International Journal of Biometeorology

, Volume 58, Issue 8, pp 1803–1809

On the nature of canopy illumination due to differences in elemental orientation and aggregation for radiative transfer

Short Note

Abstract

The nature of canopy radiative transfer mechanism (CRTM) describes the amount of beam penetration through a canopy and governs the nature of canopy illumination, i.e. the abundance of sunlit and shaded portions. Realistic representation of canopy illumination is critical for simulating various canopy biophysical processes associated with vegetated land surfaces. The adequate representation of CRTM can be attributed to the parameterizations of the two main canopy characteristics: the foliage projection (G-function) and the clumping effect (Ω function). Herein, using various types of G and Ω functions developed in a previous study, I tested 15 CRTM scenarios that combine different types of G and Ω functions to predict the dynamics of sunlit fraction (ε) of canopies having a wide range of plant area index (Ptotal) at various solar zenith angles (SZAs). It was observed that, for a given Ptotal, ε decreases as the SZA increases. However, ε significantly changed in accordance with the type of G and Ω functions used. Scenarios that employed random distribution of elements in space (S-4, S-9, and S-14) consistently returned larger ε values even at lower SZAs. This means that ignoring the clumping behavior of canopies could result in greater proportion of sunlit elements thereby reducing the beam penetration deeper into the canopy as opposed to those canopies where the elements are more aggregated. Beyond 70° SZA, almost all the scenarios returned similar ε values for a given Ptotal, which implied that the methods used is less sensitive at higher SZAs. The values of ε calculated by all the scenarios were significantly different from the S-6 (the ideal case). This observation highlights the importance of explicitly describing the G and Ω functions to adequately depict canopy illumination conditions.

References

  1. Anisimov O, Fukshansky L (1997) Optics of vegetation: implications for the radiation balance and photosynthetic performance. Agric For Meteorol 85(1–2):33–49CrossRefGoogle Scholar
  2. Alton PB, Ellis R, Los SO, North PR (2007) Improved global simulations of gross primary product based on a separate and explicit treatment of diffuse and direct sunlight. J Geophys Res 112, D07203. doi:10.1029/2006JD008022 Google Scholar
  3. Baldocchi DD, Hutchison BA, Matt DR, McMillen RT (1985) Canopy radiative-transfer models for spherical and known leaf inclination angle distributions: a test in an oak hickory forest. J Appl Ecol 22(2):539–555CrossRefGoogle Scholar
  4. Berbigier P, Bonnefond J-M (1995) Measurements and modeling of radiation transmission within a stand of maritime pine (Pinus pinaster Aït.). Ann des Sci forestières 52:23–42CrossRefGoogle Scholar
  5. Bonan GB (1989) A computer-model of the solar-radiation, soil-moisture, and soil thermal regimes in boreal forests. Ecol Model 45:275–306CrossRefGoogle Scholar
  6. Campbell GS (1990) Derivation of an angle density-function for canopies with ellipsoidal leaf angle distributions. Agric For Meteorol 49(3):173–176CrossRefGoogle Scholar
  7. Chen JM (1996) Optically-based methods for measuring seasonal variation in leaf area index of boreal conifer forests. Agric For Meteorol 80:135–163CrossRefGoogle Scholar
  8. Chen JM, Mo G, Pisek J, Liu J, Deng F, Ishizawa M, Chan D (2012) Effects of foliage clumping on the estimation of global terrestrial gross primary productivity, Global Biogeochem. Cycles 26, GB1019. doi:10.1029/2010GB003996 CrossRefGoogle Scholar
  9. Chen JM, Liu J, Cihlar J, Guolden ML (1999) Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol Model 124:99–119CrossRefGoogle Scholar
  10. Chen JM, Rich PM, Gower TS, Norman JM, Plummer S (1997) Leaf area index of boreal forests: theory, techniques, and measurements. J Geophys Res 102:29,429–29,444CrossRefGoogle Scholar
  11. Chen JM, Black TA (1991) Measuring leaf-area index of plant canopies with branch architecture. Agric For Meteorol 57(1–3):1–12CrossRefGoogle Scholar
  12. Chen JM, Menges CH, Leblanc SG (2005) Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing Environ 97(4):447–457CrossRefGoogle Scholar
  13. De Pury DGG, Farquhar GD (1997) Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant, Cell Environ 20:537–557. doi:10.1111/j.1365-3040.1997.00094.x CrossRefGoogle Scholar
  14. Dickinson RE, Sellers AH, Kennedy PJ, Wilson MF (1986) ‘Biosphere-atmosphere transfer scheme (BATS) for the NCAR community climate model’, InNCAR Tech. Note TN-275, National Center for Atmosphere Research, Boulder, Colorado, USA.Google Scholar
  15. Fassnacht KS, Gower ST, Norman JM, Mcmirtrie RE (1994) A comparison of optical and direct methods in estimating foliage surface area in forests. Agric For Meteorol 71:183–207CrossRefGoogle Scholar
  16. Goel NS, Strebel DE (1984) Simple beta distribution representation of leaf orientation in vegetation canopies. Agrono J 76(5):800–802CrossRefGoogle Scholar
  17. Govind A, Guyon D, Roujean J-L, Yauschew-Raguenes N, Kumari J, Pisek P, Wigneron J-P (2013) Effects of canopy architectural parameterizations on the modeling of radiative transfer mechanism. Ecol Model 251(2013):114–126CrossRefGoogle Scholar
  18. Govind A, Chen JM, McDonnell J, Kumari J, Sonnentag O (2010) Effect of lateral hydrological processes on photosynthesis and evapotranspiration. Ecohydrology.3. (doi: 10.1002/eco.141).
  19. Govind A, Chen JM, Margolis H, Ju W, Sonnentag O, Giasson MA (2009a) A spatially explicit hydro-ecological modeling framework (BEPS-TerrainLab V2.0): model description and test in a boreal ecosystem in Eastern North America. J Hydrol 367:200–216. doi:10.1016/j.jhydrol.2009.01.006 CrossRefGoogle Scholar
  20. Govind A, Chen JM, Ju W (2009b) Spatially explicit simulation of hydrologically controlled carbon and nitrogen cycles and associated feedback mechanisms in a boreal ecosystem. Journal of Geophysical Research 114, G02006. doi:10.1029/2008JG000728 CrossRefGoogle Scholar
  21. Govind A, Chen JM, Bernier PY, Margolis H, Guindon L, Beaudoin A (2011) Spatially distributed modeling of the long-term carbon balance of a boreal landscape. Ecological Modeling 222(15):2780–2795CrossRefGoogle Scholar
  22. Kucharik CJ, Norman JM, Gower ST (1999) Characterization of radiation regimes in nonrandom forest canopies: theory, measurements, and a simplified modeling approach. Tree Physiology 19(11):695–706CrossRefGoogle Scholar
  23. Lang ARG, Xiang Y (1986) Estimation of leaf area index from transmission of direct sunlight in discontinuous canopies. Agric For Meteorol 37(3):229–243Google Scholar
  24. Lemeur R, Blad BL (1974) A critical review of light models for estimating the shortwave radiation regime of plant canopies. Agric Meteorol 14(255):286Google Scholar
  25. Monsi M, Saeki T (1953) Über den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung für die Stoffproduktion. Japanese Journal of Botany 14:22–52Google Scholar
  26. Nilson T, Ross J (1997) Modeling radiative transfer through forest canopies: implications for canopy photosynthesis and remote sensing. The use of remote sensing in the modeling of forest productivity, pp. 23–60Google Scholar
  27. Nilson T (1971) Theoretical analysis of frequency of gaps in plant stands. Agric Meteorol 8(1):25–38CrossRefGoogle Scholar
  28. Norman JM (1982) Simulation of microclimates. In: Hatfield JL, Thomason IJ (eds) Biometeorology in Integrated Pest Management. Academic, San Diego, Calif, pp 65–99CrossRefGoogle Scholar
  29. Pisek J, Chen JM, Nilson T (2011) Estimation of vegetation clumping index using MODIS BRDF data. International Journal of Remote Sensing 32(9):2645–2657CrossRefGoogle Scholar
  30. Ross J (1981) The radiation regime and architecture of plant stands. Dr. W. Junk, Norwell, Mass., USA, p. 391Google Scholar
  31. Stenberg P, Linder S, Smolander H, Flower-Ellis J (1994) Performance of the LAI- (2000) Plant canopy analyzer in estimating leaf area index of some Scotspine stands. Tree Physiology 14:981–995Google Scholar
  32. Sterck FJ, Schieving F (2007) 3-D growth patterns of trees: effects of carbon economy, meristem activity, and selection. Ecol Monogr 77:405–420. doi:10.1890/06-1670.1 CrossRefGoogle Scholar
  33. Walter J-MN, Fournier RA, Soudani K, Meyer E (2003) Integrating clumping effects in forest canopy structure: an assessment through hemispherical photographs. Canadian Journal of Remote Sensing 29:388–410CrossRefGoogle Scholar
  34. Wang Y-P, Leuning R (1998) A two-leaf model for canopy conductance, photosynthesis, and partitioning of available energy I: model description and comparison with a multi-layered model. Agric For Meteorol 91:89–111. doi:10.1016/S0168-1923(98)00061-6 CrossRefGoogle Scholar
  35. Zhang YQ, Chen JM, Miller JR (2005) Determining digital hemispherical photograph exposure for leaf area index estimation. Agric For Meteorol 133(1–4):166–181CrossRefGoogle Scholar

Copyright information

© ISB 2013

Authors and Affiliations

  1. 1.INRAUR 1263 EPHYSE, F-33140 Villenave d’Ornon, Centre INRA BordeauxAquitaineFrance

Personalised recommendations