Chin J Plant Ecol ›› 2010, Vol. 34 ›› Issue (7): 845-854.DOI: 10.3773/j.issn.1005-264x.2010.07.010

• Research Articles • Previous Articles     Next Articles

Estimating leaf carotenoid content with hyperspectral parameters in rice

YANG Jie, TIAN Yong-Chao, YAO Xia, CAO Wei-Xing, ZHU Yan*()   

  1. Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
  • Received:2009-11-26 Accepted:2010-03-01 Online:2010-11-26 Published:2010-07-01
  • Contact: ZHU Yan


Aims Our objectives were to detect the relationship between leaf carotenoid (Car) content and spectral reflectance characteristics and to recommend useful hyperspectral wavebands and hyperspectral indices for nondestructive and quick estimation of Car content in rice (Oryza sativa).

Methods Four field experiments with different nitrogen application rates and rice cultivars were conducted at different eco-sites over three years. Time-course measurements were taken on hyperspectral reflectance of 350- 2 500 nm and Car content in four top leaves. We calculated the simple ratio spectral index (SR (λ1, λ2)) and normalized difference spectral index (ND (λ1, λ2)) with all combinations of two wavelengths (λ1 and λ2 nm) as well as other indices sensitive to Car, and analyzed the relationships between Car content to single wavelength reflectance and these spectral indices.

Important findings Spectral reflectance varied with Car content, and the sensitive wavebands mostly occurred at green and red edge regions. The SR indices using reflectance around 723 nm combined with near infrared reflectance (NIR) or the ND indices using reflectance around 713 nm combined with NIR could be used to estimate leaf Car content in rice, among which the SR (723, 770) and ND (770, 713) have the best performance, with determination of coefficients (R2) of 0.897 and 0.898, respectively. Tests with an independent dataset showed that R2 values between observed and predicted Car content with SR (723, 770) and ND (770, 713) were 0.856 and 0.858, with root meansquare error (RMSE) as 0.072, and relative error (RE) as 11.9% and 12.0%, respectively, which indicated that Car content in top leaves of rice could be predicted effectively with these two indices.

Key words: carotenoid content, hyperspectral, Oryza sativa, spectral index