Abstract
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.
Highlights
The development of remote sensing tools that bridge the scale of carbon flux measurements from individual eddy covariance towers to broader, continuous spatial scales has long been a goal of the Earth systems science community (Bauer, 1975; Running et al, 1999; Ustin et al, 2004)
We investigated the ability of partial least-squares regression (PLSR) modeling with the hyperspectral canopy reflectance measurements to predict instantaneous gross primary productivity (GPP) and net ecosystem exchange (NEE) fluxes from the same half hour of spectral measurement, in addition to fluxes integrated over the previous day, week, and month
The PLSR models developed from hyperspectral canopy reflectance collected during 100 site visits from 2010 to 2014 at a Pasture and a Rice paddy achieved a high level of predictability for both NEE and GPP flux where the predictive R2 ranged from 0.24 to 0.69 using an independent validation data set
Summary
The development of remote sensing tools that bridge the scale of carbon flux measurements from individual eddy covariance towers to broader, continuous spatial scales has long been a goal of the Earth systems science community (Bauer, 1975; Running et al, 1999; Ustin et al, 2004). This goal inspired the formation of the international research group SpecNet, developed to synthesize the collection of near-surface ground reflectance measurements at eddy covariance tower sites to provide a crucial link between the spatial scales of eddy flux towers and aircraft or satellite measurements (Gamon et al, 2010).
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