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Title:   Author  item
                                            ZHANG, Q. - Universities Space Research Associatonitem
                                            MIDDLETON, E.M. - National Aeronautics And Space Administration (NASA)item
                                            CHENG, Y. - Collaboratoritem
                                            HUEMMRICH, K.F. - University Of Marylanditem
                                            COOK, B. D. - National Aeronautics And Space Administration (NASA)item
                                            CORP, L.A. - Sigma Space Corporationitem
                                            Kustas, William

                                                         - *Bill*item
                                            Russ, Andrew

                                                         - *Andy*item
                                            Prueger, Johnitem
                                            YAO, TIAN - Collaborator
  Submitted to: Remote Sensing of Environment
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 8/12/2016
                                    Publication  Date: 9/6/2016
                                    Citation: Zhang, Q., Middleton, E., Cheng, Y., Huemmrich, K., Cook, B., Corp, L., Kustas, W.P., Russ, A.L., Prueger, J.H., Yao, T. 2016. Integrating fAPARchl and PRInadir from EO-1/Hyperion to predict cornfield daily gross primary production (GPP). Remote Sensing of Environment. 186:311-321.Interpretive Summary: Accurate estimates of terrestrial carbon sequestration are essential for evaluating changes in the  carbon cycle due to global climate change. In a recent assessment of 26 carbon assimilation models at 39 FLUXNET tower sites across the United States and Canada, all models failed to adequately compute monthly gross primary production (GPP) within observed uncertainty in tower-based measurements. This limitation in estimating GPP is due in large part to inaccurate radiative transfer parameterizations of photosynthetic active radiation (PAR) through the canopy layer.  This study applied a remote sensing-based technique to more accurately estimate fraction of PAR absorbed by corn plants for photosynthesis and compute GPP based on a remote sensing estimate of the light use efficiency.  The computed daily crop GPP agreed much more closely with the measurements indicating that carbon assimilation models need to incorporate remote sensing-based techniques providing crop chlorophyll information to more accurately estimate light use efficiency and daily GPP.  This will significantly improve estimates of terrestrial carbon sequestration for agro-ecosystems, which is critical for evaluating changes in the carbon cycle of agricultural crops due to global climate change.
                                Technical Abstract: Accurate estimates of terrestrial carbon sequestration is essential for evaluating changes in the  carbon cycle due to global climate change. In a recent assessment of 26 carbon assimilation models at 39 FLUXNET tower sites across the United States and Canada, all models failed to adequately compute monthly gross primary production (GPP) within observed uncertainty in eddy-covariance based measurements (Schaefer et al. 2012). This limitation in estimating GPP is due in large part to inaccurate radiative transfer parameterizations of photosynthetic active radiation (PAR) through the canopy layer (Bonan et al. 2011). In this study, the fraction of the PAR absorbed (fAPAR) by crop chlorophyll (fAPARchl) instead of fAPAR by the whole canopy (fAPARcanopy) is used to estimate absorbed PAR for photosynthesis (APARPSN). Two methods of estimating light use efficiency at chlorophyll level (LUEchl) were tested to simulate daily GPP with the equation GPP=LUEchl*APARPSN: (1) using average LUEchl value; and (2) using photochemical reflectance index (PRI) to estimate LUEchl where APARPSN=fAPARchl*PAR. The coefficient of determination (R^2), the root mean square error (RMSE), coefficient of variation (CV) were the statistics used to assess performance of the two methods.  The second method had better performance with R^2=0.96, RMSE=1.34 g C/ mol PPFD, and CV=15%. This study suggests significant improvement in daily crop GPP is achieved by using fAPARchl. It also indicates that carbon assimilation models need to incorporate remote sensing-based techniques providing crop chlorophyll information to more accurately simulating light use efficiency.


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