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  • 1
    Publication Date: 2022-05-25
    Description: Author Posting. © The Author(s), 2016. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Global Change Biology 23 (2017): 2874-2886, doi: 10.1111/gcb.13590.
    Description: Accurate estimation of terrestrial gross primary productivity (GPP) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales, and explored how leaf-level ChlF was linked with canopy-scale solar induced chlorophyll fluorescence (SIF) in a temperate deciduous forest at Harvard Forest, Massachusetts, USA. Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R2=0.73, 0.77 and 0.86 at leaf, canopy and satellite scales, respectively; p〈0.0001). We developed a model to estimate GPP from the tower-based measurement of SIF and leaf-level ChlF parameters. The estimation of GPP from this model agreed well with flux tower observations of GPP (R2=0.68; p〈0.0001), demonstrating the potential of SIF for modeling GPP. At the leaf scale, we found that leaf Fq’/Fm’, the fraction of absorbed photons that are used for photochemistry for a light adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopy-SIF yield (SIF/APAR, R2=0.79; p〈0.0001). We also found that canopy-SIF and SIF-derived GPP (GPPSIF) were strongly correlated to leaf-level biochemistry and canopy structure, including chlorophyll content (R2=0.65 for canopy-GPPSIF and chlorophyll content; p〈0.0001), leaf area index (LAI) (R2=0.35 for canopy-GPPSIF and LAI; p〈0.0001), and normalized difference vegetation index (NDVI) (R2=0.36 for canopy-GPPSIF and NDVI; p〈0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.
    Description: This research was supported by U.S. Department of Energy Office of Biological and Environmental Research Grant DE-SC0006951, National Science Foundation Grants DBI-959333 and AGS-1005663, and the University of Chicago and the MBL Lillie Research Innovation Award to J. Tang, National Science Foundation of China Grants (41671421) to Y. Zhang, and China Scholarship Council (CSC) to H. Yang.
    Description: 2017-12-14
    Keywords: Solar induced fluorescence ; Photosynthesis ; Gross primary production ; Chlorophyll ; Vegetation indices ; Carbon cycle
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
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  • 2
    Publication Date: 2022-05-26
    Description: Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here for personal use, not for redistribution. The definitive version was published in Remote Sensing of Environment 179 (2016): 1-12, doi:10.1016/j.rse.2016.03.026.
    Description: Understanding the temporal patterns of leaf traits is critical in determining the seasonality and magnitude of terrestrial carbon and water fluxes. However, robust and efficient ways to monitor the temporal dynamics of leaf traits are lacking. Here we assessed the potential of using leaf spectroscopy to predict leaf traits across their entire life cycle, forest sites, and light environments (sunlit vs. shaded) using a weekly sampled dataset across the entire growing season at two temperate deciduous forests. The dataset includes field measured leaf-level directional-hemispherical reflectance/transmittance together with seven important leaf traits [total chlorophyll (chlorophyll a and b), carotenoids, mass-based nitrogen concentration (Nmass), mass-based carbon concentration (Cmass), and leaf mass per area (LMA)]. All leaf properties, including leaf traits and spectra, varied significantly throughout the growing season, and displayed trait-specific temporal patterns. We used a Partial Least Square Regression (PLSR) analysis to estimate leaf traits from spectra, and found a significant capability of PLSR to capture the variability across time, sites, and light environment of all leaf traits investigated (R2=0.6~0.8 for temporal variability; R2=0.3~0.7 for cross-site variability; R2=0.4~0.8 for variability from light environments). We also tested alternative field sampling designs and found that for most leaf traits, biweekly leaf sampling throughout the growing season enabled accurate characterization of the leaf trait seasonal patterns. Increasing the sampling frequency improved in the estimation of Nmass, Cmass and LMA comparing with foliar pigments. Our results, based on the comprehensive analysis of spectra-trait relationships across time, sites and light environments, highlight the capacity and potential limitations to use leaf spectra to estimate leaf traits with strong seasonal variability, as an alternative to time-consuming traditional wet lab approaches.
    Description: This research was supported by the Brown University–Marine Biological Laboratory graduate program in Biological and Environmental Sciences, and Marine Biological Laboratory start-up funding for JT. JT was also partially supported by the U.S. Department of Energy (U.S. DOE) Office of Biological and Environmental Research grant DE-SC0006951 and the National Science Foundation grants DBI-959333 and AGS-1005663. SPS was supported in part by the U.S. DOE contract No. DE-SC00112704 to Brookhaven National Laboratory. JW was supported by the NASA Earth and Space Science Fellowship (NESSF2014).
    Keywords: Phenology ; Leaf physiology ; Foliar chemistry ; Carbon cycle ; Chlorophyll ; Carotenoids ; Nitrogen ; Leaf mass per area ; Partial least square regression (PLSR) ; Sun and shaded leaves
    Repository Name: Woods Hole Open Access Server
    Type: Preprint
    Location Call Number Expected Availability
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