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1. Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest

   https://doi.org/10.1111/gcb.13590  

   Yang, Hualei ; Yang, Xi ; Zhang, Yongguang ; Heskel, Mary A. ; Lu, Xiaoliang ; Munger, J. William ; Sun, Shucun ; Tang, Jianwu ( January 2017 , Global Change Biology)

   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 estimateGPPfrom the tower‐based measurement ofSIFand leaf‐level ChlF parameters. The estimation ofGPPfrom this model agreed well with flux tower observations ofGPP(R² = 0.68;P < 0.0001), demonstrating the potential ofSIFfor modelingGPP. At the leaf scale, we found that leafF_q’/F_m’, 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 canopySIFyield (SIF/APAR,R² = 0.79;P < 0.0001). We also found that canopySIFandSIF‐derivedGPP(GPP_SIF) were strongly correlated to leaf‐level biochemistry and canopy structure, including chlorophyll content (R² = 0.65 for canopyGPP_SIFand chlorophyll content;P < 0.0001), leaf area index (LAI) (R² = 0.35 for canopyGPP_SIFandLAI;P < 0.0001), and normalized difference vegetation index (NDVI) (R² = 0.36 for canopyGPP_SIFandNDVI;P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.

    

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2. Representation of Leaf‐to‐Canopy Radiative Transfer Processes Improves Simulation of Far‐Red Solar‐Induced Chlorophyll Fluorescence in the Community Land Model Version 5

   https://doi.org/10.1029/2021MS002747  

   Li, Rong ; Lombardozzi, Danica ; Shi, Mingjie ; Frankenberg, Christian ; Parazoo, Nicholas C. ; Köhler, Philipp ; Yi, Koong ; Guan, Kaiyu ; Yang, Xi ( March 2022 , Journal of Advances in Modeling Earth Systems)

   Recent advances in satellite observations of solar‐induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf‐level to canopy‐level is usually not well‐represented. Here, we incorporate the simulation of far‐red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf‐level fluorescence yield was simulated by a parametric simplification of the Soil Canopy‐Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf‐level to top‐of‐canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R² > 0.91, root‐mean‐square error <0.19 W⋅m⁻²⋅sr⁻¹⋅μm⁻¹), and captured the day‐to‐day variation of tower‐measured SIF at temperate forest sites (R² > 0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite‐observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

    

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3. Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence

   https://doi.org/10.3389/ffgc.2021.695269  

   Yazbeck, Theresia ; Bohrer, Gil ; Gentine, Pierre ; Ye, Luping ; Arriga, Nicola ; Bernhofer, Christian ; Blanken, Peter D. ; Desai, Ankur R. ; Durden, David ; Knohl, Alexander ; et al ( December 2021 , Frontiers in Forests and Global Change)

   Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accounting for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions. 

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4. Evaluating photosynthetic activity across Arctic-Boreal land cover types using solar-induced fluorescence

   https://doi.org/10.1088/1748-9326/ac9dae  

   Cheng, Rui ; Magney, Troy S ; Orcutt, Erica L ; Pierrat, Zoe ; Köhler, Philipp ; Bowling, David R ; Bret-Harte, M Syndonia ; Euskirchen, Eugénie S ; Jung, Martin ; Kobayashi, Hideki ; et al ( November 2022 , Environmental Research Letters)

   Abstract Photosynthesis of terrestrial ecosystems in the Arctic-Boreal region is a critical part of the global carbon cycle. Solar-induced chlorophyll Fluorescence (SIF), a promising proxy for photosynthesis with physiological insight, has been used to track gross primary production (GPP) at regional scales. Recent studies have constructed empirical relationships between SIF and eddy covariance-derived GPP as a first step to predicting global GPP. However, high latitudes pose two specific challenges: (a) Unique plant species and land cover types in the Arctic–Boreal region are not included in the generalized SIF-GPP relationship from lower latitudes, and (b) the complex terrain and sub-pixel land cover further complicate the interpretation of the SIF-GPP relationship. In this study, we focused on the Arctic-Boreal vulnerability experiment (ABoVE) domain and evaluated the empirical relationships between SIF for high latitudes from the TROPOspheric Monitoring Instrument (TROPOMI) and a state-of-the-art machine learning GPP product (FluxCom). For the first time, we report the regression slope, linear correlation coefficient, and the goodness of the fit of SIF-GPP relationships for Arctic-Boreal land cover types with extensive spatial coverage. We found several potential issues specific to the Arctic-Boreal region that should be considered: (a) unrealistically high FluxCom GPP due to the presence of snow and water at the subpixel scale; (b) changing biomass distribution and SIF-GPP relationship along elevational gradients, and (c) limited perspective and misrepresentation of heterogeneous land cover across spatial resolutions. Taken together, our results will help improve the estimation of GPP using SIF in terrestrial biosphere models and cope with model-data uncertainties in the Arctic-Boreal region. 

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5. Optimal carbon partitioning helps reconcile the apparent divergence between optimal and observed canopy profiles of photosynthetic capacity

   https://doi.org/10.1111/nph.17199  

   Buckley, Thomas N. ( March 2021 , New Phytologist)

   Photosynthetic capacity per unit irradiance is greater, and the marginal carbon revenue of water (∂A/∂E) is smaller, in shaded leaves than sunlit leaves, apparently contradicting optimization theory. I tested the hypothesis that these patterns arise from optimal carbon partitioning subject to biophysical constraints on leaf water potential.

   In a whole plant model with two canopy modules, I adjusted carbon partitioning, nitrogen partitioning and leaf water potential to maximize carbon profit or canopy photosynthesis, and recorded how gas exchange parameters compared between shaded and sunlit modules in the optimum.

   The model predicted that photosynthetic capacity per unit irradiance should be larger, and ∂A/∂Esmaller, in shaded modules compared to sunlit modules. This was attributable partly to radiation‐driven differences in evaporative demand, and partly to differences in hydraulic conductance arising from the need to balance marginal returns on stem carbon investment between modules. The model verified, however, that invariance in the marginal carbon revenue of N (∂A/∂N) is in fact optimal.

   The Cowan–Farquhar optimality solution (invariance of ∂A/∂E) does not apply to spatial variation within a canopy. The resulting variation in carbon–water economy explains differences in capacity per unit irradiance, reconciling optimization theory with observations.

    

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