Deriving a light use efficiency estimation algorithm using in situ hyperspectral and eddy covariance measurements for a maize canopy in Northeast China

We estimated the light use efficiency (LUE) via vegetation canopy chlorophyll content (CCC (canopy)) 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), CCC (canopy) had the most obviously exponential relationships with the red edge position (REP) (R (2) = .97, p < .001) and normalized difference vegetation index (NDVI) (R (2) = .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 CCC (canopy) 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 R (2) values of .92 and .90 (p < .001). Remotely monitoring LUE through estimating CCC (canopy) derived from field spectrometry data provided accurate prediction of midday gross primary productivity (GPP) in a rainfed maize agro‐ecosystem (R (2) = .95, p < .001). This study provides a new paradigm for monitoring vegetation GPP based on the combination of LUE models with plant physiological properties.