Photochemical reflectance ratio for tracking light use efficiency for sunlit leaves in two forest types

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 123, Issue , 2017, Pages 47-61

  Zheng, Ting ^a   Chen, Jing M ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

BRDF; Light use efficiency; Photochemical reflectance index; Photochemical reflectance ratio; Sunlit shaded separation

Indexed keywords

CARBOXYLATION; EFFICIENCY; FORESTRY; PLANTS (BOTANY); REMOTE SENSING;

BRDF; GROSS PRIMARY PRODUCTIVITY; LIGHT USE EFFICIENCY; NORMALIZED DIFFERENCE INDICES; NORMALIZED DIFFERENCE VEGETATION INDEX; PHOTOCHEMICAL REFLECTANCE; PHOTOCHEMICAL REFLECTANCE INDEX; REMOTE SENSING APPROACHES;

REFLECTION;

CANOPY REFLECTANCE; ERROR CORRECTION; FOREST CANOPY; LIGHT USE EFFICIENCY; NDVI; NET PRIMARY PRODUCTION; NUMERICAL METHOD; PHOTOCHEMISTRY; REMOTE SENSING;

ACER SACCHARUM; ANTIDESMA PLATYPHYLLUM;

EID: 84998865510     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2016.11.005     Document Type: Article

References (63)

1. Arain, M.A., Restrepo-Coupe, N., Net ecosystem production in a temperate pine plantation in southeastern Canada. Agric. For. Meteorol. 128:3–4 (2005), 223–241.
2. Barton, C.V.M., North, P.R.J., Remote sensing of canopy light use efficiency using the photochemical reflectance index: model and sensitivity analysis. Remote Sens. Environ. 78:3 (2001), 264–273.
3. Bonan, G.B., et al. Improving canopy processes in the Community Land Model version 4 (CLM4) using global flux fields empirically inferred from FLUXNET data. J. Geophys. Res. 116:G2 (2011), 1–22.
4. Chen, J.M., 1996. Canopy architecture and remote sensing of the fraction of photosynthetically active radiation absorbed by boreal conifer forests.
5. Chen, J.M., et al. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124:2–3 (1999), 99–119.
6. Chen, J.M., et al. Effects of foliage clumping on the estimation of global terrestrial gross primary productivity. Global Biogeochem. Cycles 26:1 (2012), 1–18.
7. Chen, J.M., et al. Leaf area index of boreal forests: theory, techniques, and measurements. J. Geophys. Res., 102(D24), 1997, 29429.
8. Chen, J.M., Deng, F., Chen, M., Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter. IEEE Trans. Geosci. Remote Sens. 44:8 (2006), 2230–2237.
9. Chen, J.M., Leblanc, S.G., A four-scale bidirectional reflectance model based on canopy architecture. IEEE Trans. Geosci. Remote Sens. 35:5 (1997), 1316–1337.
10. Cramer, W., Field, C.B., Comparing global models of terrestrial net primary productivity (NPP): introduction. Global Change Biol. 5:S1 (1999), iii–iv.
11. Croft, H., Chen, J.M., Zhang, Y., The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures. Ecol. Complex. 17 (2014), 119–130.
12. Erbs, D.G., Klein, S.A., Duffie, J.A., Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation. Solar Energy 28:4 (1982), 293–302.
13. Falge, E., et al. Gap filling strategies for long term energy flux data sets. Agric. For. Meteorol. 107:1 (2001), 71–77.
14. 2 assimilation in leaves of C3 species. Planta 149:1 (1980), 78–90.
15. Filewod, B., Thomas, S.C., Impacts of a spring heat wave on canopy processes in a northern hardwood forest. Glob. Change Biol. 20:2 (2014), 360–371.
16. Gamon, A., Serrano, L., Surfus, S., The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112:4 (1997), 492–501.
17. Gamon, J.A., et al. Remote sensing of the xanthophyll cycle and chlorophyll fluorescence in sunflower leaves and canopies. Oecologia 85:1 (1990), 1–7.
18. Gamon, J.A., Penuelas, J., Field, C.B., A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 41:1 (1992), 35–44.
19. Garbulsky, M.F., et al. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis. Remote Sens. Environ. 115:2 (2011), 281–297.
20. Geddes, J.A., et al. Net ecosystem exchange of an uneven-aged managed forest in central Ontario, and the impact of a spring heat wave event. Agric. For. Meteorol. 198–199 (2014), 105–115.
21. Groenendijk, M., Dolman, A.J., et al. Assessing parameter variability in a photosynthesis model within and between plant functional types using global Fluxnet eddy covariance data. Agric. For. Meteorol. 151:1 (2011), 22–38.
22. Groenendijk, M., Dolman, A.J., et al. Seasonal variation of photosynthetic model parameters and leaf area index from global Fluxnet eddy covariance data. J. Geophys. Res., 116(G4), 2011, G04027.
23. Hall, F.G., et al. Multi-angle remote sensing of forest light use efficiency by observing PRI variation with canopy shadow fraction. Remote Sens. Environ. 112:7 (2008), 3201–3211.
24. Hall, F.G., Hilker, T., Coops, N.C., PHOTOSYNSAT, photosynthesis from space: Theoretical foundations of a satellite concept and validation from tower and spaceborne data. Remote Sens. Environ. 115:8 (2011), 1918–1925.
25. Hilker, T., et al. Instrumentation and approach for unattended year round tower based measurements of spectral reflectance. Comput. Electron. Agric. 56:1 (2007), 72–84.
26. Hilker, T., et al. Remote sensing of photosynthetic light-use efficiency across two forested biomes: spatial scaling. Remote Sens. Environ. 114:12 (2010), 2863–2874.
27. Hilker, T., et al. Separating physiologically and directionally induced changes in PRI using BRDF models. Remote Sens. Environ. 112:6 (2008), 2777–2788.
28. Hmimina, G., et al. Deconvolution of pigment and physiologically related photochemical reflectance index variability at the canopy scale over an entire growing season. Plant, Cell Environ. 38:8 (2015), 1578–1590.
29. Hmimina, G., Dufrêne, E., Soudani, K., Relationship between photochemical reflectance index and leaf ecophysiological and biochemical parameters under two different water statuses: towards a rapid and efficient correction method using real-time measurements. Plant, Cell Environ. 37:2 (2014), 473–487.
30. Jin, P., et al. Retrieval of seasonal variation in photosynthetic capacity from multi-source vegetation indices. Ecol. Inf. 7:1 (2012), 7–18.
31. Jordan, C.F., Derivation of leaf area index from quality of light on the floor. Ecology 50 (1969), 663–666.
32. Kattge, J., et al. Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models. Glob. Change Biol. 15:4 (2009), 976–991.
33. Lasslop, G., et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Change Biol. 16:1 (2010), 187–208.
34. Lewis, J., McKane, R., Vertical gradients in photosynthetic light response within an old-growth Douglas-fir and western hemlock canopy. Tree Ellipsis, 2000, 447–456.
35. Li, X., Strahler, A.H., Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: effect of crown shape and mutual shadowing. IEEE Trans. Geosci. Remote Sens. 30:2 (1992), 276–292.
36. Los, S.O., et al. A method to convert AVHRR normalized difference vegetation index time series to a standard viewing and illumination geometry. Remote Sens. Environ. 99:4 (2005), 400–411.
37. Maccioni, A., Agati, G., Mazzinghi, P., New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra. J. Photochem. Photobiol., B 61:1–2 (2001), 52–61.
38. Monteith, J.L., Solar radiation and productivity in tropical ecosystems. J. Appl. Ecol. 9:3 (1972), 747–766.
39. Monteith, J.L., Moss, C.J., Climate and the efficiency of crop production in Britain [and discussion]. Philos. Trans. R. Soc. B: Biol. Sci. 281:980 (1977), 277–294.
40. Mõttus, M., et al. Diffuse sky radiation influences the relationship between canopy PRI and shadow fraction. ISPRS J. Photogram. Remote Sens. 105 (2015), 54–60.
41. Nakaji, T., et al. Utility of spectral vegetation indices for estimation of light conversion efficiency in coniferous forests in Japan. Agric. For. Meteorol. 148:5 (2008), 776–787.
42. Nakaji, T., Oguma, H., Fujinuma, Y., Seasonal changes in the relationship between photochemical reflectance index and photosynthetic light use efficiency of Japanese larch needles. Int. J. Remote Sens. 27:3 (2006), 493–509.
43. Nichol, C.J., et al. Remote sensing of photosynthetic-light-use efficiency of a Siberian boreal forest. Tellus B 54:5 (2002), 677–687.
44. Nichol, C.J., et al. Remote sensing of photosynthetic-light-use efficiency of boreal forest. Agric. For. Meteorol. 101:2–3 (2000), 131–142.
45. Norman, J.M., Simulation of microclimates. Biometeorology in Integrated Pest Management, 1982, Academic Press, 65–99.
46. Peichl, M., Arain, M.A., Above- and belowground ecosystem biomass and carbon pools in an age-sequence of temperate pine plantation forests. Agric. For. Meteorol. 140:1–4 (2006), 51–63.
47. Peñuelas, J., Filella, I., Gamon, J.A., Assessment of photosynthetic radiation-use efficiency with spectral reflectance. New Phytol. 131:3 (1995), 291–296.
48. Peñuelas, J.J., et al. Photochemical re¯ ectance index and leaf photosynthetic radiation-use-e ciency assessment in Mediterranean trees. Int. J. Remote Sens. 18:13 (1997), 2863–2868.
49. Potter, C., Continental-scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982–1998. Global Planet. Change 39:3–4 (2003), 201–213.
50. Ripullone, F., et al. Effectiveness of the photochemical reflectance index to track photosynthetic activity over a range of forest tree species and plant water statuses. Funct. Plant Biol. 38:3 (2011), 177–186.
51. Running, S.W., et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 2004, 547.
52. Soudani, K., et al. Relationships between photochemical reflectance index and light-use efficiency in deciduous and evergreen broadleaf forests. Remote Sens. Environ. 144 (2014), 73–84.
53. Spitters, C.J.T., Toussaint, H.A.J.M., Goudriaan, J., Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis Part I. Components of incoming radiation. Agric. For. Meteorol. 38:1–3 (1986), 217–229.
54. Stagakis, S., et al. Tracking seasonal changes of leaf and canopy light use efficiency in a Phlomis fruticosa Mediterranean ecosystem using field measurements and multi-angular satellite hyperspectral imagery. ISPRS J. Photogram. Remote Sens. 97 (2014), 138–151.
55. Thornley, J.H.M., Instantaneous canopy photosynthesis: analytical expressions for sun and shade leaves based on exponential light decay down the canopy and an acclimated non-rectangular hyperbola for leaf photosynthesis. Ann. Bot. 89:4 (2002), 451–458.
56. Verrelst, J., et al. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – a review. ISPRS J. Photogram. Remote Sens. 108 (2015), 273–290.
57. Wang, Q., Iio, A., Kakubari, Y., Broadband simple ratio closely traced seasonal trajectory of canopy photosynthetic capacity. Geophys. Res. Lett. 35:7 (2008), n/a–n/a.
58. Wanner, W., Li, X., Strahler, A.H., On the derivation of kernels for kernel-driven models of bidirectional reflectance. J. Geophys. Res., 100(D10), 1995, 21077.
59. Weng, J.-H., et al. Relationships between photosystem II efficiency and photochemical reflectance index under different levels of illumination: comparison among species grown at high- and low elevations through different seasons. Trees 26:2 (2011), 343–351.
60. Wong, C.Y.S., Gamon, J.A., Three causes of variation in the photochemical reflectance index (PRI) in evergreen conifers. New Phytol. 206:1 (2015), 187–195.
61. Wu, C., et al. Improved estimation of light use efficiency by removal of canopy structural effect from the photochemical reflectance index (PRI). Agric. Ecosyst. Environ. 199 (2015), 333–338.
62. Zhang, C., et al. Affecting factors and recent improvements of the photochemical reflectance index (PRI) for remotely sensing foliar, canopy and ecosystemic radiation-use efficiencies. Remote Sens., 8(9), 2016, 677.
63. Zhou, Y., et al. Close relationship between spectral vegetation indices and V cmax in deciduous and mixed forests. Tellus B 1 (2014), 1–16.