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Acta Agron Sin ›› 2015, Vol. 41 ›› Issue (07): 1073-1085.doi: 10.3724/SP.J.1006.2015.01073


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 Online:2015-07-12 Published:2015-05-15
  • Contact: 盖钧镒, E-mail: sri@njau.edu.cn; 赵晋铭, E-mail: jmz3000@126.com E-mail:mtsdhzhq@163.com


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).

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