期刊
JOURNAL OF FORESTRY RESEARCH
卷 34, 期 4, 页码 915-927出版社
NORTHEAST FORESTRY UNIV
DOI: 10.1007/s11676-022-01546-6
关键词
Gross primary production; Absorbed photosynthetically active radiation; Photosynthetic component; Vegetation index; AmeriFlux; European fluxes database
类别
Partitioning non-photosynthetic components is important for accurate estimation of gross primary production in deciduous forests. The use of vegetation indices and radiation absorption observations can effectively separate the radiation absorbed by photosynthetic components. The results of this study demonstrate that the APAR(green)-based method significantly improves daily GPP estimation.
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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