Journal of
Computational Biology and Bioinformatics Research

  • Abbreviation: J. Comput. Biol. Bioinform. Res
  • Language: English
  • ISSN: 2141-2227
  • DOI: 10.5897/JCBBR
  • Start Year: 2009
  • Published Articles: 41

Full Length Research Paper

Establishment of rice yield prediction model using canopy reflectance

K. W. Chang
Department of Leisure and Recreation Studies, Aletheia University, Tainan, 721, Taiwan, Republic of China.
Email: [email protected]

  •  Accepted: 04 September 2012
  •  Published: 31 December 2012


The major objectives of this study were to identify spectral characteristics associated with rice yield and to establish their quantitative relationships. Field experiments were conducted at Shi-Ko experimental farm of TARI’s Chiayi Station, during 2001 to 2005. Rice cultivar Tainung 67 (Oryza sativa L.), the major cultivar grown in Taiwan, was used in the study. Various levels of rice yield were obtained via nitrogen application treatments. Canopy reflectance spectra were measured during entire growth period and dynamic changes of characteristic spectrum were analyzed. Relationships among rice yields and characteristic spectrum were studied to establish yield estimation models suitable for remote sensing purposes. Spectrum analysis indicated that the changes of canopy reflectance spectrum were least during booting stages. Therefore, the canopy reflectance spectra during this period were selected for model development. Two multiple regression models, constituting of band ratios (NIR/RED and NIR/GRN) were then constructed to estimate rice yields for first and second crops separately. Results of the validation experiments indicated that the derived regression equations successfully predicted rice yield using canopy reflectance measured at booting stage unless other severe stresses occurred afterward.


Key words: Rice yield, canopy reflectance, prediction model.