Characterizing measurement and interpretation challenges for tower-based solar-induced fluorescence data

Abstract:

Solar-induced chlorophyll fluorescence (SIF) has been widely cited as a proxy for photosynthesis, exhibiting a strong relationship to gross primary productivity (GPP) in satellite-based analyses. However, chlorophyll fluorescence does not exhibit a uniformly linear relationship with photosynthesis at finer scales. This is driven in part by the fact that SIF originates from a pathway competing with photochemistry for absorbed light, and is controlled by dynamic energy partitioning at the leaf level. Following induced stomatal closure in deciduous woody tree branches, we found no change in SIF despite clear reductions in stomatal conductance, carbon assimilation, and light-use efficiency in treated leaves. While SIF data may provide insight into the light reactions of photosynthesis, they do not directly track carbon assimilation. The SIF-GPP relationship observed from satellites may result from shared drivers, such as chlorophyll content or energy partitioning. Challenges in making and interpreting SIF retrievals are common at the tower scale. In a meta-analysis of the tower-based and airborne SIF literature, we found that mean SIF retrievals from unstressed vegetation spanned three orders of magnitude (0.041 mW m^-2 nm^-1 sr^-1 to 14.8 mW m^-2 nm^-1 sr^-1). In these same papers, we found inconsistent reporting on if and how key calibration methodology was performed. In our tower-based field system, we found dramatic changes in SIF retrieval magnitude before and after applying radiometric calibrations and corrections for electronic dark current, detector noise, atmospheric O₂ absorbance, and cosine corrector effects, as well as significant differences between instrument performance in the field and expected performance based on laboratory characterizations. Based on a Monte Carlo simulation of uncertainty estimates associated with these corrections, it is likely that calibration methodologies and hardware characterizations explain some of the observed variability in published SIF retrievals. SIF offers tremendous promise for improving the characterization of terrestrial carbon exchange, and a fuller understanding of the boundaries on its measurement and interpretation will help to create more reliable models of global productivity.