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Hyperspectral Proximal Sensing for Estimating Photosynthetic Capacities at Leaf and Canopy Scales

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Photosynthesis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2790))

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Abstract

Agronomists, plant breeders, and plant biologists have been promoting the need to develop high-throughput methods to measure plant traits of interest for decades. Measuring these plant traits or phenotypes is often a bottleneck since skilled personnel, resources, and ample time are required. Additionally, plant phenotypic traits from only a select number of breeding lines or varieties can be quantified because the “gold standard” measurement of a desired trait cannot be completed in a timely manner. As such, numerous approaches have been developed and implemented to better understand the biology and production of crops and ecosystems. In this chapter, we explain one of the recent approaches leveraging hyperspectral measurements to estimate different aspects of photosynthesis. Notably, we outline the use of hyperspectral radiometer and imaging to rapidly estimate two of the rate-limiting steps of photosynthesis: the maximum rate of the carboxylation of Rubisco (Vcmax) and the maximum rate of electron transfer or regeneration of RuBP (Jmax).

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References

  1. Hibberd JM, Sheehy JE, Langdale JA (2008) Using C4 photosynthesis to increase the yield of rice – rationale and feasibility. Curr Opin Plant Biol 11(2):228–231

    Article  CAS  PubMed  Google Scholar 

  2. Long SP, Marshall-Colon A, Zhu X-G (2015) Meeting the global food demand of the future by engineering crop photosynthesis and yield potential. Cell 161(1):56–66

    Article  CAS  PubMed  Google Scholar 

  3. Monteith JL (1965) Light distribution and photosynthesis in field crops. Ann Bot 29(1):17–37

    Article  Google Scholar 

  4. Ort DR et al (2015) Redesigning photosynthesis to sustainably meet global food and bioenergy demand. Proc Natl Acad Sci 112(28):8529–8536

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wu A et al (2019) Quantifying impacts of enhancing photosynthesis on crop yield. Nat Plants 5(4):380–388

    Article  PubMed  Google Scholar 

  6. Fu P et al (2022) Advances in field-based high-throughput photosynthetic phenotyping. J Exp Bot 73(10):3157–3172

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Farquhar GD, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149(1):78–90

    Article  CAS  PubMed  Google Scholar 

  8. Bernacchi CJ et al (2001) Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant Cell Environ 24(2):253–259

    Article  CAS  Google Scholar 

  9. Stinziano JR et al (2019) The rapid A/Ci response: a guide to best practices. New Phytol 221(2):625–627

    Article  PubMed  Google Scholar 

  10. Fu P et al (2019) Hyperspectral leaf reflectance as proxy for photosynthetic capacities: an ensemble approach based on multiple machine learning algorithms. Front Plant Sci 10:730

    Article  PubMed  PubMed Central  Google Scholar 

  11. Meacham-Hensold K et al (2019) High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity. Remote Sens Environ 231:111176

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst 58(2):109–130

    Article  CAS  Google Scholar 

  13. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576

    Article  CAS  PubMed  Google Scholar 

  14. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  15. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8(2):127–150

    Article  Google Scholar 

  16. Clevers JGPW, Kooistra L (2012) Using hyperspectral remote sensing data for retrieving canopy chlorophyll and nitrogen content. IEEE J Sel Top Appl Earth Obs Remote Sens 5(2):574–583

    Article  Google Scholar 

  17. Gitelson A, Merzlyak MN (1994) Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J Plant Physiol 143(3):286–292

    Article  CAS  Google Scholar 

  18. Dash J, Curran PJ (2004) The MERIS terrestrial chlorophyll index. Int J Remote Sens 25(23):5403–5413

    Article  Google Scholar 

  19. Jacquemoud S et al (2009) PROSPECT+SAIL models: a review of use for vegetation characterization. Remote Sens Environ 113:S56–S66

    Article  Google Scholar 

  20. Fu P et al (2020) Estimating photosynthetic traits from reflectance spectra: a synthesis of spectral indices, numerical inversion, and partial least square regression. Plant Cell Environ 43(5):1241–1258

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Burnett AC et al (2021) A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression. J Exp Bot 72(18):6175–6189

    Article  CAS  PubMed  Google Scholar 

  22. Grzybowski M et al (2021) Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: progress and challenges. Plant Commun 2(4):100209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Siebers MH et al (2021) Emerging approaches to measure photosynthesis from the leaf to the ecosystem. Emerg Top Life Sci 5(2):261–274

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Heckmann D, Schlüter U, Weber APM (2017) Machine learning techniques for predicting crop photosynthetic capacity from leaf reflectance spectra. Mol Plant 10(6):878–890

    Article  CAS  PubMed  Google Scholar 

  25. Busch FA (2024) Photosynthetic gas exchange in land plants at the leaf level. In: Covshoff S (ed) Photosynthesis: methods and protocols, 2nd edn. Springer, New York

    Google Scholar 

  26. Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129–137

    Article  Google Scholar 

  27. Song G, Wang Q, Jin J (2023) Fractional-order derivative spectral transformations improved partial least squares regression estimation of photosynthetic capacity from hyperspectral reflectance. IEEE Trans Geosci Remote Sens 61:1–10

    Google Scholar 

  28. Serbin SP et al (2019) From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance. New Phytol 224(4):1557–1568

    Article  PubMed  Google Scholar 

  29. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technometrics 11(1):137–148

    Article  Google Scholar 

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Fu, P., Montes, C., Meacham-Hensold, K. (2024). Hyperspectral Proximal Sensing for Estimating Photosynthetic Capacities at Leaf and Canopy Scales. In: Covshoff, S. (eds) Photosynthesis . Methods in Molecular Biology, vol 2790. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3790-6_18

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  • DOI: https://doi.org/10.1007/978-1-0716-3790-6_18

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3789-0

  • Online ISBN: 978-1-0716-3790-6

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