Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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


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 enable detection of ChlaF induced by sunlight across a range of larger scales, from 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 applications of SIF.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: From incoming radiation to observed SIF and photosynthesis: mechanistic challenges.
Fig. 2: The relationship between SIF and GPP across space and time.
Fig. 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).
Fig. 4: A roadmap towards the standardized interpretation of SIF.
Fig. 5: Potential and emerging SIF applications illustrated in the form of a ‘SIF city’ metro plan.


  1. 1.

    Genty, B., Wonders, J. & Baker, N. R. Non-photochemical quenching of Fo in leaves is emission wavelength dependent: consequences for quenching analysis and its interpretation. Photosynth. Res. 26, 133–139 (1990).

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Franck, F., Juneau, P. & Popovic, R. Resolution of the photosystem I and photosystem II contributions to chlorophyll fluorescence of intact leaves at room temperature. Biochim. Biophys. Acta 1556, 239–246 (2002).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Neubauer, C. & Schreiber, U. The polyphasic rise of chlorophyll fluorescence upon onset of strong continuous illumination: I. Saturation characteristics and partial control by the photosystem II acceptor side. Z. f.ür. Naturforsch. C. 42, 1246–1254 (1987).

    CAS  Article  Google Scholar 

  4. 4.

    Strasser, R. J., Tsimilli-Michael, M. & Srivastava, A. in Chlorophyll a Fluorescence. Advances in Photosynthesis and Respiration Vol. 19 (eds Papageorgiou G. C. & Govindjee) 321–362 (Springer, 2004).

  5. 5.

    Schreiber, U., Schliwa, U. & Bilger, W. Continuous recording of photochemical and non-photochemical chlorophyll fluorescence quenching with a new type of modulation fluorometer. Photosynth. Res. 10, 51–62 (1986).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Maxwell, K. & Johnson, G. N. Chlorophyll fluorescence—a practical guide. J. Exp. Bot. 51, 659–668 (2000).

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Govindjee, E. 63 years since Kautsky-chlorophyll-a fluorescence. Aust. J. Plant Physiol. 22, 131–160 (1995).

    CAS  Google Scholar 

  8. 8.

    Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065–4095 (2014).

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Tikkanen, M., Rantala, S., Grieco, M. & Aro, E. Comparative analysis of mutant plants impaired in the main regulatory mechanisms of photosynthetic light reactions–from biophysical measurements to molecular mechanisms. Plant Physiol. Biochem. 112, 290–301 (2017).

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Kolber, Z. et al. Measuring photosynthetic parameters at a distance: laser induced fluorescence transient (LIFT) method for remote measurements of photosynthesis in terrestrial vegetation. Photosynth. Res. 84, 121–129 (2005).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Keller, B. et al. Genotype specific photosynthesis × environment interactions captured by automated fluorescence canopy scans over two fluctuating growing seasons. Front. Plant Sci. 10, 1482 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Mohammed, G. H. et al. Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress. Remote Sens. Environ. 231, 111177 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Evain, S., Camenen, L. & Moya, I. Three-channel detector for remote sensing of chlorophyll fluorescence and reflectance from vegetation. In: 8th International Symposium: Physical Measurements and Signatures in Remote Sensing (ed. Leroy, M.) 395–400 (CNES, 2001).

  14. 14.

    Louis, J. et al. Remote sensing of sunlight-induced chlorophyll fluorescence and reflectance of Scots pine in the boreal forest during spring recovery. Remote Sens. Environ. 96, 37–48 (2005).

    Article  Google Scholar 

  15. 15.

    Guanter, L. et al. Estimation of solar-induced vegetation fluorescence from space measurements. Geophys. Res. Lett. 34, L08401 (2007).

    Article  CAS  Google Scholar 

  16. 16.

    Aasen, H. et al. Sun-induced chlorophyll fluorescence II: review of passive measurement setups, protocols, and their application at the leaf to canopy level. Remote Sens. 11, 927 (2019).

    Article  Google Scholar 

  17. 17.

    Yang, X. et al. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).

    CAS  Article  Google Scholar 

  18. 18.

    Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl Acad. Sci. USA 116, 11640–11645 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Bendig, J., Malenovský, Z., Gautam, D. & Lucieer, A. Solar-induced chlorophyll fluorescence measured from an unmanned aircraft system: sensor etaloning and platform motion correction. IEEE Trans. Geosci. Remote Sens. 58, 3437–3444 (2019).

    Article  Google Scholar 

  20. 20.

    Vargas, J. Q. et al. Unmanned aerial systems (UAS)-based methods for solar induced chlorophyll fluorescence (SIF) retrieval with non-imaging spectrometers: state of the art. Remote Sens. 12, 1624 (2020).

    Article  Google Scholar 

  21. 21.

    Rascher, U. et al. Sun-induced fluorescence—a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant. Glob. Change Biol. 21, 4673–4684 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Frankenberg, C. et al. The chlorophyll fluorescence imaging spectrometer (CFIS), mapping far red fluorescence from aircraft. Remote Sens. Environ. 217, 523–536 (2018).

    Article  Google Scholar 

  23. 23.

    Frankenberg, C. et al. New global observations of the terrestrial carbon cycle from GOSAT: patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 38, 17706 (2011).

    Article  CAS  Google Scholar 

  24. 24.

    Köhler, P. et al. Global retrievals of solar-induced chlorophyll fluorescence at red wavelengths with TROPOMI. Geophys. Res. Lett. 47, e2020GL087541 (2020).

    Article  CAS  Google Scholar 

  25. 25.

    Drusch, M. et al. The fluorescence explorer mission concept—ESA’s Earth Explorer 8. IEEE Trans. Geosci. Remote Sens. 55, 1273–1284 (2016).

    Article  Google Scholar 

  26. 26.

    Olascoaga, B., Mac Arthur, A., Atherton, J. & Porcar-Castell, A. A comparison of methods to estimate photosynthetic light absorption in leaves with contrasting morphology. Tree Physiol. 36, 368–379 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Zhang, Z. et al. Assessing bi-directional effects on the diurnal cycle of measured solar-induced chlorophyll fluorescence in crop canopies. Agric. Meteorol. 295, 108147 (2020).

    Article  Google Scholar 

  28. 28.

    Bittner, T., Irrgang, K., Renger, G. & Wasielewski, M. R. Ultrafast excitation energy transfer and exciton-exciton annihilation processes in isolated light harvesting complexes of photosystem II (LHC II) from spinach. J. Phys. Chem. 98, 11821–11826 (1994).

    CAS  Article  Google Scholar 

  29. 29.

    Kalaji, H. M. et al. Frequently asked questions about chlorophyll fluorescence, the sequel. Photosynth. Res. 132, 13–66 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  30. 30.

    Genty, B., Briantais, J. & Baker, N. R. The relationship between the quantum yield of photosynthetic electron transport and quenching of chlorophyll fluorescence. Biochim. Biophys. Acta 990, 87–92 (1989).

    CAS  Article  Google Scholar 

  31. 31.

    Anderson, J. M., Chow, W. S. & Goodchild, D. J. Thylakoid membrane organisation in sun/shade acclimation. Funct. Plant Biol. 15, 11–26 (1988).

    Article  Google Scholar 

  32. 32.

    Ballottari, M., Dall’Osto, L., Morosinotto, T. & Bassi, R. Contrasting behavior of higher plant photosystem I and II antenna systems during acclimation. J. Biol. Chem. 282, 8947–8958 (2007).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Schreiber, U., Klughammer, C. & Kolbowski, J. Assessment of wavelength-dependent parameters of photosynthetic electron transport with a new type of multi-color PAM chlorophyll fluorometer. Photosynth. Res. 113, 127–144 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Laisk, A. et al. A computer-operated routine of gas exchange and optical measurements to diagnose photosynthetic apparatus in leaves. Plant Cell Environ. 25, 923–943 (2002).

    CAS  Article  Google Scholar 

  35. 35.

    Pfündel, E. Estimating the contribution of photosystem I to total leaf chlorophyll fluorescence. Photosynthesis Res. 56, 185–195 (1998).

    Article  Google Scholar 

  36. 36.

    Peterson, R. B. et al. Fluorescence Fo of photosystems II and I in developing C3 and C4 leaves, and implications on regulation of excitation balance. Photosynth. Res. 122, 41–56 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  37. 37.

    Pfündel, E. E. Simultaneously measuring pulse-amplitude-modulated (PAM) chlorophyll fluorescence of leaves at wavelengths shorter and longer than 700 nm. Photosynth. Res. 147, 345–358 (2021).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  38. 38.

    Demmig-Adams, B. & Adams, W. W. III Photoprotection in an ecological context: the remarkable complexity of thermal energy dissipation. N. Phytol. 172, 11–21 (2006).

    CAS  Article  Google Scholar 

  39. 39.

    Porcar-Castell, A. A high-resolution portrait of the annual dynamics of photochemical and non-photochemical quenching in needles of Pinus sylvestris. Physiol. Plant. 143, 139–153 (2011).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  40. 40.

    Van der Tol, C., Berry, J. A., Campbell, P. & Rascher, U. Models of fluorescence and photosynthesis for interpreting measurements of solar-induced chlorophyll fluorescence. J. Geophys. Res. 119, 2312–2327 (2014).

    Article  Google Scholar 

  41. 41.

    Springer, K. R., Wang, R. & Gamon, J. A. Parallel seasonal patterns of photosynthesis, fluorescence, and reflectance indices in boreal trees. Remote Sens. 9, 691 (2017).

    Article  Google Scholar 

  42. 42.

    Zhang, C. et al. Do all chlorophyll fluorescence emission wavelengths capture the spring recovery of photosynthesis in boreal evergreen foliage? Plant Cell Environ. 42, 3264–3279 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  43. 43.

    Ensminger, I. et al. Intermittent low temperatures constrain spring recovery of photosynthesis in boreal Scots pine forests. Glob. Change Biol. 10, 995–1008 (2004).

    Article  Google Scholar 

  44. 44.

    Verhoeven, A. Sustained energy dissipation in winter evergreens. New Phytol. 201, 57–65 (2014).

    Article  Google Scholar 

  45. 45.

    Gu, L., Han, J., Wood, J. D., Chang, C. Y. & Sun, Y. Sun-induced Chl fluorescence and its importance for biophysical modeling of photosynthesis based on light reactions. New Phytol. 223, 1179–1191 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  46. 46.

    Raczka, B. et al. Sustained nonphotochemical quenching shapes the seasonal pattern of solar-induced fluorescence at a high-elevation evergreen forest. J. Geophys. Res. 124, 2005–2020 (2019).

    Article  Google Scholar 

  47. 47.

    Nixon, P. J. Chlororespiration. Philos. Trans. R. Soc. Lond. B 355, 1541–1547 (2000).

    CAS  Article  Google Scholar 

  48. 48.

    Ogren, W. L. Photorespiration: pathways, regulation, and modification. Annu. Rev. Plant Physiol. 35, 415–442 (1984).

    CAS  Article  Google Scholar 

  49. 49.

    Asada, K. The water-water cycle in chloroplasts: scavenging of active oxygens and dissipation of excess photons. Annu. Rev. Plant Biol. 50, 601–639 (1999).

    CAS  Article  Google Scholar 

  50. 50.

    Morfopoulos, C. et al. A model of plant isoprene emission based on available reducing power captures responses to atmospheric CO2. New Phytol. 203, 125–139 (2014).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Maseyk, K., Lin, T., Cochavi, A., Schwartz, A. & Yakir, D. Quantification of leaf-scale light energy allocation and photoprotection processes in a Mediterranean pine forest under extensive seasonal drought. Tree Physiol. 39, 1767–1782 (2019).

    CAS  PubMed  Article  Google Scholar 

  52. 52.

    Migliavacca, M. et al. Plant functional traits and canopy structure control the relationship between photosynthetic CO2 uptake and far-red sun-induced fluorescence in a Mediterranean grassland under different nutrient availability. New Phytol. 214, 1078–1091 (2017).

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Kallel, A. FluLCVRT: Reflectance and fluorescence of leaf and canopy modeling based on Monte Carlo vector radiative transfer simulation. J. Quant. Spectrosc. Radiat. Transf. 253, 107183 (2020).

    CAS  Article  Google Scholar 

  54. 54.

    Sabater, N. et al. Compensation of oxygen transmittance effects for proximal sensing retrieval of canopy–leaving sun–induced chlorophyll fluorescence. Remote Sens. 10, 1551 (2018).

    Article  Google Scholar 

  55. 55.

    Sabater, N., Kolmonen, P., Van Wittenberghe, S., Arola, A. & Moreno, J. Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and PRI region dynamics from space. Remote Sens. Environ. 254, 112226 (2021).

    Article  Google Scholar 

  56. 56.

    Iermak, I., Vink, J., Bader, A. N., Wientjes, E. & van Amerongen, H. Visualizing heterogeneity of photosynthetic properties of plant leaves with two-photon fluorescence lifetime imaging microscopy. Biochim. Biophys. Acta 1857, 1473–1478 (2016).

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Romero, J. M., Cordon, G. B. & Lagorio, M. G. Modeling re-absorption of fluorescence from the leaf to the canopy level. Remote Sens. Environ. 204, 138–146 (2018).

    Article  Google Scholar 

  58. 58.

    Magney, T. S. et al. Disentangling changes in the spectral shape of chlorophyll fluorescence: Implications for remote sensing of photosynthesis. J. Geophys. Res. 124, 1491–1507 (2019).

    Article  Google Scholar 

  59. 59.

    Murchie, E. H. et al. Measuring the dynamic photosynthome. Ann. Bot. 122, 207–220 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Magney, T. S., Barnes, M. L. & Yang, X. On the covariation of chlorophyll fluorescence and photosynthesis across scales. Geophys. Res. Lett. 47, e2020GL091098 (2020).

    Article  Google Scholar 

  61. 61.

    Yang, P., van der Tol, C., Campbell, P. K. & Middleton, E. M. Unraveling the physical and physiological basis for the solar-induced chlorophyll fluorescence and photosynthesis relationship using continuous leaf and canopy measurements of a corn crop. Biogeosciences 18, 441–465 (2021).

    CAS  Article  Google Scholar 

  62. 62.

    Liu, X. et al. Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model. Remote Sens. Environ. 231, 110772 (2019).

    Article  Google Scholar 

  63. 63.

    Joiner, J. et al. Systematic orbital geometry-dependent variations in satellite solar-induced fluorescence (SIF) retrievals. Remote Sens. 12, 2346 (2020).

    Article  Google Scholar 

  64. 64.

    Dechant, B. et al. Canopy structure explains the relationship between photosynthesis and sun-induced chlorophyll fluorescence in crops. Remote Sens. Environ. 241, 111733 (2020).

    Article  Google Scholar 

  65. 65.

    He, L. et al. From the ground to space: using solar-induced chlorophyll fluorescence to estimate crop productivity. Geophys. Res. Lett. 47, e2020GL087474 (2020).

    Google Scholar 

  66. 66.

    Ač, A. et al. Meta-analysis assessing potential of steady-state chlorophyll fluorescence for remote sensing detection of plant water, temperature and nitrogen stress. Remote Sens. Environ. 168, 420–436 (2015).

    Article  Google Scholar 

  67. 67.

    Wohlfahrt, G. et al. Sun-induced fluorescence and gross primary productivity during a heat wave. Sci. Rep. 8, 14169 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Van Wittenberghe, S., Alonso, L., Verrelst, J., Moreno, J. & Samson, R. Bidirectional sun-induced chlorophyll fluorescence emission is influenced by leaf structure and light scattering properties: A bottom-up approach. Remote Sens. Environ. 158, 169–179 (2015).

    Article  Google Scholar 

  69. 69.

    Magney, T. S. et al. Connecting active to passive fluorescence with photosynthesis: A method for evaluating remote sensing measurements of Chl fluorescence. New Phytol. 215, 1594–1608 (2017).

    CAS  PubMed  Article  Google Scholar 

  70. 70.

    Rajewicz, P. A., Atherton, J., Alonso, L. & Porcar-Castell, A. Leaf-level spectral fluorescence measurements: comparing methodologies for broadleaves and needles. Remote Sens. 11, 532 (2019).

    Article  Google Scholar 

  71. 71.

    Van Wittenberghe, S., Alonso, L., Malenovský, Z. & Moreno, J. In vivo photoprotection mechanisms observed from leaf spectral absorbance changes showing VIS–NIR slow-induced conformational pigment bed changes. Photosynth. Res. 142, 283–305 (2019).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  72. 72.

    Meeker, E. W., Magney, T. S., Bambach, N., Momayyezi, M. & McElrone, A. J. Modification of a gas exchange system to measure active and passive chlorophyll fluorescence simultaneously under field conditions. AoB Plants 13, plaa066 (2021).

    PubMed  Article  Google Scholar 

  73. 73.

    Acebron, K. et al. Diurnal dynamics of nonphotochemical quenching in Arabidopsis npq mutants assessed by solar-induced fluorescence and reflectance measurements in the field. New Phytol. 229, 2104–2119 (2020).

    PubMed  Article  CAS  Google Scholar 

  74. 74.

    Malenovský, Z., Lucieer, A., King, D. H., Turnbull, J. D. & Robinson, S. A. Unmanned aircraft system advances health mapping of fragile polar vegetation. Methods Ecol. Evol. 8, 1842–1857 (2017).

    Article  Google Scholar 

  75. 75.

    Atherton, J., Nichol, C. J. & Porcar-Castell, A. Using spectral chlorophyll fluorescence and the photochemical reflectance index to predict physiological dynamics. Remote Sens. Environ. 176, 17–30 (2016).

    Article  Google Scholar 

  76. 76.

    Van Wittenberghe, S. et al. Combined dynamics of the 500–600 nm leaf absorption and chlorophyll fluorescence changes in vivo: evidence for the multifunctional energy quenching role of xanthophylls. Biochim. Biophys. Acta 1862, 148351 (2021).

    Article  CAS  Google Scholar 

  77. 77.

    Gamon, J. A. et al. Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 85, 1–7 (1990).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  78. 78.

    Filella, I. et al. PRI assessment of long-term changes in carotenoids/chlorophyll ratio and short-term changes in de-epoxidation state of the xanthophyll cycle. Int. J. Remote Sens. 30, 4443–4455 (2009).

    Article  Google Scholar 

  79. 79.

    Peñuelas, J., Filella, I. & Gamon, J. A. Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol. 131, 291–296 (1995).

    Article  Google Scholar 

  80. 80.

    Gamon, J. A. et al. A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers. Proc. Natl Acad. Sci. USA 113, 13087–13092 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Costa, J. M., Grant, O. M. & Chaves, M. M. Thermography to explore plant-environment interactions. J. Exp. Bot. 64, 3937–3949 (2013).

    CAS  PubMed  Article  Google Scholar 

  82. 82.

    Konings, A. G., Rao, K. & Steele-Dunne, S. C. Macro to micro: microwave remote sensing of plant water content for physiology and ecology. New Phytol. 223, 1166–1172 (2019).

    PubMed  Article  Google Scholar 

  83. 83.

    Junttila, S. et al. Terrestrial laser scanning intensity captures diurnal variation in leaf water potential. Remote Sens. Environ. 255, 112274 (2021).

    Article  Google Scholar 

  84. 84.

    Whelan, M. E. Two scientific communities striving for a common cause: innovations in carbon cycle science. Bull. Am. Meteorol. Soc. 101, E1537–1543 (2020).

    Article  Google Scholar 

  85. 85.

    Farquhar, G. D., von Caemmerer, S. V. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).

    CAS  PubMed  Article  Google Scholar 

  86. 86.

    Bacour, C. et al. Improving estimates of gross primary productivity by assimilating solar-induced fluorescence satellite retrievals in a terrestrial biosphere model using a process-based SIF model. J. Geophys. Res. 124, 3281–3306 (2019).

    Article  Google Scholar 

  87. 87.

    Norton, A. J. et al. Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model. Biogeosciences 16, 3069–3093 (2019).

    CAS  Article  Google Scholar 

  88. 88.

    Thum, T. et al. Modelling sun-induced fluorescence and photosynthesis with a land surface model at local and regional scales in northern Europe. Biogeosciences 14, 1969–1987 (2017).

    CAS  Article  Google Scholar 

  89. 89.

    Qiu, B., Chen, J. M., Ju, W., Zhang, Q. & Zhang, Y. Simulating emission and scattering of solar-induced chlorophyll fluorescence at far-red band in global vegetation with different canopy structures. Remote Sens. Environ. 233, 111373 (2019).

    Article  Google Scholar 

  90. 90.

    Johnson, J. E. & Berry, J. A. The role of Cytochrome b6f in the control of steady-state photosynthesis: a conceptual and quantitative model. Photosynth. Res. (2021).

  91. 91.

    Janoutová, R. et al. Influence of 3D spruce tree representation on accuracy of airborne and satellite forest reflectance simulated in DART. Forests 10, 292 (2019).

    Article  Google Scholar 

  92. 92.

    Liu, W. et al. Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements. Remote Sens. Environ. 232, 111274 (2019).

    Article  Google Scholar 

  93. 93.

    Pinto, F. et al. Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to quantify spatio-temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ. 39, 1500–1512 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  94. 94.

    Siegmann, B. et al. The high-performance airborne imaging spectrometer HyPlant—From raw images to top-of-canopy reflectance and fluorescence products: Introduction of an automatized processing chain. Remote Sens. 11, 2760 (2019).

    Article  Google Scholar 

  95. 95.

    Yang, P., van der Tol, C., Campbell, P. K. & Middleton, E. M. Fluorescence Correction Vegetation Index (FCVI): A physically based reflectance index to separate physiological and non-physiological information in far-red sun-induced chlorophyll fluorescence. Remote Sens. Environ. 240, 111676 (2020).

    Article  Google Scholar 

  96. 96.

    Zeng, Y. et al. A radiative transfer model for solar induced fluorescence using spectral invariants theory. Remote Sens. Environ. 240, 111678 (2020).

    Article  Google Scholar 

  97. 97.

    Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Wang, S. et al. Urban–rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons. Nat. Ecol. Evol. 3, 1076–1085 (2019).

    PubMed  Article  Google Scholar 

  99. 99.

    Long, S. P., Farage, P. K. & Garcia, R. L. Measurement of leaf and canopy photosynthetic CO2 exchange in the field. J. Exp. Bot. 47, 1629–1642 (1996).

    CAS  Article  Google Scholar 

  100. 100.

    Baldocchi, D. D. Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future. Glob. Change Biol. 9, 479–492 (2003).

    Article  Google Scholar 

  101. 101.

    Kaiser, Y. I., Menegat, A. & Gerhards, R. Chlorophyll fluorescence imaging: a new method for rapid detection of herbicide resistance in Alopecurus myosuroides. Weed Res. 53, 399–406 (2013).

    CAS  Article  Google Scholar 

  102. 102.

    Sievänen, R., Godin, C., DeJong, T. M. & Nikinmaa, E. Functional–structural plant models: a growing paradigm for plant studies. Ann. Bot. 114, 599–603 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  103. 103.

    Damm, A., Paul-Limoges, E., Kükenbrink, D., Bachofen, C. & Morsdorf, F. Remote sensing of forest gas exchange: considerations derived from a tomographic perspective. Glob. Change Biol. 26, 2717–2727 (2020).

    Article  Google Scholar 

  104. 104.

    Ensminger, I. Fast track diagnostics: Hyperspectral reflectance differentiates disease from drought stress in trees. Tree Physiol. 40, 1143–1146 (2020).

    PubMed  Article  PubMed Central  Google Scholar 

  105. 105.

    Mutka, A. M. & Bart, R. S. Image-based phenotyping of plant disease symptoms. Front. Plant Sci. 5, 734 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  106. 106.

    Zarco-Tejada, P. J. et al. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 4, 432–439 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  107. 107.

    Dı́az, S. & Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).

    Article  Google Scholar 

  108. 108.

    Skidmore, A. K. et al. Environmental science: Agree on biodiversity metrics to track from space. Nature 523, 403–405 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  109. 109.

    Tagliabue, G. et al. Sun–induced fluorescence heterogeneity as a measure of functional diversity. Remote Sens. Environ. 247, 111934 (2020).

    Article  Google Scholar 

  110. 110.

    Pacheco-Labrador, J. et al. Multiple-constraint inversion of SCOPE. Evaluating the potential of GPP and SIF for the retrieval of plant functional traits. Remote Sens. Environ. 234, 111362 (2019).

    Article  Google Scholar 

  111. 111.

    Smith, W. K. et al. Remote sensing of dryland ecosystem structure and function: progress, challenges, and opportunities. Remote Sens. Environ. 233, 111401 (2019).

    Article  Google Scholar 

  112. 112.

    Kellner, J. R., Albert, L. P., Burley, J. T. & Cushman, K. C. The case for remote sensing of individual plants. Am. J. Bot. 106, 1139–1142 (2019).

    PubMed  Article  Google Scholar 

  113. 113.

    Flexas, J. et al. Steady-state chlorophyll fluorescence (Fs) measurements as a tool to follow variations of net CO2 assimilation and stomatal conductance during water-stress in C3 plants. Physiol. Plant. 114, 231–240 (2002).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  114. 114.

    Marrs, J. K. et al. Solar-induced fluorescence does not track photosynthetic carbon assimilation following induced stomatal closure. Geophys. Res. Lett. 47, e2020GL087956 (2020).

    CAS  Article  Google Scholar 

  115. 115.

    Maes, W. H. et al. Sun-induced fluorescence closely linked to ecosystem transpiration as evidenced by satellite data and radiative transfer models. Remote Sens. Environ. 249, 112030 (2020).

    Article  Google Scholar 

  116. 116.

    Shan, N. et al. A model for estimating transpiration from remotely sensed solar-induced chlorophyll fluorescence. Remote Sens. Environ. 252, 112134 (2021).

    Article  Google Scholar 

  117. 117.

    Wang, X. et al. Globally consistent patterns of asynchrony in vegetation phenology derived from optical, microwave, and fluorescence satellite data. J. Geophys. Res. Biogeosci. 125, e2020JG005732 (2020).

    Google Scholar 

  118. 118.

    Liu, J. et al. Contrasting carbon cycle responses of the tropical continents to the 2015-2016 El Niño. Science 358, eaam5690 (2017).

    PubMed  Article  CAS  Google Scholar 

  119. 119.

    Albert, L. P. et al. Stray light characterization in a high-resolution imaging spectrometer designed for solar-induced fluorescence. In Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV (eds Velez-Reyes, M. & Messinger, D. W.) 109860G (SPIE, 2019).

  120. 120.

    Meroni, M. et al. Remote sensing of solar-induced chlorophyll fluorescence: Review of methods and applications. Remote Sens. Environ. 113, 2037–2051 (2009).

    Article  Google Scholar 

  121. 121.

    Cendrero-Mateo, M. P. et al. Sun-induced chlorophyll fluorescence III: Benchmarking retrieval methods and sensor characteristics for proximal sensing. Remote Sens. 11, 962 (2019).

    Article  Google Scholar 

  122. 122.

    Vilfan, N. et al. Extending Fluspect to simulate xanthophyll driven leaf reflectance dynamics. Remote Sens. Environ. 211, 345–356 (2018).

    Article  Google Scholar 

  123. 123.

    Yang, P., Prikaziuk, E., Verhoef, W. & van der Tol, C. SCOPE 2.0: A model to simulate vegetated land surface fluxes and satellite signals. Geosci. Model Dev. Discuss. (2020).

  124. 124.

    Gastellu-Etchegorry, J. et al. DART: recent advances in remote sensing data modeling with atmosphere, polarization, and chlorophyll fluorescence. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 2640–2649 (2017).

    Article  Google Scholar 

Download references


The idea for this Perspective was conceived during the Fluorescence Across Space and Time workshop, which took place at the Hyytiälä Forestry Research Station (SMEARII, Finland) during February 2019. We thank the following participants for active discussions during the workshop: J. Bendig, K.-M. Erkkilä, N. Hibiki, L. V. Junker-Frohn, V. Kuznetsova, H. Lindqvist, P. Näthe, J. Oivukkamaki, N. Sabater, T. Solanki, T. Thum, S. Xu and C. Zhang. We also thank B. Osmond and J. Peñuelas for valuable comments on the manuscript; N. Altimir for improving graphic design of Figs. 1 and 5; and B. Siegmann for the preparation of the HyPlant image in Fig. 3. We acknowledge the Academy of Finland (project numbers 288039 and 319211) for financial support. Z.M. was supported by the Australian Research Council (FT160100477), T.M. was supported by the National Aeronautics and Space Administration (80NSSC19M0129) and S.V.W. was supported by the Generalitat Valenciana and the European Social Fund (APOSTD/2018/162). Headwall SIF images from L.P.A. and J.R.K. were supported by grants from the Institute at Brown for Environment and Society at Brown University.

Author information




A.P.-C. conceived the original idea and wrote the manuscript with Z.M., T.M., B.L., S.V.W., B.F.-M., F.M., Y.Z. and K.M., with comments and contributions from all co-authors. Further contributions: Fig. 1 (A.P.-C., Z.M. and S.V.W.), Fig. 2 (A.P.-C., B.F.-M., T.M. and S.V.W.), Fig. 3 (L.P.A., U.R. and J.R.K.), Fig. 4 (A.P.-C., Z.M., U.R. and B.F.-M.), Fig. 5 (J.-I.G.-P., J.A., Z.M. and I.E.), Box 1 (T.M. and A.P.-C.), Box 2 (Z.M. and A.P.-C.) and supplementary information (Z.M. and F.Z.).

Corresponding author

Correspondence to Albert Porcar-Castell.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Jeannine Cavender-Bares, David Schimel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Video 1

Three-dimensional discrete anisotropic radiative transfer (DART) modelling of chlorophyll a SIF emissions in a structurally complex white peppermint (Eucalyptus pulchella) forest stand. Virtual 3D representations of the eucalyptus, endemic to the Tasmanian island, were constructed from terrestrial LiDAR measurements of trees located southeast of Hobart, Australia. A highly clumped eucalyptus foliage is strongly affecting scattering and absorption of SIF photons. The video illustrates the impact of this specific canopy architecture on SIF signal emitted in a 3D vertical profile of the forest stand and on the top-of-canopy SIF in a diurnal course modelled with DART at 740 nm between 07:00 and 18:00 (local time).

Supplementary Video 2

Three-dimensional SIF emissions of a maize (Zea mays L.) crop modelled using the fluorescence model with weighted photon spread (FluorWPS). The virtually grown maize plants were simulated in a computer crop-growth model. The video demonstrates SIF emitted in a 3D vertical profile of maize canopy during the first 30 days of the crop development and a potential impact of sensor–observation geometry on remotely sensed SIF signal, modelled at 740 nm for various viewing zenith and azimuth angles with FluorWPS.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Porcar-Castell, A., Malenovský, Z., Magney, T. et al. Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science. Nat. Plants 7, 998–1009 (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing