作物学报Issue(7)：1073-1085,13.DOI:10.3724/SP.J.1006.2015.01073
利用高光谱技术估测大豆育种材料的叶面积指数
Prediction of Leaf Area Index Using Hyperspectral Remote Sensing in Breed-ing Programs of Soybean
摘要
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 hyper-spectral reflectance at R2, R4, and R5 growth stages in a field experiment with 52 soybean varieties under completely 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.关键词
大豆/高光谱/遥感/叶面积指数/地上部生物量Key words
Soybean/Hyperspectral reflectance/Remote sensing/Leaf area index (LAI)/Aboveground biomass (ABM)引用本文复制引用
齐波,张宁,赵团结,邢光南,赵晋铭,盖钧镒..利用高光谱技术估测大豆育种材料的叶面积指数[J].作物学报,2015,(7):1073-1085,13.基金项目
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。 （973计划）
