Better representation of vegetation phenology improves estimations of annual gross primary productivity

Abstract

Carbon uptake by vegetation plays a vital role in the global carbon cycle. Annual gross primary productivity (AGPP) represents the total amount of carbon compounds produced by vegetation photosynthesis over a year and is a crucial metric for quantifying vegetation carbon uptake. Although some theories have been developed to explain the spatiotemporal variation in AGPP, they often overlook the seasonal differences in photosynthesis across vegetation phenological periods. This gap highlights the need for a more reasonable representation of vegetation phenology in AGPP modelling. Therefore, we developed a novel theoretical AGPP model that decomposes AGPP into detailed vegetation phenological periods (green-up, maturation, and senescence) and investigated the effects of climatic factors on the seasonal components of AGPP. Compared with existing models, our model considers the length of the multiple vegetation phenological periods, rather than just the length of the carbon uptake period. When evaluated against flux tower data, our model outperformed the comparative model, with a higher determination coefficient and lower root mean square error. The analysis also demonstrated that the developed model can reproduce spatial and temporal variations in satellite-based global AGPP. In addition, we identified distinct differences in the responses of AGPP's seasonal components to climatic factors: shortwave radiation predominantly affected the seasonal component of AGPP during senescence, air temperature during green-up, and vapor pressure deficit during maturation. Our study proposes a potential mechanism for AGPP estimation and highlights the importance of accurately representing vegetation phenology in AGPP modelling.