Abstract
Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis. Therefore, the accuracy of gross primary production (GPP) estimates is expected to improve by removing these components. However, their influence in GPP estimations has not been quantitatively evaluated for deciduous forests. Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components (\({FAPAR}_{green}\)) for partitioning \({APAR}_{green}\) (photosynthetically active radiation absorbed by photosynthetic components). In this study, the enhanced vegetation index (EVI) estimated \({FAPAR}_{green}\) and to separate the photosynthetically active radiation absorbed by photosynthetic components (\({APAR}_{green}\)) from total APAR observations (\({APAR}_{total}\)) at two deciduous forest sites. The eddy covariance-light use efficiency (EC-LUE) algorithm was employed to evaluate the influence of non-photosynthetic components and to test the performance of \({APAR}_{green}\) in GPP estimation. The results show that the influence of non-photosynthetic components have a seasonal pattern at deciduous forest sites, large differences are observed with normalized root mean square error (RMSE*) values of \({APAR}_{green}\)-based GPP and \({APAR}_{total}\)-based GPP between tower-based GPP during the early and end stages, while slight differences occurred during peak growth seasons. In addition, daily GPP estimation was significantly improved using the \({APAR}_{green}\)-based method, giving a higher coefficient of determination and lower normalized root mean square error against the GPP estimated by the \({APAR}_{total}\)-based method. The results demonstrate the significance of partitioning \({APAR}_{green}\) from \({APAR}_{total}\) for accurate GPP estimation in deciduous forests.
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Acknowledgements
We acknowledge the supprot of the U.S. Department of Energy Office of Science for providing the AmeriFlux data and the European research framework programs (CarboEurope, FP6; CarboExtreme, FP7) for the flux and meteorological data for the FR-Fon site. We are further grateful for the data provided by the MODIS and VIIRS Land Products Global Subsetting and Visualization Tool.
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Project funding: This research was funded by Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals (No. CBAS2022IRP01), the National Earth System Science Data Sharing Infrastructure (No. 2005DKA32300), and the National Natural Science Foundation of China (No. 41825002).
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Chen, S., Liu, L., Sui, L. et al. Improving GPP estimates by partitioning green APAR from total APAR in two deciduous forest sites. J. For. Res. 34, 915–927 (2023). https://doi.org/10.1007/s11676-022-01546-6
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DOI: https://doi.org/10.1007/s11676-022-01546-6