African Journal of Plant Breeding Vol. 1 (6), pp. 118-123, November, 2013. © International Scholars Journals
Full Length Research Paper
Absorption spectrum estimating rice chlorophyll concentration: Preliminary investigations
Jinheng Zhang1*, Chao Han1 and Zhiheng Liu1,2
1Institute of Eco-environment and Agriculture Information and College of Environment and Safety Engineering, Qingdao
University of Science and Technology, Qingdao, Shandong 266042, China.
2College of Science, Guizhou University, Guiyang, Guizhou 550025, China.
*Corresponding author. E-mail: firstname.lastname@example.org, email@example.com. Fax: 0086-532-84022617
Accepted 10 June, 2013
Our objective in this study was to develop spectral absorption indices for prediction of leaf chlorophyll concentration based on blue/yellow/red/ edge absorption spectrum. Two field experiments were conducted to study the response of chlorophyll index based on leaf absorption spectra to chlorophyll concentration in rice. The ultimate, penultimate and third expanded leaves were sampled for chlorophyll measurements and the absorption spectra of the leaves on the main stem for three rice varieties at different growth stages to select the absorption wavelength position near zero and develop better algorithms for estimating chlorophyll concentration. Some indices called blue/yellow/red/ edge absorption spectra chlorophyll index (BEACI/ YEACI/ REACI) were calculated from elected absorption wavelength positions. For the 1st experiment the correlation coefficients were similar between chlorophyll concentration and single leaf spectral absorption and between chlorophyll concentration and these indices. But the chlorophyll concentration had significant correlations (P<0.01) to these indices than single leaf spectral absorption in the 2nd experiment. The liner regression models with single leaf spectral absorption y = -2.271A480.188 + 5.574A651.232 - 2.899A753.552 - 0.269, y= -4.079A480.188 - 2.233A753.552 + 5.892A663.239 + 0.547 and y = 4.217A651.232 -0.718A753.552 - 2.897A663.239 - 0.399 had higher power prediction total chlorophyll, chlororphyll a and chlorophyll b concentrations, respectively. Compared with BEACI and REACI, stepwise regression analysis showed that YEACI630.610, YEACI570.169 and YEACI651.232 were good predictive power for predicting chlorophyll total concentration, chlorophyll a concentration and chlororphyll b concentration respectively.
Key words: Rice, chlorophyll concentration, leaf absorption spectrum, vegetation index.