Abstract: Aims Accurate estimation of crop yield is important for agricultural policy formulation. Vegetation canopy reflectance spectra and carbon fluxes are the primary data for monitoring crop growth status. However, there are fewer studies comparing their performance in predicting crop yield. Methods Here, we explored the capability of various parameters to predict rice (Oryza sativa) grain yield and aboveground biomass using synchronized observations of vegetation canopy reflectance spectra and gas exchange. Important findings Results showed that vegetation reflectance index outperformed carbon flux parameters in estimating rice grain yield and aboveground biomass, in which the optimal estimation parameter was the near-infrared reflectance of vegetation (NIRv). The vegetative phase was the optimal estimation phase of rice grain yield and aboveground biomass. Our results could provide critical guidances for cropland yield estimation based on remote sensing data and ground flux data.

Key words: vegetation canopy reflectance index, grain yield, aboveground biomass, near-infrared reflectance of vegetation, solar-induced chlorophyll fluorescence