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Modeling the Radiance Field in 3D Crop Canopies for Efficient Light Transfer

This study explores the complex interactions of light within 3D crop canopies using radiosity and Monte Carlo ray tracing methods. By focusing on a significant canopy pattern, variables like canopy BRDF, gap fraction, and radiance distribution are estimated. The research delves into multiple scattering effects and the use of stochastically propagated light rays to simulate light-canopy interactions. Various polygons with different leaf BRDF characteristics are analyzed to provide insights on canopy reflectance and mean flux profiles. The study also scrutinizes the efficiency and accuracy of the modeling approaches, considering factors like plant movement, optical properties, and structure accuracy of 3D canopies.

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Modeling the Radiance Field in 3D Crop Canopies for Efficient Light Transfer

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  1. Modeling the radiance field within 3D crop canopies Michaël Chelle, Bruno Andrieu UMR Environnement et Grandes Cultures INRA Thiverval-Grignon - France

  2. Maize leaf BRDF Sanz et al, 1997 Modeling 3D light transfer Light-leaf interaction incident reflection absorption transmission

  3. Modeling 3D light transfer scattering interception The radiance equation L(y,yx) Light-leaves interactions Complexity of solving this equation depends on the number of surfacesSy => Not working on a whole canopy, but on a significant pattern ∞ duplicated

  4. First order of scattering Projection (Z-buffer) Efficient treatment of periodic infinite canopy Canopy gap fraction => single Z-buffer : Monogap Canopy BRDF => double Z-buffer : Bvis(B. Andrieu, 1999)

  5. First order of scattering Example of application Estimation of the clumping parameter

  6. Multiple scattering Monte Carlo ray tracing Ross & Marshak (1988); ART (Dauzat, 1991) Raytran (Govaerts, 1994), North(1996), BPMS (Lewis, 1999),… Following stochastically the propagation of light rays within a 3D canopy Our Monte Carlo ray tracing : PARCINOPY • Polygons set, various leaf BRDF • Multispectral: work in progress * Classic CG algorithms * Numerous output variables (not only canopy reflectance) + Canopy BRDF, gap fraction,… + Profile of mean fluxes, radiance distrib° + virtual sensors + polygons irradiance each variable may be given by scattering order * Estimation of the variance of each output Few assumptions, but Computing-time consuming

  7. ? an erectophile canopy lit with a zenith source NIR TM, LAI 4, 60°, NIR LAI 0.5, LAI 2 LAI 3.7 Multiple scattering Illustrations of parcinopy uses • Generation of reference dataset: nested radiosity, Kuusk (97),Shabanov (2000) • Analysis of sensitivity : leaf BRDF, Plant geometry (Espana et al) • Study of radiative transfer: what about fluxes isotropy? scattering order?

  8. Thus, the radiance equation is simplified: i fr(x) H Lambertian L(y,r) Bi (radiosity) Multiple scattering A more efficient method : radiosity Borel (1991); Goel (1991), Garcia-Haro (2002), A radiosity model consists in: • computing the N2 form factors between each leaf • solving the resulting system of linear equations • Two limitations of the radiosity method: • the N2 complexity • the Lambertian approximation

  9. Multiple scattering A dedicated radiosity method for canopy the nested radiosity(Chelle et Andrieu, 1998)  For each triangle, a sphere defines the close objects  The far radiations are estimated by a TM model: SAIL Designed to estimate leaf irradiances, a Z-buffer projection may be used to estimate canopy BRDF from these…

  10. Modeling 3D light transfer Several questions remains: • What about the 3D structure accuracy? • Quid about moving plants ? • How detailed should be the optical properties ? • Are these approaches also suitable for forest canopy? • What about needles? • Experimental dataset ? • Should the 3D approaches be restricted to the theoretical studies • to improve efficient TM models (hot spot, clumping,…) • or be used to design operational methods?

  11. Conclusion Combining accurate 3D canopies and 3D RT tools • Provide tools to investigate light-canopy interactions and the properties of resulting fluxes • Provide reference dataset Basis to develop efficient, but correct RT models to analyze remote sensing data

  12. 0 <1 0 ~ 1

  13. Sensivity to the sphere diameter : the case of maize

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