Predicting landscape-scale CO₂ 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 5 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 interannual variability in the timing of seasonal patterns that propagated into interannual variability in measured hyperspectral reflectance. We used partial least-squares regression (PLSR) modeling to leverage the information contained within the entire 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 data set 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 data set 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 daily to monthly integrated flux timescales. The high level of predictability achieved by PLSR in this study demonstrated the potential for using repeated hyperspectral canopy reflectance measurements to help partition NEE into its component fluxes, GPP and ecosystem respiration, and for using quasi-continuous hyperspectral reflectance measurements to model regional carbon flux in future analyses.