Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture

摘要

Airborne multispectral data were acquired by the Compact Airborne Spectral Imager (CASI) for an agricultural area in Denmark with the purpose of quantifying vegetation amount and variations in the physiological status of the vegetation. Spectral reflectances, vegetation indices, and red edge positions were calculated on the basis of the CAST data and compared to field measurements of green leaf area index (LAI. L) and canopy nitrogen concentrations (N{sub}c) sampled at 16 sites. Because of the variety of the samples with respect to vegetation type, leaf age, and phenological developmental stage, the data of L and N{sub}c were uncorrelated. The scattering effect of leaves effectuated a robust linear relationship between L and near-infrared (NIR) reflectance (r=.93), whereas the N{sub}c (vegetative period) was significantly correlated with the spectral reflectance in the green (r= - .88) and far-red wavebands (r= - .94). The correlations between vegetation indices and L were also important, in particular, for the enhanced vegetation index (EVI; r = .88), whereas the red edge position correlated less significantly with N{sub}c (r=.78). Assuming L and N{sub}c to be responsible for most of the spatial variability in the CO{sub}2 assimilation rates, remote sensing-based maps of these variables were produced for use in a coupled sun/shade photosynthesis/transpiration model. The predicted rates of net photosynthesis and transpiration compared reasonably with eddy covariance measurements of CO{sub}2 and water vapour fluxes recorded at four different crop fields. The results allowed evaluation of the spatial variations in the photosynthetic light, nitrogen, and water use efficiencies. While photosynthesis was linearly related to the transpiration, the light use efficiency (LUF) was found to be dependent on nitrogen concentrations.