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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #305069
Title: Integrating fAPARchl and PRInadir from EO-1/Hyperion to predict cornfield daily gross primary production (GPP)

Author
item ZHANG, Q. - Universities Space Research Associaton
item MIDDLETON, E.M. - National Aeronautics And Space Administration (NASA)
item CHENG, Y. - Collaborator
item HUEMMRICH, K.F. - University Of Maryland
item COOK, B. D. - National Aeronautics And Space Administration (NASA)
item CORP, L.A. - Sigma Space Corporation
item Kustas, William - Bill
item Russ, Andrew - Andy
item Prueger, John
item 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.