral remote sensing technique as a fast and non-destructive method can estimate growth traits and yield in crop,which provides an effective tool for field evaluation and selection in large-scale breeding programs. In the present study,a field experiment comparing 52 soybean varieties with similar flowering and maturity dates were tested a randomized blocks design with three replications in two years. The measurement of leaf area index( LAI) and aboveground biomass( ABM) was synchronized with the information collection of the canopy hyperspectral reflectance at R2,R4,and R5 growth stages. The seed yield was acquired after harvest. The partial least squares regression( PLSR) between canopy spectral reflectance at different growth stages and growth traits and seed yield showed that the PLSR models of ABM and LAI at different growth stages could explain65. 5% ~ 67. 0% and 54. 4% ~ 61. 0% of the total variance of ABM and LAI,respectively,and R5 stage performed as the best of the three growth stages for predicting yield using canopy spectral reflectance with an explanation up to 66. 1% of the total seed yield variance. The results can serve a quick and non-destructive technique for monitoring field growing status and predicting yield in large-scale soybean breeding programs.
出处 《大豆科学》 CAS CSCD 北大核心 2015年第3期414-419,426,共7页 Soybean Science
基金 国家重点基础研究发展计划"973计划"(2011CB1093) 国家高技术研究发展计划"863计划"(2011AA10A105) 国家公益性行业(农业)专项经费项目(201203026-4) 教育部111项目(B08025) 教育部创新团队项目(PCSRT13073) 中央高校基本科研业务费项目(KYZ201202-8) 江苏省优势学科建设工程专项 江苏省JCIC-MCP项目资助
关键词 大豆 高光谱 叶面积指数 地上部生物量 产量 偏最小二乘回归 Soybean Hyperspectral reflectance Leaf area index(LAI) Aboveground biomass(ABM) Yield Partial least squares regression(PLSR)
  • 相关文献

参考文献29

  • 1Baez-Gonzalez A D, Kiniry J R, Maas S J, et al. Large-area maize yield forecasting using leaf area index based yield model [J]. Agronomy Journal, 2005, 97(2) : 418-425. 被引量:1
  • 2Bouman B. Crop modelling and remote sensing for yield prediction [J]. Netherlands Journal of Agricultural. Sciences, 1995, 43 (2) : 143-161. 被引量:1
  • 3Gutierrez M, Norton R, Thorp K R, et al. Association of spectral reflectance indices with plant growth and lint yield in upland cot- ton[J]. Crop Science, 2012, 52(2) : 849-857. 被引量:1
  • 4浦瑞良,宫鹏著..高光谱遥感及其应用[M].北京:高等教育出版社,2000:254.
  • 5Bakhshandeh E, Kamkar B, Tsialtas J T, et al. Application of linear models for estimation of leaf area in soybean [ Glycine max (L) Merr. ] [J]. Photosynthetiea, 2011,49(3) : 405-416. 被引量:1
  • 6Vifia A, Gitelson A A, Nguy-Robertson A L, et al. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops[ J]. Remote Sensing of Environment, 2011, 115 (12) : 3468-3478. 被引量:1
  • 7刘红辉,杨小唤,王乃斌.REMOTE SENSING BASED ESTIMATION SYSTEM FOR WINTER WHEAT YIELD IN NORTH CHINA PLAIN[J].Chinese Geographical Science,1999,9(1):40-48. 被引量:1
  • 8Raun W R, Solie J B, Johnson G V, et al. In-season prediction of potential grain yield in winter wheat using canopy reflectance [ J ]. Agronomy Journal, 2001,93( 1 ) : 131-138. 被引量:1
  • 9Prasad B, Carver B F, Stone M L, et al. Genetic analysis of indi- rect selection for winter wheat grain yield using spectral reflectance indices[J]. Crop Science, 2007, 47(4) : 1416-1425. 被引量:1
  • 10Casanova D, Epema G F, Goudriaan J, et al. Monitoring rice re- flectance at field level for estimating biomass and LAI [ J ]. Field Crops Research, 1998, 55(1-2) : 83-92. 被引量:1

二级参考文献50

  • 1薛利红,曹卫星,罗卫红.基于冠层反射光谱的水稻产量预测模型[J].遥感学报,2005,9(1):100-105. 被引量:46
  • 2Brogea N H, Mortensen J V. Deriving green crop areaindex and canopy chlorophyll density of winter wheat from spectral reflectance data [J]. Remote Sensing of Environment, 2002,81: 45- 57. 被引量:1
  • 3Chen J M, Cihlar J. Retrieving leaf area index of boreal conifer forests using Landsat TM images [J]. Remote Sensing of Environment, 1996,55 : 153- 162. 被引量:1
  • 4Chason J W, Balsocchi D D, et al. A comparion of direct and indirect methods for estimating forest canopy leaf area [J]. Agricultural and Forest Meterology, 1991,57: 107- 128. 被引量:1
  • 5Gitelson A A, Merzlyak M N. Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll[J]. J Plant Physical, 1996,148:494-500. 被引量:1
  • 6Blachburn G A. Quantifying chlorophylls and caroteniods at leaf and canopy scales: an evaluation of some hyperspectaral approaches [ J ]. Remote Sensing of Environment, 1998,66: 273- 285. 被引量:1
  • 7Blachburn G A, Milton E J. Seasonal variations in the spectral reflectance of deciduous tree canopies [J]. Int J Remote Sensing, 1995,16(4):709-720. 被引量:1
  • 8Shibayama M, Akiyama Y. Estimating grain yield of maturing rice canopy using high spectral resolution reflectance measurements [ J ]. Remote Sensing of Environment, 1991,36 : 45- 53. 被引量:1
  • 9Ian B. Strachana, Elizabeth. Pattey, Johanne B. Boisvert. Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance [J]. Remote Sensing of Environment, 2002,80 : 213- 224. 被引量:1
  • 10Stith T. Gower, Chris J, Kucharik, John M. Norman. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems[J]. Remote Sensing of Environment, 1999,70: 29- 51. 被引量:1

共引文献113

同被引文献11

引证文献1

二级引证文献6

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部