Waveband selection using a phased regression with a bootstrap procedure for estimating legume content in a mixed sown pasture
2011
Kawamura, Kensuke | Watanabe, Nariyasu | Sakanoue, Seiichi | Lee, Hyo-Jin | Inoue, Yoshio
Legume content in grass-legume mixtures is a key parameter for deciding the forage quality and the amount of fertilizer application to the pasture due to nitrogen (N) fixation. To estimate legume content in a grass-white clover (WC) mixed pasture in Hokkaido, we searched for robust hyperspectral wavebands from in situ canopy reflectance spectra over the 400-2350 nm range comparing a phased regression with a bootstrap procedure (PHR-BS) (Ferwerda et al. 2006) and forward stepwise multiple linear regression (FS-MLR). Canopy reflectance data and plant samples were obtained from 50 selected sites during two seasons (n = 100); spring (May) and summer (July) 2007. Although selected wavebands were similar in the PHR-BS and FS-MLR, PHR-BS gave a higher predictive accuracy (44-74%) than FS-MLR (35-73%). Selected wavebands in the final models were blue (400-456 nm) and red bands (659-670 nm) in visible wavelength, red-edge region (704-724 nm), near infrared regions (813, 937, and 1121 nm), and shortwave infrared regions (2303-2344 nm) that are mainly linked to known biochemical components such as chlorophyll, N, lignin and cellulose. These results suggest that legume content in grass-legume mixtures can be predicted by in situ canopy reflectance, and that the predictive ability of the model can be improved by wavelength selection using the PHR-BS method.
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