Data From: Unpacking the drivers of diurnal dynamics of sun-induced chlorophyll fluorescence (SIF): Canopy structure, plant physiology, instrument configuration and retrieval methods
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Chang, Christine Y.; Sun, Ying; Gu, Lianhong; Wood, Jeffrey D.
Data in support of research: Sun-induced chlorophyll fluorescence (SIF) from spaceborne sensors is a promising tool for global carbon cycle monitoring, but its application is constrained by insufficient understanding of the drivers underlying diurnal SIF dynamics. SIF measurements from ground-based towers can reveal diurnal SIF dynamics across biomes and environmental conditions; however, meaningful interpretation of diurnal variations requires disentangling impacts from canopy structure, plant physiology, instrument configuration and retrieval methods, which often interact with and confound each other. This study aims to unpack these drivers using 1) concurrent ground and airborne canopy-scale and leaf-scale measurements at a corn field, 2) a mechanistic SIF model that explicitly considers the dynamics of photochemistry (via the fraction of open photosystem II reaction centers, qL) and photoprotection (via nonphotochemical quenching, NPQ) as well as their interactive dependence on the sub-canopy light environment, and 3) cross-comparison of SIF instrument configurations and retrieval methods. We found that crop row orientations and sun angles can introduce a distinctive midday dip in SIF in absence of stress, due to a midday drop of absorbed photosynthetically active radiation (APAR) when crop rows are north-south oriented. Canopy structure caused distinctive responses in both qL and NPQ at different positions within the vertical canopy that collectively influenced fluorescence quantum yield (ΦF) at the leaf scale. Once integrated at the canopy scale, diurnal dynamics of both APAR and canopy escape probability (ε) are critical for accurately shaping diurnal SIF variations. While leaf-level qL and NPQ exhibited strong diurnal dynamics, their influence was attenuated at the canopy scale due to opposing effects on SIF at different canopy layers. Furthermore, different system configurations (i.e., bi-hemispherical vs. hemispherical-conical) and retrieval methods can bias the SIF magnitude and distort its diurnal shapes, therefore confounding the interpretation of inherent strength and dynamics of SIF emission. Our findings demonstrate the importance of crop row structures, interactive variations in canopy structure and plant physiology, instrument configuration, and retrieval method in shaping the measured dynamics of diurnal SIF. This study highlights the necessity to account for these factors to accurately interpret satellite SIF, and informs future synthesis work with different SIF instrumentation and retrieval methods across sites.
Please cite as: Chang et al. (2022). Data from: Unpacking the drivers of diurnal dynamics of sun-induced chlorophyll fluorescence (SIF): Canopy structure, plant physiology, instrument configuration and retrieval methods [Dataset]. Cornell University Library eCommons Repository. https://doi.org/10.7298/mqfk-hs97
USDA-NIFA Postdoctoral Fellowship to CYC, Grant #: 2018-67012-27985 USDA-NIFA Hatch Award to YS, Grant #: 1014740 This research is also supported by the US Department of Energy (DOE), Office of Science, Biological and Environmental Research Program. ORNL is managed by UT-Battelle, LLC, for DOE under contract DE-AC05-00OR22725.
sun-induced chlorophyll fluorescence (SIF); diurnal SIF dynamics; mechanistic SIF model; canopy structure; plant physiology; crop row orientation; SIF instrumentation
Naethe, Paul, & Rascher, Uwe. (2021). Selected clear-sky FloX hyperspectral data (V.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5286019
Chang, C. Y., Wen, J., Han, J., Kira, O., LeVonne, J., Melkonian, J., Riha, S. J., Skovira, J., Ng, S., Gu, L., Wood, J. D., Näthe, P., & Sun, Y. (2021). Unpacking the drivers of diurnal dynamics of sun-induced chlorophyll fluorescence (SIF): Canopy structure, plant physiology, instrument configuration and retrieval methods. Remote Sensing of Environment, 265, 112672. https://doi.org/10.1016/j.rse.2021.112672
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