We estimated the light use efficiency (LUE) via vegetation canopy chlorophyll content (CCCcanopy) based on in situ measurements of spectral reflectance, biophysical characteristics, ecosystem CO 2 fluxes and micrometeorological factors over a maize canopy in Northeast China. The results showed that among the common chlorophyll-related vegetation indices (VIs), CCCcanopy had the most obviously exponential relationships with the red edge position (REP) (R2 = .97, p < .001) and normalized difference vegetation index (NDVI) (R2 = .91, p < .001). In a comparison of the indicating performances of NDVI, ratio vegetation index (RVI), wide dynamic range vegetation index (WDRVI), and 2-band enhanced vegetation index (EVI2) when estimating CCCcanopy using all of the possible combinations of two separate wavelengths in the range 400-1300 nm, EVI2 [1214, 1259] and EVI2 [726, 1248] were better indicators, with R2 values of .92 and .90 (p < .001). Remotely monitoring LUE through estimating CCCcanopy derived from field spectrometry data provided accurate prediction of midday gross primary productivity (GPP) in a rainfed maize agro-ecosystem (R2 = .95, p < .001). This study provides a new paradigm for monitoring vegetation GPP based on the combination of LUE models with plant physiological properties.
Keywords: canopy chlorophyll content; eddy covariance; hyperspectral remote sensing; light use efficiency; spectral vegetation indices.