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.
Matthes, J. H.; Knox, S. H.; Sturtevant, C.; and Sonnentag, O., "Predicting Landscape-Scale CO 2 Flux at a Pasture and Rice Paddy with Long-Term Hyperspectral Canopy Reflectance Measurements" (2015). Open Dartmouth: Faculty Open Access Articles. 553.