# Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science

Albert Porcar-Castell, Zbyněk Malenovský, Troy Magney, Shari van Wittenberghe, Beatriz Fernández-Marín, Fabienne Maignan, Yongguang Zhang, Kadmiel Maseyk, Jon Atherton, Loren Albert, et al.

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## To cite this version:

Albert Porcar-Castell, Zbyněk Malenovský, Troy Magney, Shari van Wittenberghe, Beatriz Fernández-Marín, et al.. Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science. Nature Plants, 2021, 10.1038/s41477-021-00980-4, hal-03320325

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# Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science

Porcar-Castell, Albert

Nature Research

2021-08-09

Porcar-Castell, A, Malenovsky, Z, Magney, T, Van Wittenberghe, S, Fernandez-Marin, B, Maignan, F, Zhang, Y, Maseyk, K, Atherton, J, Albert, L P, Robson, T M, Zhao, F, Garcia-Plazaola, J-I, Ensminger, I, Rajewicz, P A, Grebe, S, Tikkanen, M, Kellner, J R, Ihalainen, J A, Rascher, U & Logan, B 2021, 'Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science', Nature plants, vol. 7, no. 8, pp. 998–1009. https://doi.org/10.1038/s41477-021-00980-4

http://hdl.handle.net/10138/339926
10.1038/s41477-021-00980-4

unspecified
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*This is an electronic reprint of the original article.*

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⁹Institute at Brown for Environment and Society, Brown University, Providence, RI 02912, United States of America.

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¹⁰Biology Department, West Virginia University, Morgantown, WV 26506-6300, United States of America.

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¹¹Organismal and Evolutionary Biology, Viikki Plant Science Centre (ViPS), Faculty of Biological and Environmental Science, 00014, University of Helsinki, Finland.

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¹²School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing, 100083, China.

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¹³Department of Plant Biology and Ecology, University of the Basque Country (UPV/EHU), Bilbao, Spain.

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¹⁴Department of Biology, Graduate Programs in Cell & Systems Biology and Ecology & Evolutionary Biology, University of Toronto, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada.

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¹⁵Molecular Plant Biology, University of Turku, FI-20520 Turku, Finland.

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¹⁶Department of Ecology and Evolutionary Biology, Brown University, Providence RI 02912, United States of America.

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¹⁷Nanoscience Center, Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä 40014, Finland.

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¹⁸Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany.

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¹⁹Biology Department, Bowdoin College, Brunswick, Maine, United States of America.

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For decades, the dynamic nature of chlorophyll-a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods now enable detection of ChlaF induced by sunlight across a range of larger scales, using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as *solar-induced fluorescence (SIF)* and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new SIF applications.

When illuminated, chlorophyll-a molecules weakly emit light in the 650-850 nm range; that is, they fluoresce. Steady state¹,² and time-resolved fluorescence spectroscopy³,⁴, as well as pulse-amplitude modulated (PAM) fluorescence⁵,⁶ have long been used by biophysicists, molecular biologists and ecophysiologists to elucidate the structure and function of the photosynthetic apparatus⁷⁻⁹. These techniques are regarded as active because the measured ChlaF originates from a controlled light source, and accordingly have largely¹⁰,¹¹ been restricted to measurements at the subcellular and leaf levels.

Interest in passive remote sensing methods capable of retrieving solar-induced ChlaF across a continuum of spatial scales emerged more than two decades ago¹². These seminal activities led to the first demonstrations of tower-based¹³,¹⁴ and satellite¹⁵ SIF measurements over terrestrial ecosystems. The opportunity to remotely detect an energy flux (Box 1) that arises directly from
within the photosynthetic process spurred the rapid development of measurement techniques, retrieval protocols, and models for estimating and interpreting SIF across scales. As reviewed in Mohammed et al.¹² and Aasen et al.¹⁶, SIF can now be measured from an expanding number of sensors mounted on towers¹⁷,¹⁸, drones¹⁹,²⁰, aircraft²¹,²² and satellites with ever-improving spatial and temporal resolution²³,²⁴. So far, all satellite SIF retrievals have been serendipitous, relying on instruments originally designed to measure atmospheric gases. The first satellite mission designed specifically for the measurement of SIF is the ESA Fluorescence Explorer (FLEX) mission, which is set to launch in 202⁵.

SIF methods are rapidly breaking through the scale bottleneck of traditional ChlaF measurements, opening up a range of new opportunities to study photosynthesis across the continuum of spatial scales from the leaf, through plant canopies, and up to the globe. With SIF we now have the potential to illuminate the path connecting plant molecular biology to Earth-system science. However, before the full potential of multiscale SIF observations can be realized, a number of challenges must be overcome. Extracting the information embedded in the SIF signal requires a fundamental understanding and a quantitative description of the processes that connect measured ChlaF with photosynthesis (Fig.1), as well as their variation across space and time (Fig. 2). In this Perspective, we present these challenges and propose a roadmap of activities to facilitate future research. Finally, we discuss key emerging SIF applications that can benefit from cross-disciplinary expertise.

**Challenge 1: APAR<sub>g</sub>.** The common denominator between ChlaF and the photosynthetic uptake of CO<sub>2</sub> is the flux of photosynthetically active radiation absorbed by photosynthetic pigments, or APAR<sub>g</sub> (where the g stands for green), which provides the foundation for the mechanistic connection between SIF and photosynthesis. APAR<sub>g</sub> is the product of the incoming photosynthetically active radiation (PAR) and the fraction of this PAR absorbed by photosynthetic pigments (fAPAR<sub>g</sub>) (Fig.1). Importantly, although the absorption of radiation by leaves and plant
canopies can be quantified using radiometric sensors either coupled to an integrating sphere²⁶ (e.g. leaf absorptance profile in Fig.1) or mounted above and below a plant canopy²⁷, these measurements also include a significant and dynamic contribution from non-photosynthetic pigments and other canopy elements. While inaccuracies in the estimation of APAR<sub>g</sub> do not disrupt the relationship between SIF and photosynthesis, accurate quantification of the energy flux entering the photosynthetic process is essential for a mechanistic interpretation of SIF and remains a challenge.

**Challenge 2: Distribution of excitation energy between PSII and PSI and their ChlaF emissions.**

APAR<sub>g</sub> is absorbed mostly by chlorophyll-a and chlorophyll-b associated with either photosystem II (PSII) or photosystem I (PSI) reaction centres. Interestingly, while both types of chlorophyll have the capacity to fluoresce, essentially all chlorophyll fluorescence *in vivo* originates from chlorophyll-a due to the efficient transfer of excitation energy from chlorophyll-b to chlorophyll-a within light harvesting antennae<sup>28</sup>. Likewise, although both photosystems emit ChlaF, ChlaF from PSII typically dominates the signal, especially in the red region of the emission spectrum<sup>2</sup> (Fig.1), and exhibits greater variation in quantum yield in response to photochemical and non-photochemical processes<sup>7,29</sup>. The dynamic nature of PSII ChlaF explains the widespread application of PAM fluorescence to probe the energy partitioning between photochemical and non-photochemical processes or to estimate the rate of linear electron transport (LET) in PSII<sup>30</sup>. However, the estimation of LET requires knowledge of the distribution of absorption between the photosystems (i.e. the use of an energy partitioning factor), which is rarely measured and often assumed to be 0.5<sup>6</sup>. Although biochemical and biophysical methods to assess the stoichiometry and antenna sizes of PSI and PSII do exist<sup>31-33</sup>, these methods only provide a relative assessment of the energy distribution; absolute quantification requires the combination of simultaneous ChlaF and 820 nm absorption measurements to probe the energy partitioning in PSII and PSI, respectively, along with photosynthetic gas exchange measurements<sup>34</sup>. Overall, the evidence gathered to date
suggests that neither the distribution of excitation energy between PSII and PSI nor the contribution
of ChlaF from PSI to SIF remain constant over time, between species or within canopy light
gradients³⁵,³⁶. Questions remain: how large is this variability? What controls it? And what is its
significance for the interpretation of SIF? Answers to these questions await the development of
versatile field methods and protocols (e.g. based on rapid optical measurements³⁷) to enable
the characterization of these factors across a wide range of conditions.

**Challenge 3: Energy partitioning in PSII.** Energy absorbed in PSII is partitioned between three main processes: a) photochemical quenching (PQ) of excitation energy, promoting linear electron transport, b) non-photochemical quenching (NPQ), which includes both regulated and sustained forms of thermal dissipation, and c) emission of ChlaF. The quantum yield of a process, e.g. ChlaF emission (ΦF), can be expressed as the ratio of the rate constant associated to that process relative to the sum of all rate constants. Importantly, the rate constants associated to PQ and NPQ are highly dynamic, which allows plants to regulate the flow of energy through PSII and to protect against light-induced damage³⁸, ³⁹. During the growing season, the rate constants of PQ and NPQ vary over time-scales of seconds to minutes in response to the redox dynamics of the quinone acceptor pool and induction and relaxation of regulated thermal dissipation, respectively. Outside of the growing season, or during periods of profound environmental stress, rate constants can be affected by photoinhibition of PQ and the induction of sustained NPQ. Accordingly, changes in the quantum yield of ChlaF (ΦF) reflect the combined effect of PQ and NPQ dynamics and a quantitative connection between ΦF and ΦP (the quantum yield of photochemistry) cannot be established without knowledge of either PQ or NPQ⁸,⁴⁰. PAM fluorescence uses saturating light pulses to solve the energy partitioning and estimate ΦP; an approach that is not feasible during SIF measurements, precluding partitioning from SIF alone.

Under certain conditions, either NPQ or PQ can dominate the relationships between ΦF and ΦP,
resulting in the emergence of a positive or negative relationship respectively. For example, under
low light intensities – when regulatory NPQ remains inactive - the relationship between ΦP and ΦF is negative under the action of PQ, which exerts opposite effects on (i.e. decouples) ΦP and ΦF.

Under high light - when PQ tends to saturate and NPQ is highly active - the relationship between ΦP and ΦF turns to positive under the action of NPQ, which competes for excitation with both (i.e. couples) ΦP and ΦF<sup>12,52,53</sup>. The latter case can explain the seasonal correlation between ΦP and ΦF observed at the leaf<sup>41,42</sup> (Fig. 2) and canopy scales<sup>18</sup>, in response to the modulation of sustained NPQ that protects the foliage from the harmful combination of excessive light and low temperatures<sup>43,44</sup>. Despite the positive relationship between ΦP and ΦF that emerges in response to certain stress conditions, the quantitative treatment of the energy partitioning in PSII requires the use of mechanistic models and remains one of the core challenges to the interpretation of SIF<sup>40,45,46</sup>.

**Challenge 4: Alternative energy sinks.** Photosynthetic linear electron transport provides reducing power for a range of metabolic processes beyond CO₂ assimilation via the Calvin cycle, including chlororespiration<sup>47</sup>, photorespiration<sup>48</sup>, nitrogen, sulphur and oxygen reduction (the latter known as the Mehler reaction in the water-water cycle<sup>49</sup>), and the synthesis of volatile organic compounds<sup>50</sup>. Importantly, the dynamics of these ‘non-assimilatory’ electron sinks can affect ChlF in a manner not directly correlated with CO₂ assimilation. In particular, because alternative energy sinks can have a protective function by sustaining LET under conditions when CO₂ assimilation is impaired<sup>51</sup>, they could influence the capacity of SIF to detect certain plant stress responses. Therefore, it is critical to address the extent that these dynamics decouple SIF from GPP, in particular during plant stress. As with Challenge 2, answering this question will benefit from the development of versatile field methods and protocols to promote the widespread characterization of these factors across a wide range of conditions.

**Challenge 5: Leaf and canopy ChlaF scattering, reabsorption and measurement geometry.** Although the lighter and darker green stripes seen on an athletic field may give the impression of different chlorophyll contents, they are an optical reflection effect created when the grass is bent in
a particular direction during mowing. SIF measurements over plant canopies are similarly affected by the distribution of leaves, canopy architecture and measurement geometry<sup>27,52</sup>. The amount and distribution of chlorophyll within a leaf (influenced by photosystem and thylakoid structure, chloroplast distribution, and internal leaf morphology), as well as the amount and geometrical arrangement of leaves and other non-photosynthetic material within a plant canopy (influenced by branch/stem architecture) drive APAR<sub>g</sub>, connecting SIF and photosynthesis at the leaf and canopy scales, respectively. Once emitted, ChlaF photons travel through the same leaf and canopy structures, where some of the ChlaF photons are reabsorbed (Fig. 1 and Fig. 2 "spectral dynamics"). As a result, spatial and temporal variations in leaf biochemistry, leaf morphology, and canopy architecture, as well as foliage illumination and viewing geometry, influence the probability of ChlaF photons contributing to a SIF measurement (known as the escape probability). These factors decouple the total emitted ChlaF from the measured SIF, and by extension from photosynthesis. Physically-based radiative transfer models, which simulate the movement of photons through leaves and plant canopies (Box 2), can be used to provide a quantitative framework to investigate and account for the impact of these factors on APAR<sub>g</sub> and SIF observations<sup>27,53</sup>. Although spatially explicit RTM approaches are already available (see Supplementary Video 1 and 2), advances in the parametrization of within-leaf and canopy drivers of SIF - e.g. canopy gradients in foliar morphology, pigment contents (Challenge 1) or ChlaF contribution from PSI (Challenge 2) - remain areas of active development.

**Challenge 6: Atmospheric absorption and scattering.** Atmospheric gases, aerosols and other particles absorb and scatter ChlaF photons traveling from a plant canopy to a remote detector. The extent of atmospheric absorption and scattering of SIF depends on the retrieval wavelength, the distance between target and sensor, and the properties of the atmosphere (Box 1). In particular, SIF retrieval methods based on the in-filling of atmospheric gas absorption bands, such as the O2-A or O2-B bands (Fig. 1), face the challenge that the gas absorption feature used for the SIF retrieval
simultaneously attenuates the ChlaF signal as it travels towards the detector. This effect requires a correction even for short-distance measurements from canopy towers and drones<sup>54</sup>. Although an atmospheric RTM can be used to characterize and correct for these effects, its application requires site-specific measurements of atmospheric profile parameters for model input, which remains an operational challenge<sup>55</sup>.

**Challenge 7: Integrating SIF controls across space and time.** A final challenge, and perhaps the most relevant, is the contextualization of the interpretation of SIF (including the previous six challenges) within the spatial and temporal domain of the measurements (Fig. 2). Temporally, ChlaF dynamics have been used to investigate the energy transfer within photosystems (femto picosecond scale)<sup>7,56</sup>, the redox status of the donor and acceptor sides of the photosystem (microsecond-millissecond scale)<sup>3,4</sup>, and the variations in PQ and NPQ (seconds-to-seasonal scale)<sup>38</sup>. <sup>39,44</sup> Spatially, the intensity and spectral properties of SIF are also controlled by factors that regulate both APAR<sub>g</sub> and ChlaF scattering and reabsorption within a leaf or plant canopy<sup>57,58</sup> (Fig. 2, “spectral dynamics”). When ChlaF is measured as SIF across coarser spatial and longer temporal scales, the signal carries information that aggregates an expanding assortment of physical and biological factors<sup>59-61</sup>. New controls may appear while the effects of others may be subordinated, strengthening (via ‘couplers’; Fig. 2) or disrupting (via ‘decouplers’; Fig. 2) the relationship between SIF and GPP.

For example, tower-based SIF studies reveal a strong seasonal linear relationship between canopy SIF and ecosystem GPP across a wide range of ecosystems<sup>17,18,61</sup>, consistent with the coupling action of APAR<sub>g</sub> and NPQ described above. Yet, the sensitivity, strength and linearity of the seasonal SIF-GPP relationship is not universal and has been found to depend on additional physical and physiological decoupling factors, such as sun-vegetation-sensor geometries<sup>62,63</sup>, vegetation canopy structure<sup>52,64</sup>, or photosynthetic pathway (C3 vs. C4)<sup>27,65</sup>, with contrasting responses to different environmental stressors<sup>66,67</sup>. Clearly, integrating and disentangling the relationship
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between SIF and GPP across species, space, time and in response to environmental stress, remains
still a challenge that calls for comprehensive field studies.

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## Roadmap towards a consistent interpretation of SIF

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The time for multiscale SIF measurements is already here (Fig. 3). Yet, converting these data into meaningful information and new applications still requires effort dedicated to scaling and standardizing methods for SIF interpretation, with particular attention to the seven challenges described above. This process requires accounting for the influence of 1) instrumental, 2) atmospheric, 3) structural and 4) physiological factors to unlock the quantitative association between measured SIF and photosynthesis (Fig. 4). Addressing these challenges requires new data, protocols and models to interpret SIF and bridge the gap between molecular processes, i.e. photosynthesis, and satellite imagery.

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At the leaf level, new instruments and techniques employing optical bandpass filters have been developed to record fluorescence spectral dynamics under both natural or controlled illumination, temperature, and CO₂ concentration¹⁶,⁶⁸⁻⁷¹. Such spectral approaches, combined with foliar pigment analysis, photosynthetic gas exchange, and PAM ChlaF measurements, provide new insights into the connection between SIF and photosynthesis dynamics of leaves⁴²,⁶⁹,⁷². Going forward, mechanistically modeling the link between SIF and GPP (Challenges 1-4) will require the combination of field campaigns covering full growing seasons, multiple species and stress responses with detailed experimentation under highly controlled conditions, for example using *Arabidopsis* mutants with altered photochemical properties⁹,⁷³. In particular, the development of versatile field instrumentation and protocols for the estimation of APARg (Challenge 1), energy distribution between PSII and PSI - including the ChlaF contribution from PSI - (Challenge 2), or the quantification of alternative energy sinks (Challenge 4), is key to resolving the spatial and temporal influences of these factors on SIF.

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The synergistic use of complementary data streams can also help to constrain the modelling of photosynthesis and support SIF interpretation. For example, leaf and canopy reflectance data can inform us on the chlorophyll content in the leaf or the amount of leaves in the canopy<sup>74</sup>, relating to APAR<sub>g</sub> (<b>Challenge 1</b>). In addition, reflectance data have been used to explore the regulatory dynamics of NPQ<sup>75</sup>, which could contribute to resolving energy partitioning in PSII (<b>Challenge 3</b>). This approach is feasible due to the spectral change that accompanies the operation of the xanthophyll cycle<sup>76</sup> - by which violaxanthin is converted to antheraxanthin and zeaxanthin in a process that modulates NPQ<sup>38,77</sup> - as well as the seasonal dynamics of leaf carotenoid and chlorophyll contents<sup>78</sup>. These spectral changes, which have been captured by the photochemical reflectance index (PRI)<sup>77,79</sup> or the Chlorophyll/Carotenoid Index (CCI)<sup>80</sup>, are now being revisited and investigated in depth across the whole VIS-NIR region alongside with SIF dynamics<sup>71,76</sup>. Clearly, as in the case of SIF, careful use of canopy and atmospheric RTMs will be needed to disentangle these subtle physiologically-induced reflectance changes from those of a dynamic background<sup>55</sup>. In addition to synergies with spectral reflectance, use of thermal imaging<sup>81</sup>, radar<sup>82</sup>, or multispectral laser scanning methods<sup>83</sup> offer interesting possibilities to constrain the carbon reactions of photosynthesis by providing independent information on plant water status (<b>Challenge 4</b>). Likewise, leaf and ecosystem-level measurements of carbonyl sulfide (COS) uptake by vegetation can provide an independent source of information on stomatal conductance in vascular plants<sup>84</sup>, which could be highly relevant for the development and validation of ecosystem-level SIF-GPP models.

Process-based and radiative transfer models are required to integrate physical and physiological mechanisms operating at different scales (<b>Challenge 7</b>), providing excellent frameworks for multidisciplinary collaborations to connect molecular-level with Earth-system processes. Clearly, as our mechanistic understanding of the connection between SIF and GPP increases (<b>Challenges 1-4</b>), so will the accuracy of process-based models. For example, the integration of the Farquhar-
Caemmerer-Berry⁸⁵ biochemical model of photosynthesis into dynamic land-surface models (e.g., ORCHIDEE⁸⁶ or BETHY⁸⁷) provides a gateway for assimilating satellite SIF data and improving the accuracy of GPP estimations⁸⁸,⁸⁹. In addition, SIF resides at the core of a new generation of photosynthesis models that emphasize the light reactions⁴⁵,⁹⁰. In the case of RTMs with established SIF capabilities (Box 2), further improvements can be achieved by coupling with new techniques measuring detailed 3D structures. Leaf RTMs would benefit from including variations in leaf morphology, thylakoid structure, or the spectral signatures of PSI and PSII. The 3D parameterization of canopy RTMs via lidar-based reconstruction methods⁹¹,⁹², coupled to non-imaging¹⁷,¹⁹ and imaging proximal/airborne SIF measurements (Fig. 3)⁹³,⁹⁴, offers excellent opportunities to integrate and resolve the diversity of factors that control SIF across space and time (Challenge 7). Drone-based measurements could serve to investigate and model the impact of atmospheric properties on SIF retrieval approaches, by hovering at different distances above the target⁵⁴ (Challenge 6). Finally, less accurate but simpler alternative methods for separating the physiological and structural influences on the SIF signal have been recently proposed based on the theory of vegetation canopy near-infrared spectral invariants⁹⁵,⁹⁶. Whether this or other correction methods are applicable to canopy SIF acquisitions across scales, especially observations at very high spatial resolutions (Fig. 3) should be further investigated.

Equally critical for the consistent interpretation of SIF is the establishment of a global network and database of leaf and ecosystem-level SIF measurements covering different biomes, and supporting model development as well as airborne and satellite calibration/validation activities. While regional SIF networks are starting to emerge in North America, Europe, and Asia, their global connectivity should be a priority to promote the adoption of standards for instrument calibrations and long-term monitoring operations (Fig. 4).

Our roadmap for resolving the seven SIF challenges will only succeed through multidisciplinary collaboration involving specialists from across molecular biology, plant physiology, optical physics
and remote sensing. Together, the characterization and modeling of the interplay between structural, optical and functional dynamics of leaves and plant canopies, can turn our crops and forests into observable field laboratories.

## 303 Emerging and potential SIF applications

Satellite SIF data are already providing new insight into photosynthetic dynamics at the global scale⁹⁷,⁹⁸. Likewise, with the advent of multiscale SIF measurements (Fig. 3), and as the remaining challenges are overcome (Fig. 4), a new range of SIF applications unfolds across fields of biochemistry, biophysics, ecology, ecophysiology, biogeochemistry, agriculture and forestry (Fig. 5). Equally important, the continuum of scales at which SIF can be measured provides a focal point to promote and strengthen the interaction between research communities, from plant molecular biology to Earth-system science. Here, we outline four examples of potential and emerging SIF applications.

**Spatial and 3D photosynthesis.** Photosynthetic CO₂ assimilation can be measured using infrared gas analyzers, either coupled to chambers or enclosures at the leaf, shoot, and whole-plant level⁹⁹, or with a sonic anemometer at the ecosystem level using the eddy covariance approach¹⁰⁰. These methods, however, lack detailed spatial information. Spatial measurements of photosynthesis, in terms of photochemical rates of the light reactions, require the use of imaging systems that, to date, have remained restricted to the scale of leaves or small-sized plants, e.g. PAM imaging methods¹⁰¹.
SIF measurements have potential to fill this scale gap. For example, SIF imaging (Fig. 3) could be benchmarked with eddy-covariance methods to reveal the spatial variability of photosynthesis within the footprint of ecosystem eddy covariance measurements, allowing us to investigate the influence of microenvironment, understory and vertical canopy structure, or the interplay between biological and functional diversity within the ecosystem. Likewise, SIF imaging could be applied to
resolve photosynthesis dynamics in 3D, helping to advance our understanding of the interaction between plant structure and function¹⁰²,¹⁰³.

**Physiological phenotyping and pre-visual stress detection.** Spatial and temporal variations in plant morphological traits (e.g., canopy height, leaf area, and plant growth) have been widely used as markers for field phenotypic variability and to investigate long-term plant stress responses. However, these traits are insufficiently responsive to rapid plant physiological changes. This makes them ill-suited for physiological phenotyping (i.e. breeding plant phenotypes displaying specific physiological responses to the environment), or pre-visual stress detection and subsequent optimization of water, pesticide and fertilizer use. The current phenotyping focus has, therefore, shifted towards measurements in the visible and infrared spectral ranges, where reflectance changes can be associated with specific physiological and biochemical traits¹⁰⁴ or used for early-stress detection¹⁰⁵. In this context, emerging SIF imaging systems have already provided promising results for applications in precision agriculture and detection of pest infestations⁹³,¹⁰⁶. In the near future, these methods could also support precision forestry applications related to seedling production or tree-scale forest management.

**Functional plant diversity and spatial ecology.** Functional diversity is a fundamental component of the biodiversity concept¹⁰⁷. As a global network for monitoring biodiversity through remotely sensed plant functional traits is being developed¹⁰⁸, SIF could become one of the new essential variables for mapping functional diversity across ecosystem and landscape scales, given the wide range of biochemical and physiological factors that SIF is sensitive to (Fig. 2) in relation to plant productivity. For example, SIF has been shown to convey spatial information on leaf mass and chlorophyll content¹⁰⁹, and other functional plant traits¹¹⁰ in various forest ecosystems. Additionally and importantly, the combination of high-resolution structural, spectral and SIF data is potentially the only viable option to investigate ecosystem functions that have remained hidden from our observational abilities, such as photosynthetic phenology in evergreen forests¹⁸, cryptogamic
biocrusts¹¹¹ and spatially fragmented Antarctic mosses⁷⁴. Together with spatial photosynthesis, SIF could also offer unique opportunities for studies in spatial ecology¹¹¹,¹¹², where plant environmental responses and biotic interactions could leave their imprint on SIF.

**Carbon and water cycle studies.** The carbon and water cycles of terrestrial ecosystems are intricately connected via stomatal regulation and total leaf area. Because both canopy evapotranspiration and canopy SIF dynamics are strongly controlled by leaf area, and since ChlaF can also decrease with stomatal closure - via increased NPQ in response to water stress¹¹³,¹¹⁴; tower and satellite SIF have been preliminarily used to investigate canopy conductance and plant transpiration¹¹⁵,¹¹⁶. No doubt, better constraints on transpiration and photosynthetic dynamics in land-surface models will be achieved as the mechanistic basis of SIF is elucidated across scales (Challenges 1-7), and the integration of SIF with other remote sensing datasets increases, such as land-surface temperature¹¹⁵, surface soil moisture⁸⁹, radar-measured vegetation optical depth characterizing canopy structure and water content¹¹⁷, or column-averaged atmospheric CO₂⁹². New knowledge of photosynthesis at the ecosystem and regional scales will bring further insight into the large-scale interactions between environmental drivers and plant productivity, and feedbacks between the biosphere and atmosphere.

## Concluding remarks

The SIF signal gathers a wealth of physiological, biochemical, and structural information as it travels from the photosystems to the top of canopy and beyond (Fig. 2). This can leave the impression that SIF is, to use the classic analogy, the ‘Swiss Army Knife’ of photosynthesis measurements. Critically, the variation in SIF caused by physical and biotic factors is entangled in the spatiotemporal domain, and our capacity to disentangle it into useful informative components requires further attention. Historically, photosynthesis research has been a multidisciplinary endeavor, with breakthroughs in the 20th century emerging from collaboration between chemists,
biologists and physicists. We are now entering a new era of multiscale observations of photosynthesis which requires the interdisciplinary research environment to flourish further, this time to resolve the mechanistic connection between SIF and GPP and to scale it across space and time. The technology to measure SIF is developing at a faster pace than our capacity to interpret the acquired data. With the challenges, roadmap and unfolding opportunities introduced here we hope to encourage more scientists to join the multidisciplinary quest to reveal the true potential of SIF observation.

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4. Strasser, R. J., Tsimilli-Michael, M. & Srivastava, A. Analysis of the chlorophyll a fluorescence transient. In: Papageorgiou G.C., Govindjee (eds) Chlorophyll a Fluorescence. Advances in Photosynthesis and Respiration, vol 19. Springer, Dordrecht (2004).

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699 **Corresponding author:** Albert Porcar-Castell (joan.porcar@helsinki.fi)

700
701 **Acknowledgements:** This perspective idea originated during the Fluorescence Across Space and Time (FAST) Workshop, which took place in Hyytiälä Forestry Research Station (SMEARII, Finland) during February 2019. We thank the following participants for active discussions during the workshop: Juliane Bendig, Kukka-Maaria Erkkilä, Noda Hibiki, Laura V. Junker-Frohn, Valentyna Kuznetsova, Hannakaisa Lindqvist, Paul Näthe, Jaakko Oivukkamaki, Neus Sabater, Twinkle Solanki, Tea Thum, Shan Xu and Chao Zhang. We also thank Barry Osmond and Josep Peñuelas for valuable comments to the manuscript, to Nuria Altimir for improving graphic design of Figs 1 and 5, and to Bastian Siegmann for the preparation of the HyPlant image in Fig. 3. The
709 Academy of Finland (Project # 288039 and 319211) is acknowledged for the financial support. ZM was supported by the Australian Research Council (FT160100477), TM was supported by the National Aeronautics and Space Administration (80NSSC19M0129), and SVW was supported by the Generalitat Valenciana and the European Social Fund (APOSTD/2018/162). Headwall SIF images from LPA and JRK were supported by grants from the Institute at Brown for Environment and Society at Brown University.

715 **Author contributions:** APC conceived the original idea and wrote the manuscript with ZM, TM, BL, SVW, BFM, FM, YZ, KM with comments and contributions from all co-authors. In addition, these authors had special contribution to the following parts: Fig.1 (APC, ZM and SVW), Fig.2 (APC, BFM, TM and SVW), Fig. 3 (LPA, UR and JRK), Fig. 4. (APC, ZM, UR, BFM), Fig. 5 (JIGP, JA, ZM, IE), Box 1 (TM, APC), Box 2 (ZM, APC), Supplementary information (ZM, FZ).

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730 **Figure Legends**

731 **Figure 1.** From incoming radiation to observed SIF and photosynthesis: mechanistic challenges. Solar radiation reaching the top of the atmosphere (TOA) is partly absorbed and scattered by atmospheric gases and particles, decreasing its intensity as it reaches the bottom of the atmosphere (BOA), generating specific absorption features. Part of the radiation is absorbed by photosynthetic pigments in vegetation and leaves ($fAPAR_g$) **(Challenge 1)**, associated with either photosystem I (PSI) or photosystem II (PSII), which contribute with differential dynamics and spectral properties to overall SIF emission **(Challenge 2)**. Within each photosystem, energy is further partitioned into three dynamic processes **(Challenge 3)**: i) photochemistry (leading mainly to linear (LET) or cyclic (CET) electron transport, the latter involving PSI only), ii) thermal energy dissipation, and iii) ChlaF. Photosynthetic energy (expressed for simplicity in terms of NADPH equivalents) is further partitioned between alternative energy sinks and gross photosynthesis ($A_G$), and again between gross primary productivity (GPP) and photorespiration ($P_R$), with dynamics that are not necessarily seen by SIF **(Challenge 4)**. Notably, because it is only possible to measure the net flux of CO$_2$ from a leaf or ecosystem, i.e. net photosynthesis or net primary productivity (NPP), the rate of daytime respiration ($R_D$) must be known or estimated. In turn, because emitted ChlaF overlaps with the absorption spectra of leaves and plant canopies, some SIF photons - especially those in the red wavelengths - are re-absorbed within the canopy **(Challenge 5)**. Emitted ChlaF is further scattered and absorbed by aerosols and gases in the atmosphere **(Challenge 6)**.

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750 **Figure 2.** The connection between SIF and GPP across space and time. The relationship between SIF and GPP is affected by multiple factors as we move across spatial and temporal scales. Some factors exert a similar effect on SIF and GPP, keeping them positively correlated - we call these couplers. Other factors differentially affect SIF and GPP - we call these decouplers. Factors
driving the dynamics of NPQ and APAR will tend to keep SIF and GPP coupled both across space and time, whereas factors adding variation to the energy partitioning between ChlF and GPP, or influencing the reabsorption of ChlF, will tend to decouple SIF from GPP (see examples in the figure). Note how the shape of the ChlF spectrum ("Spectral dynamics") changes across scales in response to reabsorption within the chloroplast, leaf and canopy, measurable as SIF only within discrete wavelengths at the canopy and ecosystem levels (Box 1). Equally important to our understanding of the spatial context of the factors that couple/decouple SIF to GPP is understanding their temporal range of action (lower panels). For example, the rapid (second/minute) decrease in ChlF upon saturating illumination of dark acclimated leaves reflects the dynamics of NPQ⁷⁶.

Similar dynamics can be seen under natural conditions at the diurnal/seasonal scale in Scots pine needles, as the quantum yield of fluorescence (ΦF) responds to PQ and NPQ (redrawn from Porcar-Castell³⁹). Here, SIF was estimated for illustrative purposes as SIF (r.u.) = PAR x 0.8 x 0.5 x ΦF, where 0.8 and 0.5 are estimates for fAPARg and the fraction of radiation absorbed by PSII.

Likewise, interannual dynamics at the regional-to-local scales²⁴ can reflect changes in canopy structure, physiological stress responses or other functional traits. Ultimately, the challenge of integrating and disentangling the impact of these couplers/decouplers across space, time, species and plant stress responses remains (Challenge 7).

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**Figure 3.** State-of-the-art SIF imaging methods allow for the observation of SIF across a continuum of scales: from the leaf-to-individual (top row) to the individual-to-landscape (bottom row). Panel A shows an RGB image of a senescing maple tree next to an oak tree with green leaves. Panel B shows the SIF image of the same trees retrieved in the O2A band at 760 nm (SIF760) using a commercial, off-the-shelf imaging spectrometer¹¹⁸ mounted on a tripod some meters away and after applying a filter to exclude non-vegetation pixels (pixels with an normalized difference vegetation index (NDVI) < 0.65). As expected, the green and photosynthetically active oak emitted SIF at
higher magnitude (Panel C) than the senescing maple. Similarly, panels D-E present an airborne RGB and SIF760 map obtained with data from the HyPlant sensor collected at an altitude of 680 m above ground⁹⁴. The scene shows several plots within an experimental apple tree plantation at the agricultural research site Campus Klein-Altendorf (University of Bonn, Germany), where apple tree varieties of different ages were growing in a typical row structure. Single tree crowns were segmented by overlaying the SIF images with a 3D surface map and all pixels that were related to a background signal (defined as ground level + 30 cm) were excluded. The image visualizes the signal of individual trees, where each pixel corresponds to an area of 1x1 meters and thus the small clusters represent the signal of an individual tree.

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**Figure 4.** A roadmap towards the standardized interpretation of SIF. The critical steps, data sources and methods that will be required to overcome the seven challenges are introduced to allow for a consistent interpretation of spectral observations in terms of leaf, canopy and ecosystem traits.

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**Figure 5.** Potential and emerging SIF applications illustrated in the form of a “SIF-city” metro plan, where different colors denote five fields of plant science. Identified research applications (metro stops) are causally connected in individual communication lines, but the final trajectories and number of stops will depend on how the field of SIF research evolves over the next years. The red-colored stops denote the application topics elaborated in Section 3.

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**Box 1 | Principle of solar-induced fluorescence (SIF) retrieval**

SIF measurements take place outdoors, under ambient sunlight. Accordingly, when pointing a spectroradiometer towards a leaf or plant canopy to make a SIF measurement, we face the challenge that vegetation is highly reflective in the near infrared (NIR) wavelengths, and the signal is dominated by reflected light. The retrieval of SIF from the background reflected radiation is made possible thanks to the spectral properties of incoming light.

The solar spectrum, as measured above a plant canopy, is not continuous; rather, radiation is strongly attenuated within so-called Fraunhofer absorption lines and telluric absorption bands originating from absorption by gases in the Sun's photosphere or the Earth's atmosphere, respectively (see Fig. 1 and an idealized spectral feature in **A**). These features are exploited by the Fraunhofer line depth (FLD) methods¹¹⁹ where at least four spectral measurements, usually more¹²⁰, are required: the irradiance of the incoming sunlight and the apparent reflected radiance (called apparent, as it includes also SIF), inside and outside of the spectral absorption feature ($E_{in}/E_{out}$ and $L_{in}/L_{out}$, respectively). Since SIF contributes photons similarly both inside and outside the spectral feature (**B**), the relative contribution of SIF to reflected radiation is significantly greater inside the spectral feature, causing an increase in the apparent reflectance (**C**). This increase is proportional to the amount of SIF and can be used to construct a system of equations to retrieve SIF.

Although not mutually exclusive, SIF measurements are often conducted using either the Fraunhofer or Telluric absorption bands, which involve some trade-offs:

* **Fraunhofer lines** (multiple lines across the SIF spectrum). The advantage of these retrievals lies in their lower sensitivity to atmospheric properties, which is practical for remote measurements as well as applications with variable target-to-sensor distances (e.g., multiangular tower measurements). The main disadvantage is that they require spectrometers with extremely high spectral resolutions and generally require longer periods of signal integration.

* **Telluric bands** (mainly oxygen absorption bands B and A, centered around 687-692 nm; and 759-770 nm, respectively). Since these bands are broader, measurements do not require as high spectral resolution and can be also conducted with shorter integration time, which can be especially suitable for some applications (e.g. drone-based observations). Their main disadvantage is that attention must be paid to corrections for atmospheric absorption (Challenge 6).

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

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Box 2 | Radiative transfer models (RTMs)

*   **Forward mode.** When the required inputs are provided, RTMs are capable of simulating leaf and canopy SIF together with reflected and emitted optical and thermal radiance. Once successfully validated by independent measurements, RTMs can be used in the forward mode to investigate the sensitivity of outputs, (i.e., surface reflectance and SIF) to different structural, biochemical, and physiological inputs, extending our mechanistic understanding of reflected and emitted photons' propagation across scales.

*   **Inverse mode.** RTMs can be also inverted (i.e., run backwards) to estimate from laboratory, field and remote sensing spectral data those leaf and canopy traits that match measured reflectance and SIF data.

MODEL INPUTS:
physical, biochemical
and functional variables

*   Solar illumination and sensor viewing geometry

*   Atmospheric optical conditions

*   Leaf physiological, biochemical and morphological traits (e.g., quantum efficiencies, spectral signatures of PSI and PSII fluorescence)

*   Canopy architecture (e.g., derived from 3D laser scanning)

*   Landcover classes and landscape geomorphology

FORWARD MODE: simulation and sensitivity analyses

INVERSE MODE: interpretation of spectral observations

<figure><img src="image_8.png" /><figcaption>*   Fluspect <sup>121</sup></figcaption></figure>

1D RTMs

<figure><img src="image_9.png" /><figcaption>*   FluLCVRT <sup>53</sup></figcaption></figure>

3D RTMs

<figure><img src="image_10.png" /><figcaption>*   SCOPE <sup>122</sup></figcaption></figure>

Canopy

<figure><img src="image_11.png" /><figcaption>*   DART <sup>123</sup></figcaption></figure>

MODEL OUTPUTS:
non-imaging and imaging
spectral data

*   Laboratory, field, proximal and air-/space-borne optical and thermal measurements

*   Quantification of atmospheric optical absorption and scattering

*   Quantification of leaf and canopy light absorption and scattering interactions

*   Landscape radiative budget [i.e., fluxes in W m⁻²] in optical and thermal spectral domains

*   **1D models.** 1D leaf RTMs assume that leaf constituents are horizontally homogeneously distributed in vertically stacked plate structures, and hence require only basic morphological and biochemical inputs (e.g., pigment contents driving PAR absorption and within-leaf reabsorption, the intrinsic PSII and PSI fluorescence spectra, and the dynamics in the quantum yield of fluorescence as the mechanistic link to photosynthesis). This simplicity, however, ignores potentially important factors, such as within-leaf heterogeneity or chloroplast movements. As with the 1D leaf construct, 1D canopy RTMs assume that vegetation can be represented by horizontally homogeneous layers filled with leaves of a predefined size, density and geometry (angular distribution), which allows for minimal model inputs and a relatively straightforward application. The 1D architecture has its uses for spatially homogeneous canopies (e.g., crops).

*   **3D models.** Structurally complex leaves and spatially heterogeneous plant communities (e.g., forests and savannas) require 3D representations. 3D leaf RTMs can model optical interactions within a genuine 3D digital representation of leaf interior reconstructed, for example, with imaging tomography or confocal microscopy. As demonstrated in the Supplementary Videos 1 and 2, 3D RTM solutions also exist for spatially diversified plant canopies, allowing for accurate physical simulations of APAR<sub>g</sub> and SIF fluxes in complex canopies.
EXCITATION

Solar spectral irradiance
[W m⁻² nm⁻¹]

Leaf absorptance

<figure><img src="image_12.png" /></figure>

Wavelength [nm]

PHOTOSYNTHETIC ENERGY PARTITIONING

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

Fluorescence (SIF) radiance [mW m⁻² sr⁻¹ nm⁻¹]

EMISSION
Factors disconnecting
SIF and GPP
(DECOUPLERS)

Factors connecting
SIF and GPP
(COUPLERS)

SIF interpretation
and application

<figure><img src="image_15.png" /></figure>

Second/minute dynamics

<figure><img src="image_16.png" /></figure>

Diurnal/seasonal dynamics (leaf)

<figure><img src="image_17.png" /></figure>

Interannual dynamics

<figure><img src="image_18.png" /></figure>

TROPOMI SIF July 2018
<figure><img src="image_19.png" /><figcaption>a</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>b</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>c</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>d</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>e</figcaption></figure>
CRITICAL STEPS/FACTORS

INSTRUMENTAL

<figure><img src="image_24.png" /></figure>

* Standardized measurement and calibration protocols

ATMOSPHERIC

<figure><img src="image_25.png" /></figure>

* Atm. corrections of reflectance and SIF observations (including towers and drones)

STRUCTURAL

<figure><img src="image_26.png" /></figure>

* Improved parameterization of leaf and canopy RTMs with morphological and functional data
* Standardization of canopy SIF in terms of efficiency

PHYSIOLOGICAL

<figure><img src="image_27.png" /></figure>

* Synergistic use of complementary/ancillary data
* Full characterization of dynamics in photosynthetic energy partitioning between SIF and GPP
* Comprehensive campaigns: long-term, multiple species, stressors and biomes

Global SIF community:
Cal/Val multiscale sensor network and database

DATA SOURCES

* Instrument characterization and calibration data

* Atm. databases (e.g. Aeronet)
* Atm. in-situ measurements
* Irradiance vertical profiles (drone-based)

* 3D canopy structural data (e.g. terrestrial and airborne LiDAR)
* 3D leaf morphology
* Leaf and canopy empirical data for model calibration/validation (including PSI and PSII ChlaF spectra)

* Leaf and plant empirical data for model calibration/validation:
  - pigment content and dynamics
  - PSI and PSII ChlaF contributions
  - energy partitioning between SIF and GPP
* Data from a wide range of organisms (functional diversity)

METHODS

* Instrument calibrations
* SIF retrieval methods

* Atmospheric radiative transfer models (RTMs)

* Canopy & leaf RTMs
* Spectral invariant probability-based reflectance corrections

* Process-based model development
* Empirical models
* Versatile protocols to resolve PSI contribution and full energy partitioning
* Multisource data assimilation frameworks

SPECTRAL
OBSERVATIONS

<figure><img src="image_28.png" /></figure>

AT-SENSOR SIF/
REFLECTANCE

<figure><img src="image_29.png" /></figure>

ATMOSPHERE
CORRECTED
SIF/REFLECTANCE

<figure><img src="image_30.png" /></figure>

STRUCTURE
CORRECTED
SIF/REFLECTANCE

<figure><img src="image_31.png" /></figure>

ECOSYSTEM,
CANOPY AND
LEAF
PHOTOSYNTHETIC
TRAITS

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>
Biochemistry
& Biophysics

Molecular regulation
of photosynthesis

<figure><img src="image_34.png" /></figure>

Investigating
drivers
of photosynthesis

<figure><img src="image_35.png" /></figure>

3D photosynthesis:
Plant structure &
function

<figure><img src="image_36.png" /></figure>

Detecting plant
functional traits

Phenotyping
& stress
detection

<figure><img src="image_37.png" /></figure>

Breeding &
testing of
new varieties

<figure><img src="image_38.png" /></figure>

Land cover/use
change detection

<figure><img src="image_39.png" /></figure>

Multiscale carbon
& water cycles

<figure><img src="image_40.png" /></figure>

Biosphere-atmosphere
feedbacks

<figure><img src="image_41.png" /></figure>

Impacts of climate
change & extreme
events

Agriculture
& Forestry

<figure><img src="image_42.png" /></figure>

Resource-use
optimized
management

Pre-visual
pest &
disease
detection

<figure><img src="image_43.png" /></figure>

Precision
agriculture
& forestry

Plant

Ecophysiology

<figure><img src="image_44.png" /></figure>

Experimental
modeling

<figure><img src="image_45.png" /></figure>

Functional
dynamics in
extreme &
remote biomes

Phenology &
circadian controls

<figure><img src="image_46.png" /></figure>

Functional
& spatial
diversity

Ecology

Biogeochemistry