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Adaptation of the leaf optical property model PROSPECT to thermal infrared

Adaptation of the leaf optical property model PROSPECT to thermal infrared. A.Olioso, S. Jacquemoud* & F. Baret. UMR Climat, Sol et Environnement INRA Avignon, France

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Adaptation of the leaf optical property model PROSPECT to thermal infrared

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  1. Adaptation of the leaf optical property model PROSPECT to thermal infrared A.Olioso, S. Jacquemoud* & F. Baret UMR Climat, Sol et EnvironnementINRA Avignon, France * Institut de Physique du Globe de Paris (IPGP)Département de Géophysique Spatiale et PlanétaireUniversité Paris 7 - Denis Diderot

  2. Radiative properties of leaves in the thermal infrared are required for implementing radiative transfer models ex: => remote sensing studies => fire propagation studiesModel of leaf properties are required for=> analysing variations of leaf properties (ex. with leaf moisture) => linking leaf properties to plants processesThere is no such model !=> building a model on the basis of the PROSPECT model (Jacquemoud and Baret 1990) which is working in the solar domain

  3. Leaf optical properties depend on anatomical leaf structure and biochemical leaf composition reflected +emitted absorbed transmitted+ emitted

  4. Description of the PROSPECT model reflectance () Is Surface effects Elementary layer: n: refraction index K: global absorption coefficient Nidentical layers Hemispheric fluxes () transmittance Global absorption: Specificabsorptioncoefficients Content inabsorbingmaterial

  5. 1 1 n1 n2 2 SCATTERING Refractive index: n() Snell’s law

  6. ABSORPTION Specific absorption coefficient of constituent i: ki() d Beer law

  7. PROSPECT N Cab Cbp Cw Cdm leaf structure parameterchlorophyll a+b concentration (g.cm2)brown pigment concentration (g.cm2)equivalent water thickness (cm)dry matter content (g.cm2) N = 1.5, Cab = 50 g.cm2, Cdm = 0.005 g.cm2 PROSPECT () ()

  8. N - Number of layersCab - Chlorophyll a+b contentCbp - Brown pigment contentCw - Equivalent water thicknessCdm - Dry matter content n(λ) - Refractive index ki(λ) - Specific absorptioncoefficients of constituants () – leaf reflectance () – leaf transmittance PROSPECT INPUTS PARAMETERS between 0.4 and 2.5 µm PROSPECT OUTPUTS

  9. N - Number of layersCab - Chlorophyll a+b contentCbp - Brown pigment contentCw - Equivalent water thicknessCdm - Dry matter content n(λ) - Refractive index ki(λ) - Specific absorption coefficients of constituants () – leaf reflectance () – leaf transmittance PROSPECT INPUTS PARAMETERS between 0.4 and 2.5 µm between 2.5 and 18 µm kw(λ)kdm(λ) PROSPECT OUTPUTS ε () – leaf emissivity

  10. PROSPECT INPUTS refractive index n(λ) ?

  11. 0.4-2.5 µm PROSPECT INPUTS specific absorption coefficient of water kw(λ)

  12. PROSPECT INPUTS * specific absorption coefficient of dry matter: kdm(λ) -> no info available at the moment -> to be obtained by inverting PROSPECT against leaf spectrum data (in particular from dry leaf) * idem for leaf layer refractive index n(λ)(inversion from fresh leaf spectra) * N, Cw, Cdmmay be obtained from library, measurements or from PROSPECT inversion between 0.4 and 1.8 µm

  13. kdm(λ), n(λ) N, Cw, Cdm Solar domainThermal infrared DETERMINATION OF PROSPECT INPUTS: the only easily available data that made it possible to determine PROSPECT inputs were found in the ASTER spectral library

  14. some cellulose and lignin features but not always specific 0.4-2.5 µm • Specific absorption coefficient of dry matter: kdm(λ) • inversion of PROSPECT against ‘ASTER’ dry spectra • result of inversion compared to cellulose and lignin spectra Lignin

  15. Opposite behavior of H2O and dry matter Difficult zone becauseof high absorption of bothdry matter and H2O Low absorption zone • Specific absorption coefficient of dry matter: kdm(λ) • comparison to water

  16. Lowest absorptionzone Determination of the refractive index : n(λ) inversion of wet spectra gave refrative index

  17. COMPARISON OF PROSPECT OUTPUTS / MEASUREMENTS Data from -ASTER spectral library -Salisbury and D’Aria 1992 -MODIS spectral library

  18. Comparison of simulated reflectance to data from Salisbury and D’Aria 1992 senescent beech leaf

  19. Comparison of simulated reflectance to data from the MODIS spectra library 3 dry grass spectra

  20. Comparison of simulated reflectance to data from the MODIS spectra library various fresh leaves

  21. Comparaison de simulations à des mesures

  22. High transmittance Sensitivity to leaf water content sensitivity to Cw from 0.0002 cm-1 to 0.0512 cm-1(0.0002, 0.0008, 0.0032, 0.0128, 0.0512 cm-1) 0.0002 0.0512

  23. Sensitivity to leaf water content sensitivity to Cw from 0.0002 cm-1 to 0.0512 cm-1(0.0002, 0.0008, 0.0032, 0.0128, 0.0512 cm-1)

  24. Emissivity lower than expected fromreflectance Sensitivity to leaf water content sensitivity to Cw from 0.0002 cm-1 to 0.0512 cm-1(0.0002, 0.0008, 0.0032, 0.0128, 0.0512 cm-1) 0.0512 0.0002

  25. average behaviour in situ ? • Sensitivity of 8-14 µm emissivity to leaf moisture • fresh leaves and dry leaves don’t have the same internal structure (parameter N = 2 and 4) • different responses

  26. sensitivity to incident angle from 10 to 90° by step of 10° 10° 90° • Sensitivity to leaf surface properties • various components (silica, waxes…) and / or structure (hair, epidermis cell shape…) may affect leaf surface – radiation interactions • introduction of new components • use the radiation incident angle of the plate model (set to 59° usualy)

  27. Conclusion • Encouraging first results • There is a lot of work still to do • acquisition of leaf data for calibrating and testing the model • analysis of the effects of the various components in order to discriminate generic effects and specific effects • investigation of leaf surface effects • investigation of leaf drying impact… • …. • implementation in canopy radiative transfer model for the analysis of land surface emissivity spectra acquired from TIR multispectral sensors

  28. The end S. Knap & N. Knight, 2001, Flora, Harry N Abrams, 80 pages.

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