An intercomparison of radiation partitioning models in vineyard canopies

  • C. K. Parry
  • H. Nieto
  • P. Guillevic
  • N. Agam
  • W. P. Kustas
  • J. Alfieri
  • L. McKee
  • A. J. McElroneEmail author


Multiple radiation transfer models with unique clumping indices (a total of five approaches) were evaluated on two Pinot Noir vineyards in Central California over 3 years. In the first approach, a basic clumping index meant for heterogeneous randomly placed clumped canopies was combined with the Campbell and Norman transfer model (C&N–H). The other four approaches, namely, the Campbell and Norman with rectangular hedgerow clumping index (C&N–R), Campbell and Norman with a geometric elliptical hedgerow model (C&N–E), the 4-stream scattering by arbitrary inclined leaves model (4SAIL) with row-crop clumping index, and the discrete anisotropic radiative transfer (DART) models, account for the unique canopy coverage distribution of the vineyard row-structured canopies. Each modeling approach varied in its complexity to predict transmitted solar radiation at ground level and the outputs were compared to solar radiation observed at the surface with an array of pyranometers. All five modeling approaches showed good agreement with the observed values [correlation coefficients (r) ranged from 0.95 to 0.97]. Model performance varied throughout the season due to their sensitivity to canopy growth. Although r values showed good agreement among all approaches, the C&N–E and DART models showed a better “goodness of fit” with lower root mean squared and bias values.



Funding was provided by USDA-ARS CRIS projects.

Supplementary material

271_2019_621_MOESM1_ESM.tif (369 kb)
Supplemental Fig. 1. Image of the placement of sensor placement for transmitted solar radiation measurements. (TIF 368 KB)


  1. Allen RG (2005) Environmental and water resources Institute (U.S) In: The ASCE standardized reference evapotranspiration equation, American Society of Civil Engineers, Reston, VaGoogle Scholar
  2. Anderson MC, Neale CMU, Li F, Norman JM, Kustas WP, Jayanthi H, Chavez J (2004) Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sens Environ 92:447–464CrossRefGoogle Scholar
  3. Anderson MC, Norman JM, Kustas WP, Li FQ, Prueger JH, Mecikalski JR (2005) Effects of vegetation clumping on two-source model estimates of surface energy fluxes from an agricultural landscape during SMACEX. J Hydrometeorol 6:892–909CrossRefGoogle Scholar
  4. Annandale JG, Jovanovic NZ, Campbell GS, Du Sautoy N, Lobit P (2004) Two-dimensional solar radiation interception model for hedgerow fruit trees. Agric For Meteorol 121:207–225CrossRefGoogle Scholar
  5. Blonquist JM, Norman JM, Bugbee B (2009) Automated measurement of canopy stomatal conductance based on infrared temperature. Agric For Meteorol 149:2183–2197CrossRefGoogle Scholar
  6. Campbell GS (1986) Extinction coefficients for radiation in plant canopies calculated using an ellipsoidal inclination angle distribution. Agric For Meteorol 36(4):317–321CrossRefGoogle Scholar
  7. Campbell G (1990) Derivation of an angle density function for canopies with ellipsoidal leaf angle distributions. Agric For Meteorol 49(3):173–176CrossRefGoogle Scholar
  8. Campbell GS, Norman JM (1998) An introduction to environmental biophysics, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  9. Charles-Edwards DA, Thornley JHM (1973) Light interception by an isolated plat: a simple model. Ann Bot 37:919–928CrossRefGoogle Scholar
  10. Chen JM, Liu J, Leblan SG, Lacaze R, Roujean JL (2003) Multi-angular optical remote sensing for assessing vegetation structure and carbon absorption. Remote Sens Environ 84:516–525CrossRefGoogle Scholar
  11. Colaizzi P, Evett S, Howell T, Li F, Kustas W, Anderson M (2012) Radiation model for row crops: I. Geometric view factors and parameter optimization. Agron J 104(2):225–240CrossRefGoogle Scholar
  12. Dai Q, Sun S (2007) A comparison of two canopy radiative models in land surface processes. Adv Atmos Sci 24(3):421–434CrossRefGoogle Scholar
  13. French AN, Hunsaker DJ, Clarke TR, Fitzgerald GJ, Luckett WE, Pinter PJ Jr (2007) Energy balance estimation of evapotranspiration for wheat grown under variable management practices in Central Arizona. Trans ASABE 50(6):2059–2071CrossRefGoogle Scholar
  14. Gascon F, Gastellu-Etchegorry JP, Lefevre-Fonollosa MJ, Dufrene E (2004) Retrieval of forest biophysical variables by inverting a 3-D radiative transfer model and using high and very high resolution imagery. Int J Remote Sens 25:5601–5616CrossRefGoogle Scholar
  15. Gastellu-Etchegorry JP (2008) 3D modeling of satellite spectral images—radiation budget and energy budget of urban landscapes. Meteorol Atmos Phys 102(3–4):187–207CrossRefGoogle Scholar
  16. Gastellu-Etchegorry JP, Demarez V, Pinel V, Zagolski F (1996) Modeling radiative transfer in heterogeneous 3D vegetation canopies. Remote Sens Environ 58(2):131–156CrossRefGoogle Scholar
  17. Gastellu-Etchegorry JP, Guillevic P, Demarez V, Zagolski F, Trichon V, Deering D, Leroy M (1999) Modeling BRF and radiative regime of tropical and boreal forests—Part I: BRF. Remote Sens Environ 68:281–316CrossRefGoogle Scholar
  18. Gastellu-Etchegorry JP, Yin T, Lauret N, Cajgfinger T, Gregoire T, Grau E, Feret JB, Lopes M, Guilleux J, Dedieu G, Malenovský Z, Cook BD, Morton D, Rubio J, Durrieu S, Cazanave G, Martin E, Ristorcelli T (2015) Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes. Remote Sens 7:1667–1701CrossRefGoogle Scholar
  19. Gastellu-Etchegorry JP et al (2017) 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. CrossRefGoogle Scholar
  20. Goudriaan J (1988) The bare bones of leaf-angle distribution in radiation models for canopy photosynthesis and energy exchange. Agric For Meteorol 43:155–169CrossRefGoogle Scholar
  21. Guillevic P, Gastellu-Etchegorry JP (1999) Modeling BRF and radiative regime of tropical and boreal forests—part II: PAR regime. Remote Sens Environ 68:317–340CrossRefGoogle Scholar
  22. Howell TA, Steiner JL, Evett SR, Schneider AD, Copeland KS, Dusek DA, Tunick A (1993) Radiation balance and soil water evaporation of bare Pullman clay loam soil. In: Allen RG, Neale CMU (eds) Management of irrigation and drainage systems: integrated perspectives. American Society of Civil Engineering, New York, pp 922–929 InGoogle Scholar
  23. Jacquemoud S, Bacour C, Poilvé H, Frangi JP (2000) Comparison of four radiative transfer models to simulate plant canopies reflectance: direct and inverse mode. Remote Sens Environ 74:471–481CrossRefGoogle Scholar
  24. Kustas WP, Norman JM (1999) Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric For Meteorol 94(1):13–29CrossRefGoogle Scholar
  25. Kustas WP, Agam N, Alfieri JG, McKee LG, Prueger JH, Hipps L, Howard AM, Heitman J (2018) Below canopy radiation divergence in a vineyard: implications on interrow surface energy balance. Irrig Sci. Google Scholar
  26. Kuusk A (1985) The hot spot effect of a uniform vegetative cover. Sov J Remote Sens 3(4):645–658Google Scholar
  27. Legates DR, McCabe GJ Jr (1999) Evaluating the use of “goodness-of fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241CrossRefGoogle Scholar
  28. Levashova NT, Mukhartova YV (2018) A three-dimensional model of solar radiation transfer in a non-uniform plant canopy. IOP Conf Ser Earth Environ Sci 107:012101CrossRefGoogle Scholar
  29. Li F, Kustas WP, Prueger JH, Neale CMU, Jackson TJ (2005) Utility of remote sensing-based two-source energy balance model under low and high-vegetation cover conditions. J Hydrometeorol 6:878–891CrossRefGoogle Scholar
  30. Malenovský Z, Homolová L, Zurita-Milla R, Lukeš P, Kaplan V, Hanuš J, Gastellu-Etchegorry JP, Schaepman ME (2013) Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer. Remote Sens Environ 131:85–102CrossRefGoogle Scholar
  31. McCree KJ (1972) Test of current definitions of photosynthetically active radiation against leaf photosynthesis data. Agric Meteorol 10:443–453CrossRefGoogle Scholar
  32. Meek DW, Hatfield JL, Howell TA, Idso SB, Reginato RJ (1984) A generalized relationship between photosynthetically active radiation and solar radiation. Agron J 76:939–945CrossRefGoogle Scholar
  33. Nouvellon Y, Bégué A, Moran MS, Lo Seen D, Rambal S, Luquet D, Chehbouni G, Inoue Y (2000) PAR extinction in shortgrass ecosystems: effects of clumping, sky conditions and soil albedo. Agric For Meteorol 105(1–3):21–41CrossRefGoogle Scholar
  34. Pieri P (2010a) Modelling radiative balance in a row-crop canopy: row–soil surface net radiation partition. Ecol Model 221:791–801CrossRefGoogle Scholar
  35. Pieri P (2010b) Modelling radiative balance in a row-crop canopy: cross-row distribution of net radiation at the soil surface and energy available to clusters in a vineyard. Ecol Model 221:802–811CrossRefGoogle Scholar
  36. Pinty B, Widlowski JL, Taberner M, Gobron N, Verstraete M, Disney M, Gascon F, Gastellu JP, Jiang L, Kuusk A (2004) Radiation transfer model intercomparison (RAMI) exercise: results from the second phase. J Geophys Res Atmos. Google Scholar
  37. Semmens KA, Anderson MC, Kustas WP, Gao F, Alfieri JG, McKee L, Prueger JH, Hain CR, Cammalleri C, Yang Y, Xia T, Sanchez L, Alsina MM, Vélez M (2016) Monitoring daily evapotranspiration over two California vineyards using Landsat 8 in a multi-sensor data fusion approach. Remote Sens Environ 185:155–170CrossRefGoogle Scholar
  38. Tunick A, Rachele H, Hansen FV, Howell TA, Steiner JL, Schneider AD, Evett SR (1994) REBAL’92: a cooperative radiation and energy balance field study for imagery and electromagnetic propagation. Bull Am Meteorol Soc 75:421–430CrossRefGoogle Scholar
  39. Verhoef W, Jia L, Xiao Q, Su Z (2007) Unified optical-thermal four-stream radiative transfer theory for homogeneous vegetation canopies. IEEE Trans Geosci Remote Sens 45(6):1808–1822CrossRefGoogle Scholar
  40. Weiss A, Norman JM (1985) Partitioning solar radiation into direct and diffuse, visible, and near-infrared components. Agric For Meteorol 34:205–213CrossRefGoogle Scholar
  41. Widlowski JL, Pinty B, Lopatka M, Atzberger C, Buzica D, Chelle M, Disney M, Gastellu-Etchegorry JP, Gerboles M, Gobron N (2013) The fourth radiation transfer model intercomparison (RAMI-IV): proficiency testing of canopy reflectance models with ISO-13528. J Geophys Res Atmos 118:6869–6890CrossRefGoogle Scholar

Copyright information

©  This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. 2019

Authors and Affiliations

  1. 1.USDA-ARS Crops Pathology and Genetics Research UnitDavisUSA
  2. 2.Efficient Use of Water in Agriculture ProgramInstitut de Recerca i Tecnologia Agroalimentàries (IRTA)LleidaSpain
  3. 3.Department of Geographical SciencesUniversity of Maryland, Terrestrial Information Systems Laboratory, NASA Goddard Space Flight CenterGreenbeltUSA
  4. 4.Blaustein Institutes for Desert ResearchBen-Gurion University of the NegevBeershebaIsrael
  5. 5.USDA ARS, Hydrology and Remote Sensing LaboratoryBeltsvilleUSA
  6. 6.Department of Viticulture and EnologyUniversity of CaliforniaDavisUSA

Personalised recommendations