Predicting landscape-scale CO ₂ flux at a pasture and rice paddy with long-term hyperspectral canopy reflectance measurements

Abstract. Measurements of hyperspectral canopy reflectance provide a detailed snapshot of information regarding canopy biochemistry, structure and physiology. In this study, we collected five years of repeated canopy hyperspectral reflectance measurements for a total of over 100 site visits within the flux footprints of two eddy covariance towers at a pasture and rice paddy in Northern California. The vegetation at both sites exhibited dynamic phenology, with significant inter-annual variability in the timing of seasonal patterns that propagated into inter-annual variability in measured hyperspectral reflectance. We used partial least-squares regression (PLSR) modeling to leverage the information contained within the entire continuous canopy reflectance spectra (400–900 nm) in order to investigate questions regarding the connection between measured hyperspectral reflectance and landscape-scale fluxes of net ecosystem exchange (NEE) and gross primary productivity (GPP) across multiple timescales, from instantaneous flux to monthly-integrated flux. With the PLSR models developed from this large dataset we achieved a high level of predictability for both NEE and GPP flux in these two ecosystems, where the R2 of prediction with an independent validation dataset ranged from 0.24 to 0.69. The PLSR models achieved the highest skill at predicting the integrated GPP flux for the week prior to the hyperspectral canopy reflectance collection, whereas the NEE flux often achieved the same high predictive power at the daily- through monthly-integrated flux timescales. The high level of predictability achieved by PLSR regression in this study demonstrated the potential for using repeated hyperspectral canopy reflectance measurements to help partition NEE measurements into its component fluxes, GPP and ecosystem respiration, and for using continuous hyperspectral reflectance measurements to model regional carbon flux in future analyses.