Normalized difference spectral indices for estimating photosynthetic efficiency and capacity at a canopy scale derived from hyperspectral and CO2 flux measurements in rice

We explored simple and useful spectral indices for estimating photosynthetic variables (radiation use efficiency and photosynthetic capacity) at a canopy scale based on seasonal measurements of hyperspectral reflectance, ecosystem CO2 flux, and plant and micrometeorological variables. An experimental study was conducted over the simple and homogenous ecosystem of an irrigated rice field. Photosynthetically active radiation absorbed by the canopy (APAR), the canopy absorptivity of APAR (fAPAR), net ecosystem exchange of CO2 (NEECO2) gross primary productivity (GPP), photosynthetic capacity at the saturating APAR (Pmax), and three parameters of radiation use efficiency (εN: NEECO2/APAR; εG: GPP/APAR; φ: quantum efficiency) were derived from the data set. Based on the statistical analysis of relationships between these ecophysiological variables and reflectance indicators such as normalized difference spectral indices (NDSI[i,j]) using all combinations of two wavelengths (i and j nm), we found several new indices that would were more effective than conventional spectral indices such as photochemical reflectance index (PRI) and normalized difference vegetation index (NDVI = NDSI[near-infrared, red]). εG was correlated well with NDSI[710, 410], NDSI[710, 520], and NDSI[530, 550] derived from nadir measurements. φ was best correlated with NDSI[450, 1330]. NDSI[550, 410] and NDSI[720, 420] had a consistent linear relationships with fAPAR throughout the growing season, whereas conventional indices such as NDVI showed very different relationships before and after heading. Off-nadir measurements were more closely related to the efficiency parameters than nadir measurements. Our results provide useful insights for assessing plant productivity and ecosystem CO2 exchange, using a wide range of available spectral data as well as useful information for designing future sensors for ecosystem observations.