Waveband selection using a phased regression with a bootstrap procedure for estimating legume content in a mixed sown pasture

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.