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1. Sensing Plant Photosynthesis Using Solar-Induced Chlorophyll Fluorescence: From Chloroplasts to the Globe

   https://doi.org/10.1146/annurev-arplant-090324-115537  

   Yang, Xi; Guan, Kaiyu; Chen, Min; Johnson, Jennifer E; Li, Rong; Kimm, Hyungsuk; Wu, Genghong; Kim, Jongmin; Dashti, Hamid; Wang, Sheng; et al (May 2026, Annual Review of Plant Biology)

   Photosynthesis is the fundamental biological process that introduced oxygen into Earth's atmosphere and continues to power life, from the earliest single-celled organisms to entire global ecosystems. Yet, measuring photosynthesis across scales has been challenging because traditional techniques have not transcended scales. The emergence of remote-sensing techniques to measure solar-induced chlorophyll fluorescence (SIF) provides a unique approach to estimate photosynthesis across spatiotemporal scales, representing a new age for optical remote sensing to study photosynthesis and shaping the decades of satellite SIF research. Here, focusing on spatiotemporal scales, we review the mechanisms that drive the relationship between SIF and photosynthesis. Remotely sensed SIF is modulated by biological drivers, environmental drivers, the interaction between biological and environmental drivers, and the viewing geometry. Studying fluorescence at small scales provides the ecophysiological understanding needed to disentangle the biological and environmental drivers of SIF at larger scales. Leveraging progress in satellite SIF, future research should focus on cross-scale mechanistic understanding of the drivers of SIF and using SIF as a metric for plant function beyond photosynthesis. 

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2. Solar‐Induced Fluorescence Helps Constrain Global Patterns in Net Biosphere Exchange, as Estimated Using Atmospheric CO ₂ Observations

   https://doi.org/10.1029/2023JG007703  

   Zhang, Mingyang; Berry, Joseph A; Shiga, Yoichi P; Doughty, Russell B; Madani, Nima; Li, Xing; Xiao, Jingfeng; Wen, Jiaming; Sun, Ying; Miller, Scot M (December 2023, Journal of Geophysical Research: Biogeosciences)

   Abstract Solar‐induced chlorophyll fluorescence (SIF) shows enormous promise as a proxy for photosynthesis and as a tool for modeling variability in gross primary productivity and net biosphere exchange (NBE). In this study, we explore the skill of SIF and other vegetation indicators in predicting variability in global atmospheric CO₂observations, and thus global variability in NBE. We do so using a 4‐year record of CO₂observations from NASA's Orbiting Carbon Observatory 2 satellite and using a geostatistical inverse model. We find that existing SIF products closely correlate with space‐time variability in atmospheric CO₂observations, particularly in the extratropics. In the extratropics, all SIF products exhibit greater skill in explaining variability in atmospheric CO₂observations compared to an ensemble of process‐based CO₂flux models and other vegetation indicators. With that said, other vegetation indicators, when multiplied by photosynthetically active radiation, yield similar results as SIF and may therefore be an effective structural SIF proxy at regional to global spatial scales. Furthermore, we find that using SIF as a predictor variable in the geostatistical inverse model shifts the seasonal cycle of estimated NBE and yields an earlier end to the growing season relative to other vegetation indicators. These results highlight how SIF can help constrain global‐scale variability in NBE. 

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3. Mechanisms of ozone effects on plant stress in soybean across growing season: from leaf to regional perspective

   https://doi.org/10.1039/D5EM00785B  

   Mamić, Luka; Riches, Mj; Rossell, Rose K; Farmer, Delphine K (March 2026, Environmental Science: Processes & Impacts)

   Chamber experiments and satellite solar-induced fluorescence (SIF) measurements reveal that early-season ozone exposure induces stronger physiological stress in soybean than later or cumulative ozone exposure. Ground-level ozone (O3) is a major constraint on agricultural productivity, yet most knowledge comes from controlled fumigation experiments using chronic exposures that differ from the episodic conditions crops experience in the field. Here, we combine a five-week chamber experiment with multi-year satellite observations (2018–2021, Arkansas, U.S.) to investigate how O3 affects photosynthesis, efficiency, and growth across scales of soybean plants (Glycine max). At the leaf level, initial O3 fumigation (80 ppb for 4 h) caused the strongest suppression of CO2 assimilation (A), stomatal conductance (Gs), and photosystem II efficiency (ΦPSII), indicating entry into a physiological strain phase. Recovery between exposures was incomplete, leading to sustained growth reductions despite moderate O3 levels. At the regional scale, analysis of solar-induced fluorescence (SIF) and MODIS productivity metrics revealed parallel patterns. Early-season O3 episodes produced greater suppression of SIF, GPP, and Gs compared to equivalent late-season events, and recovery lagged for several weeks. Seasonal yield proxies were best explained not by total O3 accumulation, but by early- and peak-season exposures, which accounted for up to 98% of variance across four growing seasons. Our findings highlight that the timing of O3 episodes is more consequential than cumulative dose, and that functional indicators such as SIF can detect strain-phase stress before structural indices diverge. By linking controlled experiments with regional-scale satellite monitoring, this study advances mechanistic understanding of O3 impacts on soybean and supports the development of remote sensing-based early warning tools for crop management. 

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4. 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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5. Seasonal timing of fluorescence and photosynthetic yields at needle and canopy scales in evergreen needleleaf forests

   https://doi.org/10.1002/ecy.4402  

   Pierrat, Zoe Amie; Magney, Troy; Maguire, Andrew; Brissette, Logan; Doughty, Russell; Bowling, David R; Logan, Barry; Parazoo, Nicholas; Frankenberg, Christian; Stutz, Jochen (October 2024, Ecology)

   Abstract The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive “greenness” of these systems. We combine continuous needle‐scale chlorophyll fluorescence measurements with tower‐based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar‐induced chlorophyll fluorescence (SIF) and solar intensity‐normalized SIF (SIF_relative) under snow‐covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near‐infrared vegetation index [NIRv] under 50% snow cover). Needle‐scale fluorescence and photochemistry strongly correlated (r² = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower‐based estimates of LUE_Pand SIF_relativewhich were well correlated across all sites (r² = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data‐driven and process‐based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems. 

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