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4D modeling of canopy architecture for improved characterization of state and functionning

4D modeling of canopy architecture for improved characterization of state and functionning. F. Baret INRA-CSE Avignon. Introduction. The description of vegetation architecture is one of the main limiting factor in the estimation of canopy characteristics such as LAI

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4D modeling of canopy architecture for improved characterization of state and functionning

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  1. 4D modeling of canopy architecture for improved characterization of state and functionning F. Baret INRA-CSE Avignon

  2. Introduction • The description of vegetation architecture is one of the main limiting factor in the estimation of canopy characteristics such as LAI • Importance of the temporal dimension that drives the generation of canopy architecture and that offers regularities to be exploited Turbid medium Geometric Explicit

  3. Requirements • Good dynamic description of canopy architecture • Low amount of parameters/variables (for better retrieval) • Fast computation of the radiative transfer Objectives of the study • Illustrate how canopy structure evolution could be generated • Present the corresponding variables and parameters used • Describe how to compute the radiative transfer • Conclude on the work to achieve

  4. The context of high spatial and temporal resolution observations • High spatial resolution: • Generally ‘pure’ pixels • object observed could be identified in terms of species • High temporal resolution • Continuous monitoring to be exploited in the understanding of how the architecture builds up (or destroys down!) Case illustrated here: maize canopies with relatively simple and well known architecture

  5. Modeling maize canopies architecture Work derived from previous studies : M. Espana, B. Andrieu, M. Chelle, B. Koetz, N. Rochdi • Describing the time course of individual leaves and stems • Based on a series of experiments • Semi-mechanistic models • Reduced number of variables • Reasonable level of details in canopy architecture description

  6. Level of canopy architecture details required for reflectance simulation T0 T1 T2

  7. Leaf area time course • Time of leaf of order n: • Apparition : n*DTc • Disparition : n*DTc+DTs • Variables required: • N_max • S_max • To • DTc • DTs

  8. Other architecture characteristics Canopy • Plant density • Distance between rows • Row azimuth Plant • Leaf insertion height • Leaf shape/curvature • Leaf azimuth • Leaf zenith Leaf insertion height Leaf order

  9. Properties of the 4D maize model • Limited number of variables/parameters: • N_max • S_max • To • DTc • DTs • H_max • Density • Leaf inclination • Dynamics well described • Improvements • Leaf curvature (easy) • Better senescence including keeping senescent leaves • Variability between plants (size, position, …) • Flowers/ears • Vertical gradients in chlorophyll

  10. Regularities in Chlorophyll gradients 1999 2001 Distribution verticale du contenu en chlorophylle mesurée à partir de l’instrument SPAD502

  11. From canopy architecture … to reflectance Parcinopy Multispectral version now available (M. Chelle, V. Rancier)

  12. Decomposing radiative transfer 4 fluxesapproximation Rc=rso+tsstoo.Rs +((tss.Rs+tsd.Rs).tdo+(tsd+tss.Rs.rdd).Rs.too)/(1-Rs.*rdd) Black soil term Soil interaction term Terms required n(level,way,direction,inter_sol,inter_veget)=number of photons (radiance) level: b=bottom; t=top way: - = downward; + = upward direction: qs=sun direction; qv=view direction;h=hemispheric interaction order (inter_sol, inter_veget): 0: no interaction 1: 1 interaction only 1: one or more interactions tss=a tsstoo.rso =f rso=d+e rdd=c/(a.Rs+b.Rs) tsd=b tdo=g /(a.Rs+b.Rs)

  13. Vegetation contribution (rso) rso = f(rleaf,tleaf,P(LAI,ALA,S,D,,sv)) Parameters 'P' are spectral invariants

  14. Approach Building a parametric model [LAI,ALA,S,D,] Distribution of input variables Constructionof the 3D architecture [sv]Sun/view configuration PARCINOPY [rl, tl] leaf reflectance & Transmittance Canopyreflectance [rs] Soil reflectance RT components Parametric model P(LAI,ALA,S,D,,sv, rl, tl)

  15. CONCLUSION • A more mechanistic/realistic approach is proposed • Based on a ‘simple’ description of canopy architecture to use fewer variables • No need for continuous description (discrete is enough) • Needs sensitivity analysis to evaluate the influence of the variation of N_max, H_max, … • Needs full (or at least parametric for the spectral aspect) parametric model to be implemented to compute the reflectance fields • Needs coupling to canopy functioning models

  16. Coupling between structure and function models Reflectance LAI 4D Architecture Model Initialization Stress (H2O, N) S T Work in progress for exploitation within an assimilation scheme, …

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