B006-0019
Seasonal patterns of gross primary productivity and solar-induced chlorophyll fluorescence over the northern land

Monday, 7 December 2020
Poster
Anping Chen1, Jiafu Mao2, Daniel M Ricciuto2, Dan Lu3 and Alan Knapp4, (1)Colorado State University, Fort Collins, CO, United States, (2)Oak Ridge National Laboratory, Environmental Sciences Division and Climate Change Science Institute, Oak Ridge, TN, United States, (3)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (4)Colorado State University, Department of Biology and Graduate Degree Program in Ecology, Fort Collins, CO, United States
Abstract:
Quantification of global gross primary productivity (GPP) is highly uncertain. Emerging evidence suggests that solar-induced chlorophyll fluorescence (SIF) can be a useful surrogate to approximate GPP and a simple linear relationship between SIF and GPP has been proposed. As SIF also tracks the seasonal change of plant photosynthesis, this linear SIF-GPP relationship should also hold across the year. This assumption, however, has rarely been tested. Here, using satellite-derived SIF products and flux-based GPP estimation, we investigated whether the relationship between SIF and GPP would remain constant across different seasons.

Results from 53 northern hemisphere flux sites shows varied GPP/GOME-2_SIF ratios across different months. Moreover, for most of the sites, GPP/SIF shows a hump-shaped pattern—higher at the mid of the growing season but lower at the beginning and end of the season. This hump-shaped seasonal pattern is robust for different satellite SIF and GPP products. GPP/SIF ratios are primarily controlled by climatic factors. However, the relative strength of temperature and precipitation controls of GPP/SIF ratios is also found to change substantially across different months. Overall, our work reveals an interesting seasonal pattern on the relationship between SIF and GPP, which highlights the importance of including the different responses of SIF and GPP to climatic variations for better predicting GPP from SIF observations.